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by Alexander Gietelink Oldenziel
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[-]Alexander Gietelink Oldenziel566

The Padding Argument or Simplicity = Degeneracy

[I learned this argument from Lucius Bushnaq and Matthias Dellago. It is also latent already in Solomonoff's original work]

Consider binary strings of a fixed length  

Imagine feeding these strings into some turing machine; we think of strings as codes for a function. Suppose we have a function that can be coded by a short compressed string  of length . That is, the function is computable by a small program. 

Imagine uniformly sampling a random code for  . What number of the codes implement the same function as the string ? It's close to . Indeed, given the string  of length   we can 'pad' it to a string of length  by writing the code

"run  skip  "

where  is an arbitrary string of length  where  is a small constant accounting for the overhead. There are approximately  of such binary strings. If our programming language has a simple skip / commenting out functionality then we expect approximately  codes encoding the same function as . The fr... (read more)

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2Lucius Bushnaq
The sentence seems cut off.
[-]Daniel Murfet170

Re: the SLT dogma.

For those interested, a continuous version of the padding argument is used in Theorem 4.1 of Clift-Murfet-Wallbridge to show that the learning coefficient is a lower bound on the Kolmogorov complexity (in a sense) in the setting of noisy Turing machines. Just take the synthesis problem to be given by a TM's input-output map in that theorem. The result is treated in a more detailed way in Waring's thesis (Proposition 4.19). Noisy TMs are of course not neural networks, but they are a place where the link between the learning coefficient in SLT and algorithmic information theory has already been made precise.

For what it's worth, as explained in simple versus short, I don't actually think the local learning coefficient is algorithmic complexity (in the sense of program length) in neural networks, only that it is a lower bound. So I don't really see the LLC as a useful "approximation" of the algorithmic complexity.

For those wanting to read more about the padding argument in the classical setting, Hutter-Catt-Quarel "An Introduction to Universal Artificial Intelligence" has a nice detailed treatment.

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5Alexander Gietelink Oldenziel
Thank you for the references Dan. I agree neural networks probably don't actually satisfy the padding argument on the nose and agree that the exact degeneracy is quite interesting (as I say at the end of the op). I do think for large enough overparameterization the padding argument suggests the LLC might come close to the K-complexity in many cases. But more interestingly to me is that the padding argument doesn't really require the programming language to be Turing-complete. In those cases the degeneracy will be proportional to complexity/simplicity measures that are specific to the programming language (/architecture class). Inshallah I will get to writing something about that soon.
[-]Alexander Gietelink Oldenziel140

Neural Network have a bias towards Highly Decomposable Functions. 

tl;dr Neural networks favor functions that can be "decomposed" into a composition of simple pieces in many ways - "highly decomposable functions". 

Degeneracy = bias under uniform prior

[see here for why I think bias under the uniform prior is important]

Consider a space  of parameters used to implement functions, where each element  specifies a function via some map . Here, the set  is our parameter space, and we can think of each as representing a specific configuration of the neural network that yields a particular function . 

The mapping  assigns each point  to a function . Due to redundancies and symmetries in parameter space, multiple configurations  might yield the same function, forming what we call a fiber, or the "set of degenerates." of  

 This fiber is the set of ways in which the same functional behavior can be achieved by different parameterizations. If we uniformly sample from codes, the degeneracy of a function  counts how likely it is to be sampl... (read more)

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[-]Alexander Gietelink Oldenziel40

Looking for specific tips and tricks to break AI out of formal/corporate writing patterns. Tried style mimicry ('write like Hemingway') and direct requests ('be more creative') - both fell flat. What works?

Should I be using different AI models ( I am using GPT and Claude)? The base models output an enormous creative storm, but somehow the RLHF has partially lobotomized LLMs such that they always seem to output either cheesy stereotypes or overly verbose academise/corporatespeak. 

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4dirk
Edit: ChatGPT and Claude are both fine IMO. Claude has a better ear for language, but ChatGPT's memory is very useful for letting you save info about your preferences, so I'd say they come out about even. For ChatGPT in particular, you'll want to put whatever prompt you ultimately come up with into your custom instructions or its memory; that way all new conversations will start off pre-prompted. In addition to borrowing others' prompts as Nathan suggested, try being more specific about what you want (e.g., 'be concise, speak casually and use lowercase, be sarcastic if i ask for something you can't help with'), and (depending on the style) providing examples (ETA: e.g., for poetry I'll often provide whichever llm with a dozen of my own poems in order to get something like my style back out). (Also, for style prompting, IME 'write in a pastiche of [author]' seems more powerful than just 'write like [author]', though YMMV).
3Garrett Baker
I have found that they mirror you. If you talk to them like a real person, they will act like a real person. Call them (at least Claude) out on their corporate-speak and cheesy stereotypes in the same way you would a person scared to say what they really think.
2Nathan Helm-Burger
The two suggestions that come to mind after brief thought are: 1. Search the internet for prompts others have found to work for this. I expect a fairly lengthy and complicated prompt would do better than a short straightforward one. 2. Use a base model as a source of creativity, then run that output through a chat model to clean it up (grammar, logical consistency, etc)
[-]Alexander Gietelink Oldenziel40

Is true Novelty a Mirage?

One view on novelty is that it's a mirage. Novelty is 'just synthesis of existing work, plus some randomness.'

I don't think that's correct. I think true novelty is more subtle than that. Yes sometimes novel artforms or scientific ideas are about noisily mixing existing ideas. Does it describe all forms of novelty?

A reductio ad absurdum of the novelty-as-mirage point of view is that all artforms that have appeared since the dawn of time are simply noised versions of cavepaintings. This seems absurd.

Consider AlphaGO. Does AlphaGO jus... (read more)

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2Vladimir_Nesov
Creativity is RL, converting work into closing the generation-discrimination gap wherever it's found (or laboriously created by developing good taste). The resulting generations can be novelty-worthy, imitating them makes it easier to close the gap, reducing the need for creativity.
[-]Alexander Gietelink Oldenziel7418

Misgivings about Category Theory

[No category theory is required to read and understand this screed]

A week does not go by without somebody asking me what the best way to learn category theory is. Despite it being set to mark its 80th annivesary, Category Theory has the evergreen reputation for being the Hot New Thing, a way to radically expand the braincase of the user through an injection of abstract mathematics. Its promise is alluring, intoxicating for any young person desperate to prove they are the smartest kid on the block.

Recently, there has been significant investment and attention focused on the intersection of category theory and AI, particularly in AI alignment research. Despite the influx of interest I am worried that it is not entirely understood just how big the theory-practice gap is.

 I am worried that overselling risks poisoning the well for the general concept of advanced mathematical approaches to science in general, and AI alignment in particular. As I believe mathematically grounded approaches to AI alignment are perhaps the only way to get robust worst-case safety guarantees for the superintelligent regime I think this would be bad. 

I find it difficult... (read more)

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2Quinn
my dude, top level post- this does not read like a shortform
3StartAtTheEnd
Great post! It's a habit of mine to think in very high levels of abstraction (I haven't looked much into category theory though, admittedly), and while it's fun, it's rarely very useful. I think it's because of a width-depth trade-off. Concrete real-world problems have a lot of information specific to that problem, you might even say that the unique information is the problem. An abstract idea which applies to all of mathematics is way too general to help much with a specific problem, it can just help a tiny bit with a million different problems. I also doubt the need for things which are so complicated that you need a team of people to make sense of them. I think it's likely a result of bad design. If a beginner programmer made a slot machine game, the code would likely be convoluted and unintuitive, but you could probably design the program in a way that all of it fits in your working memory at once. Something like "A slot machine is a function from the cartesian product of wheels to a set of rewards". An understanding which would simply the problem so that you could write it much shorter and simpler than the beginner. What I mean is that there may exist simple designs for most problems in the world, with complicated designs being due to a lack of understanding. The real world values the practical way more than the theoretical, and the practical is often quite sloppy and imperfect, and made to fit with other sloppy and imperfect things. The best things in society are obscure by statistical necessity, and it's painful to see people at the tail ends doubt themselves at the inevitable lack of recognition and reward.
2lemonhope
As a layman, I have not seen much unrealistic hype. I think the hype-level is just about right.
6lemonhope
You should not bury such a good post in a shortform
[-]Daniel Murfet115

Modern mathematics is less about solving problems within established frameworks and more about designing entirely new games with their own rules. While school mathematics teaches us to be skilled players of pre-existing mathematical games, research mathematics requires us to be game designers, crafting rule systems that lead to interesting and profound consequences

 

I don't think so. This probably describes the kind of mathematics you aspire to do, but still the bulk of modern research in mathematics is in fact about solving problems within established frameworks and usually such research doesn't require us to "be game designers". Some of us are of course drawn to the kinds of frontiers where such work is necessary, and that's great, but I think this description undervalues the within-paradigm work that is the bulk of what is going on.

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2Alexander Gietelink Oldenziel
Yes thats worded too strongly and a result of me putting in some key phrases into Claude and not proofreading. :p I agree with you that most modern math is within-paradigm work.
2Maelstrom
One needs only to read 4 or so papers on category theory applied to AI to understand the problem. None of them share a common foundation on what type of constructions to use or formalize in category theory. The core issue is that category theory is a general language for all of mathematics, and as commonly used just exponentially increase the search space for useful mathematical ideas. I want to be wrong about this, but I have yet to find category theory uniquely useful outside of some subdomains of pure math.
3cubefox
In the past we already had examples ("logical AI", "Bayesian AI") where galaxy-brained mathematical approaches lost out against less theory-based software engineering.
8Alexander Gietelink Oldenziel
I shall now confess to a great caveat. When at last the Hour is there the Program of the World is revealed to the Descendants of Man they will gaze upon the Lines Laid Bare and Rejoice; for the Code Kernel of God is written in category theory.
2Daniel Murfet
Typo, I think you meant singularity theory :p
[-]Alexander Gietelink Oldenziel50

Are Solomonoff Daemons exponentially dense? 

Some doomers have very strong intuitions that doom is almost assured for almost any kind of building AI. Yudkowsky likes to say that alignment is about hitting a tiny part of values space in a vast universe of deeply alien values. 

Is there a way to make this more formal? Is there a formal model in which some kind of solomonoff daemon/ mesa-optimizer/ gremlins in the machine start popping up all over the place as the cognitive power of the agent is scaled up?

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[-]Viliam106

Imagine that a magically powerful AI decides to set a new political system for humans and create a "Constitution of Earth" that will be perfectly enforced by local smaller AIs, while the greatest one travels away to explore other galaxies.

The AI decides that the most fair way to create the constitution is randomly. It will choose a length, for example 10000 words of English text. Then it will generate all possible combinations of 10000 English words. (It is magical, so let's not worry about how much compute that would actually take.) Out of the generated combinations, it will remove the ones that don't make any sense (an overwhelming majority of them) and the ones that could not be meaningfully interpreted as "a constitution" of a country (this is kinda subjective, but the AI does not mind reading them all, evaluating each of them patiently using the same criteria, and accepting only the ones that pass a certain threshold). Out of the remaining ones, the AI will choose the "Constitution of Earth" randomly, using a fair quantum randomness generator.

Shortly before the result is announced, how optimistic would you feel about your future life, as a citizen of Earth?

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9sunwillrise
As an aside (that's still rather relevant, IMO), it is a huge pet peeve of mine when people use the word "randomly" in technical or semi-technical contexts (like this one) to mean "uniformly at random" instead of just "according to some probability distribution." I think the former elevates and reifies a way-too-common confusion and draws attention away from the important upstream generator of disagreements, namely how exactly the constitution is sampled. I wouldn't normally have said this, but given your obvious interest in math, it's worth pointing out that the answers to these questions you have raised naturally depend very heavily on what distribution we would be drawing from. If we are talking about, again, a uniform distribution from "the design space of minds-in-general" (so we are just summoning a "random" demon or shoggoth), then we might expect one answer. If, however, the search is inherently biased towards a particular submanifold of that space, because of the very nature of how these AIs are trained/fine-tuned/analyzed/etc., then you could expect a different answer.
2Viliam
Fair point. (I am not convinced by the argument that if the AI's are trained on human texts and feedback, they are likely to end up with values similar to humans, but that would be a long debate.)
2Gunnar_Zarncke
This sounds related to my complaint about the YUDKOWSKY + WOLFRAM ON AI RISK debate: I got this tweet wrong. I meant if pockets of irreducibility are common and non-pockets are rare and hard to find, then the risk from superhuman AI might be lower. I think Stephen Wolfram's intuition has merit but needs more analysis to be convicing.  
4MondSemmel
Most configurations of matter, most courses of action, and most mind designs, are not conducive to flourishing intelligent life. Just like most parts of the universe don't contain flourishing intelligent life. I'm sure this stuff has been formally stated somewhere, but the underlying intuition seems pretty clear, doesn't it?
[-]Alexander Gietelink Oldenziel60

[this is a draft. I strongly welcome comments]

The Latent Military Realities of the Coming Taiwan Crisis

A blockade of Taiwan seems significantly more likely than a full-scale invasion. The US's non-intervention in Ukraine suggests similar restraint might occur with Taiwan. 

Nevertheless, Metaculus predicts a 65% chance of US military response to a Chinese invasion and separately gives 20-50% for some kind of Chinese military intervention by 2035. Let us imagine that the worst comes to pass and China and the United States are engaged in a hot war?

China's... (read more)

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3Ebenezer Dukakis
That sounds like an exaggeration? My impression is that China has OK/good relations with countries such as Vietnam, Cambodia, Pakistan, Indonesia, North Korea, factions in Myanmar. And Russia, of course. If you're serious about this claim, I think you should look at a map, make a list of countries which qualify as "neighbors" based purely on geographic distance, then look up relations for each one.
2Garrett Baker
I note you didn't mention the info-sec aspects of the war, I have heard China is better at this than the US, but that doesn't mean much because you would expect to hear that if China was really terrible too.
[-]Alexander Gietelink Oldenziel4611

How to prepare for the coming Taiwan Crisis? Should one short TSMC? Dig a nuclear cellar?

Metaculus gives a 25% of a fullscale invasion of Taiwan within 10 years and a 50% chance of a blockade. It gives a 65% chance that if China invades Taiwan before 2035 the US will respond with military force. 

Metaculus has very strong calibration scores (apparently better than prediction markets). I am inclined to take these numbers as the best guess we currently have of the situation. 

Is there any way to act on this information?

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1Milan W
The CSIS wargamed a 2026 Chinese invasion of Taiwan, and found outcomes ranging from mixed to unfavorable for China (CSIS report). If you trust both them and Metaculus, then you ought to update downwards on your estimate of the PRC's strategic ability. Personally, I think Metaculus overestimates the likelihood of an invasion, and is about right about blockades.
3ChristianKl
Why would you trust CSIS here? A US think tank like that is going to seek to publically say that invading Taiwan is bad for the Chinese.
1Milan W
Why would they? It's not like the Chinese are going to believe them. And if their target audience is US policymakers, then wouldn't their incentive rather be to play up the impact of marginal US defense investment in the area?
3Garrett Baker
I note that the PRC doesn't have a single "strategic ability" in terms of war. They can be better or worse at choosing which wars to fight, and this seems likely to have little influence on how good they are at winning such wars or scaling weaponry. Eg in the US often "which war" is much more political than "exactly what strategy should we use to win this war" is much more political than "how much fuel should our jets be able to carry", since more people can talk & speculate about the higher level questions. China's politics are much more closed than the US's, but you can bet similar dynamics are at play.
0Milan W
I should have been more clear. With "strategic ability", I was thinking about the kind of capabilities that let a government recognize which wars have good prospects, and to not initiate unfavorable wars despite ideological commitments.
3Milan W
Come to think of it, I don't think most compute-based AI timelines models (e.g. EPOCH's) incorporate geopolitical factors such as a possible Taiwan crisis. I'm not even sure whether they should. So keep this in mind while consuming timelines forecasts I guess?
[-]Alexander Gietelink Oldenziel82

Hot Take #44: Preaching to the choir is 'good' actually. 

  1. Almost anything that has a large counterfactual impact is achieved by people thinking and acting different from accepted ways of thinking and doing.
  2. With the exception of political entrepeneurs jumping into a power vacuum, or scientific achievements by exceptional individuals most counterfactual impactful is done made by movements of fanatics.
  3. The greatest danger to any movement is dissipation. Conversely, the greatest resource of any movement is the fanaticism of its members.
  4. Most persuasion is
... (read more)
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5StartAtTheEnd
This seems like an argument in favor of: Stability over potential improvement, tradition over change, mutation over identical offspring, settling in a local maximum over shaking things up, and specialization vs generalization. It seems like a hyperparameter. A bit like the learning rate in AI perhaps? Echo chambers are a common consequence, so I think the optimal ratio of preaching to the choir is something like 0.8-0.9 rather than 1. In fact, I personally prefer the /allPosts suburl over the LW frontpage because the first few votes result in a feedback loop of engagement and upvotes (forming a temporary consensus on which new posts are better, in a way which seems unfairly weighted towards the first few votes). If the posts chosen for the frontpage use the ratio of upvotes and downvotes rather than the absolute amount, then I don't thing this bias will occur (conformity might still create a weak feedback loop though). I'm simplifying some of these dynamics though.
[-]Alexander Gietelink Oldenziel61

Mindmeld

In theory AIs can transmit information far faster and more directly than humans. They can directly send weight/activation vectors to one another. The most important variable on whether entities (cells, organisms, polities, companies, ideologies, empire etc) stay individuals or amalgate into a superorganism is communication bandwith & copy fidelity. 
Both of these differ many order of magnitude for humans versus AIs. At some point, mere communication becomes a literal melding of minds. It seems quite plausibly then that AIs will tend to mind... (read more)

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7Carl Feynman
A fascinating recent paper on the topic of human bandwidth  is https://arxiv.org/abs/2408.10234.  Title and abstract: The Unbearable Slowness of Being Jieyu Zheng, Markus Meister
[-]Alexander Gietelink Oldenziel8-12

EDIT: I was wrong. Theo the French Whale was the sharp. From the Kelly formula and his own statements his all things considered probability was 80-90% - he would need to possess an enormous amount of private information to justify such a deviation from other observers. It turns out he did. He commissioned his own secret polls using a novel polling method to compensate for the shy Trump voter.

https://x.com/FellowHominid/status/1854303630549037180

The French rich idiot who bought 75 million dollar of Trump is an EA hero win or lose.

LW loves prediction markets... (read more)

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3Dana
I agree with you that people like him do a service to prediction markets: contributing a huge amount of either liquidity or information. I don't agree with you that it is clear which one he is providing, especially considering the outcome. He did also win his popular vote bet, which was hovering around, I'm not sure, ~20% most of the time?  I think he (Theo) probably did have a true probability around 80% as well. That's what it looks like at least. I'm not sure why you would assume he should be more conservative than Kelly. I'm sure Musk is not, as one example of a competent risk-taker.
2Alexander Gietelink Oldenziel
The true probability would be more like >90% considering other factors like opportunity costs, transactions cost, counterparty risk, unforeseen black swans of various kinds etc.  Bear in mind this is  all things considered probability not just in-model probability, i.e. this would have to integrate that most other observers (especially those with strong calibrated prediction ) very strongly disagree*. Certainly, in some cases this is possible but one would need quite overwhelming evidence that you had a huge edge.  I agree one can reject Kelly betting - that's pretty crazy risky but plausibly the case for people like Elon or Theo. The question is whether the rest of us (with presumably more reasonably cautious attitudes) should take his win as much epistemic evidence. I think not. From our perspective his manic riskloving wouldn't be an much evidence for rational expectations.  *didn't the Kelly formula already integrate the fact that other people think differently. No, this is an additional piece of information one has to integrate. The Kelly betting gives you an implicit risk-averseness even conditioning on your beliefs being true (on average).    EDIT: Indeed it seems Theo the French Whale might have done a Kelly bet estimate too, he reports his true probability at 80-90%. Perhaps he did have private information.  "For example, a hypothetical sale of Théo's 47 million shares for Trump to win the election would execute at an estimated average price of just $0.02, according to Polymarket, which would represent a 96% loss for the trader. Théo paid an average price of about $0.56 cents for the 47 million shares. Meanwhile, a hypothetical sale of Théo's nearly 20 million shares for Trump to win the popular vote would execute at an average price of less than a 10th of a penny, according to Polymarket, representing a near-total loss. With so much money on the line, Théo said he is feeling nervous, though he believes Trump has an 80%-90% chance to win the electio
[-]Steven Byrnes196

I disagree with “He seems to have no inside information.” He presented himself as having no inside information, but that’s presumably how he would have presented himself regardless of whether he had inside information or not. It’s not like he needed to convince others that he knows what he’s doing, like how in the stock market you want to buy then pump then sell. This is different—it’s a market that’s about to resolve. The smart play from his perspective would be to aggressively trash-talk his own competence, to lower the price in case he wants to buy more.

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4Alexander Gietelink Oldenziel
Yes, this is possible. It smells a bit of 4d-chess. As far as I can tell he already had finalized his position by the time the WSJ interview came out.  I've dug a little deeper and it seems he did do a bunch of research on polling data. I was a bit too rash to say he had no inside information whatsoever. Plausibly he had some. The degree of the inside information he would need is very high. It seems he did a similar Kelly bet calculation since he report his all-things-considered probability to be 80-90%: "With so much money on the line, Théo said he is feeling nervous, though he believes Trump has an 80%-90% chance to win the election. "A surprise can always occur," Théo told The Journal."  I have difficulty believing one can get this kind of certainty for all-things-considered-probability for something as noisy and tight as US presidential election. [but he won both the electoral college and popular vote bet]
2Viliam
To me it just seems like understanding the competitive nature of the prediction markets. In our bubble, prediction markets are celebrated as a way to find truth collectively, in a way that disincentivizes bullshit. And that's what they are... from outside. But it's not how it works from the perspective of the person who wants to make money on the market! You don't want to cooperate on finding the truth; you actually wish for everyone else to be as wrong as possible, because that's when you make most money. Finding the truth is what the mechanism does as a whole; it's not what the individual participants want to do. (Similarly how economical competition reduces the prices of goods, but each individual producer wishes they could sell things as expensively as possible.) Telling the truth means leaving money on the table. As a rational money-maximizer, you wish that other people believe that you are an idiot! That will encourage them to bet against you more, as opposed to updating towards your position; and that's how you make more money. This goes strongly against our social instincts. People want to be respected as smart. That's because in social situation, your status matters. But the prediction markets are the opposite of that: status doesn't matter at all, only being right matters. It makes sense to sacrifice your status in order to make more money. Would you rather be rich, or famous as a superforecaster? This could be a reason why money-based prediction markets will systematically differ from prestige-based prediction markets. In money-based markets, charisma is a dump stat. In prestige-based ones, that's kinda the entire point.
[-]interstice137

Looks likely that tonight is going to be a massive transfer of wealth from "sharps"(among other people) to him. Post hoc and all, but I think if somebody is raking in huge wins while making "stupid" decisions it's worth considering whether they're actually so stupid after all.

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[-]Alexander Gietelink Oldenziel100

>>  'a massive transfer of wealth from "sharps" '. 

no. That's exactly the point. 

1. there might no be any real sharps (=traders having access to real private arbitragiable information that are consistently taking risk-neutral bets on them) in this market at all.

This is because a) this might simple be a noisy, high entropy source that is inherently difficult to predict, hence there is little arbitragiable information and/or b) sharps have not been sufficiently incenticiz

2. The transfer of wealth is actually disappointing because Theo the French Whale moved the price so much. 

For an understanding of what the trading decisions of a verifiable sharp looks like one should take a look at Jim Simons' Medaillon fund. They do enormous hidden information collection, ?myssterious computer models, but at the end of the day take a large amount of very hedged tiny edge positions. 

***************************************************

You are misunderstanding my argument (and most of the LW commentariat with you). I might note that I made my statement before the election result and clearly said 'win or lose' but it seems that even on LW people think winning on a noisy N=1 sample is proof of rationality. 

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4interstice
It's not proof of a high degree of rationality but it is evidence against being an "idiot" as you said. Especially since the election isn't merely a binary yes/no outcome, we can observe that there was a huge republican blowout exceeding most forecasts(and in fact freddi bet a lot on republican pop vote too at worse odds, as well as some random states, which gives a larger update) This should increase our credence that predicting a republican win was rational. There were also some smart observers with IMO good arguments that trump was favored pre-election, e.g. https://x.com/woke8yearold/status/1851673670713802881 "Guy with somewhat superior election modeling to Nate Silver, a lot of money, and high risk tolerance" is consistent with what we've seen. Not saying that we have strong evidence that Freddi is a genius but we also don't have much reason to think he is an idiot IMO.
4Alexander Gietelink Oldenziel
Okay fair enough "rich idiot" was meant more tongue-in-cheek - that's not what I intended. 
[-]Alexander Gietelink Oldenziel101

That's why I said: "In expectation", "win or lose"

That the coinflip came out one way rather than another doesnt prove the guy had actual inside knowledge. He bought a large part of the shares at crazy odds because his market impact moved the price so much.

But yes, he could be a sharp in sheeps clothings. I doubt it but who knows. EDIT: I calculated the implied private odds for a rational Kelly bettor that this guy would have to have. Suffice to say these private odds seem unrealistic for election betting.

Point is that the winners contribute epistemics and the losers contribute money. The real winner is society [if the questions are about socially-relevant topics].

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[-]Alexander Gietelink Oldenziel7-2

The Sun revolves around the Earth actually

The traditional story is that in olden times people were proudly stupid and thought the human animal lived at the centre of the universe, with all the planets, stars and the sun revolving around the God's creation, made in his image. The church would send anybody that said the sun was at the centre to be burned at the stake. [1]

Except...

there is no absolute sense in which the sun is at the centre of the solar system [2]. It's simply a question of perspective, a choice of frame of reference. 

 

  1. Geocentrism i
... (read more)
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6AprilSR
I think it's pretty good to keep it in mind that heliocentrism is literally speaking just a change in what coordinate system you use, but it is legitimately a much more convenient coordinate system.
6tailcalled
For everyday life, flat earth is more convenient than round earth geocentrism, which in turn is more convenient than heliocentrism. Like we don't constantly change our city maps based on the time of year, for instance, which we would have to do if we used a truly heliocentric coordinate system as the positions of city buildings are not even approximately constant within such a coordinate system. This is mainly because the sun and the earth are powerful enough to handle heliocentrism for you, e.g. the earth pulls you and the cities towards the earth so you don't have to put effort into staying on it. The sun and the planetary motion does remain the most important governing factor for predicting activities on earth, though, even given this coordinate change. We just mix them together into ~epicyclic variables like "day"/"night" and "summer"/"autumn"/"winter"/"spring" rather than talking explicitly about the sun, the earth, and their relative positions.
4Hastings
Since you’re already in it: do you happen to know if the popular system of epicycles accurately represented the (relative, per body) distance of each planet from earth over time, or just the angle? I’ve been curious about this for a while but haven’t had time to dig in. They’d at minimum have to get it right for the moon and sun for predicting eclipse type.
[-]Alexander Gietelink Oldenziel11-1

Will there be >1 individual per solar system?

A recently commonly heard viewpoint on the development of AI states that AI will be economically impactful but will not upend the dominancy of humans. Instead AI and humans will flourish together, trading and cooperating with one another. This view is particularly popular with a certain kind of libertarian economist: Tyler Cowen, Matthew Barnett, Robin Hanson.

They share the curious conviction that the probablity of AI-caused extinction p(Doom) is neglible. They base this with analogizing AI with previous technological transition of humanity, like the industrial revolution or the development of new communication mediums. A core assumption/argument is that AI will not disempower humanity because they will respect the existing legal system, apparently because they can gain from trades with humans.  

The most extreme version of the GMU economist view is Hanson's Age of EM; it hypothesizes radical change in the form of a new species of human-derived uploaded electronic people which curiously have just the same dreary office jobs as we do but way faster. 

Why is there trade & specialization in the first place?

Trade and specializ... (read more)

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[-]Matthew Barnett126

A recently commonly heard viewpoint on the development of AI states that AI will be economically impactful but will not upend the dominancy of humans. Instead AI and humans will flourish together, trading and cooperating with one another. This view is particularly popular with a certain kind of libertarian economist: Tyler Cowen, Matthew Barnett, Robin Hanson.

They share the curious conviction that the probablity of AI-caused extinction p(Doom) is neglible. They base this with analogizing AI with previous technological transition of humanity, like the industrial revolution or the development of new communication mediums. A core assumption/argument is that AI will not disempower humanity because they will respect the existing legal system, apparently because they can gain from trades with humans.

I think this summarizes my view quite poorly on a number of points. For example, I think that:

  1. AI is likely to be much more impactful than the development of new communication mediums. My default prediction is that AI will fundamentally increase the economic growth rate, rather than merely continuing the trend of the last few centuries.

  2. Biological humans are very unlikely to remain

... (read more)
Reply1
4Alexander Gietelink Oldenziel
I see, thank you for the clarification. I should have been more careful with mischaracterizing your views. I do have a question or two about your views if you would entertain me. You say humans wikl be economically obsolete and will 'retire' but there will still be trade between humans and AI. Does trade here just means humans consuming, I.e. trading money for AI goods and services? That doesn't sound like trading in the usual sense where it is a reciprocal exchange of goods and services. How many 'different' AI individuals do you expect there to be ?
4Matthew Barnett
Trade can involve anything that someone "owns", which includes both their labor and their property, and government welfare. Retired people are generally characterized by trading their property and government welfare for goods and services, rather than primarily trading their labor. This is the basic picture I was trying to present. I think the answer to this question depends on how we individuate AIs. I don't think most AIs will be as cleanly separable from each other as humans are, as most (non-robotic) AIs will lack bodies, and will be able to share information with each other more easily than humans can. It's a bit like asking how many "ant units" there are. There are many individual ants per colony, but each colony can be treated as a unit by itself. I suppose the real answer is that it depends on context and what you're trying to figure out by asking the question.
[-]Alexander Gietelink Oldenziel23-5

AGI companies merging within next 2-3 years inevitable?

There are currently about a dozen major AI companies racing towards AGI with many more minor AI companies. The way the technology shakes out this seems like unstable equilibrium. 

It seems by now inevitable that we will see further mergers, joint ventures - within two years there might only be two or three major players left. Scale is all-dominant. There is no magic sauce, no moat. OpenAI doesn't have algorithms that her competitors can't copy within  6-12 months. It's all leveraging compute. Whatever innovations smaller companies make can be easily stolen by tech giants. 

e.g. we might have xAI- Meta, Anthropic- DeepMind-SSI-Google, OpenAI-Microsoft-Apple. 

Actuallly, although this would be deeply unpopular in EA circles it wouldn't be all that surprising if Anthropic and OpenAI would team up. 

And - of course - a few years later we might only have two competitors: USA, China. 

EDIT: the obvious thing to happen is that nvidia realizes it can just build AI itself. if Taiwan is Dune, GPUs are the spice, then nvidia is house Atreides

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4Mo Putera
They've already started... 
[-]Vladimir_Nesov102

In 2-3 years they would need to decide on training systems built in 3-5 years, and by 2027-2029 the scale might get to $200-1000 billion for an individual training system. (This is assuming geographically distributed training is solved, since such systems would need 5-35 gigawatts.)

Getting to a go-ahead on $200 billion systems might require a level of success that also makes $1 trillion plausible. So instead of merging, they might instead either temporarily give up on scaling further (if there isn't sufficient success in 2-3 years), or become capable of financing such training systems individually, without pooling efforts.

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3Bogdan Ionut Cirstea
For similar arguments, I think it's gonna be very hard/unlikely to stop China from having AGI within a couple of years of the US (and most relevant AI chips currently being produced in Taiwan should probably further increase the probability of this). So taking on a lot more x-risk to try and race hard vs. China doesn't seem like a good strategy from this POV.
[-]Bogdan Ionut Cirstea122

Whatever innovations smaller companies make can be easily stolen by tech giants. 

And they / their basic components are probably also published by academia, though the precise hyperparameters, etc. might still matter and be non-trivial/costly to find.

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[-]Leon Lang110

I have a similar feeling, but there are some forces in the opposite direction:

  • Nvidia seems to limit how many GPUs a single competitor can acquire.
  • training frontier models becomes cheaper over time. Thus, those that build competitive models some time later than the absolute frontier have to invest much less resources.
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[-]Alexander Gietelink Oldenziel297

Shower thought - why are sunglasses cool ?

Sunglasses create an asymmetry in the ability to discern emotions between the wearer and nonwearer. This implicitly makes the wearer less predictable, more mysterious, more dangerous and therefore higher in a dominance hierarchy.

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2cubefox
Follow-up question: If sunglasses are so cool, why do relatively few people wear them? Perhaps they aren't that cool after all?
9gwern
Sunglasses can be too cool for most people to be able to wear in the absence of a good reason. Tom Cruise can go around wearing sun glasses any time he wants, and it'll look cool on him, because he's Tom Cruise. If we tried that, we would look like dorks because we're not cool enough to pull it off and it would backfire on us. (Maybe our mothers would think we looked cool.) This could be said of many things: Tom Cruise or Kanye West or fashionable celebrities like them can go around wearing a fedora and trench coat and it'll look cool and he'll pull it off; but if anyone else tries it...
2cubefox
Yeah. I think the technical term for that would be cringe.
1Joey KL
More reasons: people wear sunglasses when they’re doing fun things outdoors like going to the beach or vacationing so it’s associated with that, and also sometimes just hiding part of a picture can cause your brain to fill it in with a more attractive completion than is likely.
6DirectedEvolution
Sunglasses aren’t cool. They just tint the allure the wearer already has.
5Nina Panickssery
Isn't this already the commonly-accepted reason why sunglasses are cool? Anyway, Claude agrees with you (see 1 and 3)
[-]Alexander Gietelink Oldenziel110

yes very lukewarm take

also nice product placement nina

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[-]quila121

also see ashiok from mtg: whole upper face/head is replaced with shadow

also, masks 'create an asymmetry in the ability to discern emotions' but do not seem to lead to the rest

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[-]Alexander Gietelink Oldenziel110

That's a good counterexample ! Masks are dangerous and mysterious, but not cool in the way sunglasses are in agree

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1quila
oh i meant medical/covid ones. could also consider furry masks and the cat masks that femboys often wear (e.g. to obscure masculine facial structure), which feel cute rather than 'cool', though they are more like the natural human face in that they display an expression ("the face is a mask we wear over our skulls")
4Garrett Baker
Yeah pretty clearly these aren’t cool because they imply the wearer is diseased.
1quila
how? edit: maybe you meant just the first kind
2Garrett Baker
Yeah, I meant medical/covid masks imply the wearer is diseased. I would have also believed the cat mask is a medical/covid mask if you hadn't give a different reason for wearing it, so it has that going against it in terms of coolness. It also has a lack of plausible deniability going against it too. If you're wearing sunglasses there's actually a utilitarian reason behind wearing them outside of just creating information asymmetry. If you're just trying to obscure half your face, there's no such plausible deniability. You're just trying to obscure your face, so it becomes far less cool.
7Garrett Baker
I think with sunglasses there’s a veneer of plausible deniability. They in fact have a utilitarian purpose outside of just creating information asymmetry. If you’re wearing a mask though, there’s no deniability. You just don’t want people to know where you’re looking.
3leogao
there is an obvious utilitarian reason of not getting sick
2Garrett Baker
Oh I thought they meant like ski masks or something. For illness masks, the reason they’re not cool is very clearly that they imply you’re diseased. (To a lesser extent too that your existing social status is so low you can’t expect to get away with accidentally infecting any friends or acquaintances, but my first point is more obvious & defensible)
[-]Alexander Gietelink Oldenziel104

Of Greater Agents and Lesser Agents

How do more sophisticated decision-makers differ from less sophisticated decision-makers in their behaviour and values?

Smarter more sophisticated decisionmakers engage in more and more complex commitments — including meta-commitments not to commit. Consequently, the values and behaviour of these more sophisticated decisionmakers "Greater Agents" are systematically biased compared to less sophisticated decisionmakers "Lesser Agents".

*******************************

Compared to Lesser Agents, the Greater Agents are more judgemental, (self-)righteous, punish naivité, are more long-term oriented, adaptive, malleable, self-modifying, legibly trustworthy and practice more virtue-signalling, strategic, engage in self-reflection & metacognition, engage in more thinking, less doing, symbolic reasoning, consistent & 'rational' in their preferences, they like money & currency more, sacred values less, value engagement in thinking over doing, engaged in more "global" conflicts [including multiverse-wide conflicts throguh acausal trade], less empirical, more rational, more universalistic in their morals, and more cosmopolitan in their esthetics, they... (read more)

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[-]Alexander Gietelink Oldenziel150

Entropy and AI Forecasting 

Until relatively recently (2018-2019?) I did not seriously entertain the possibility that AGI in our lifetime was possible. This was a mistake, an epistemic error. A rational observer calmly and objectively considering the evidence for AI progress over the prior decades - especially in the light of rapid progress in deep learning - should have come to the reasonable position that AGI within 50 years was a serious possibility (>10%). 

AGI plausibly arriving in our lifetime was a reasonable position. Yet this possibility was almost universally ridiculed or ignored or by academics and domain experts. One can find quite funny interview with AI experts on Lesswrong from 15 years ago. The only AI expert agreeing with the Yudkowskian view of AI in our lifetime was Jurgen Schmidthuber. The other dozen AI experts denied it as unknowable or even denied the hypothetical possibility of AGI. 

Yudkowsky earns a ton of Bayes points for anticipating the likely arrival of AGI in our lifetime long before the deep learning took off. 

**************************

We are currently experiencing a rapid AI takeoff, plausibly culminating in superintelligence by ... (read more)

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2Bogdan Ionut Cirstea
Fwiw, in 2016 I would have put something like 20% probability on what became known as 'the scaling hypothesis'. I still had past-2035 median timelines, though. 
2Alexander Gietelink Oldenziel
What did you mean exactly in 2016 by the scaling hypothesis ? Having past 2035 timelines and believing in the pure scaling maximalist hypothesis (which fwiw i don't believe in for reasons i have explained elsewhere) are in direct conflict so id be curious if you could more exactly detail your beliefs back then.
4Bogdan Ionut Cirstea
Something like 'we could have AGI just by scaling up deep learning / deep RL, without any need for major algorithmic breakthroughs'. I'm not sure this is strictly true, though I agree with the 'vibe'. I think there were probably a couple of things in play: * I still only had something like 20% on scaling, and I expected much more compute would likely be needed, especially in that scenario, but also more broadly (e.g. maybe something like the median in 'bioanchors' - 35 OOMs of pretraining-equivalent compute, if I don't misremember; though I definitely hadn't thought very explicitly about how many OOMs of compute at that time) - so I thought it would probably take decades to get to the required amount of compute. * I very likely hadn't thought hard and long enough to necessarily integrate/make coherent my various beliefs.  * Probably at least partly because there seemed to be a lot of social pressure from academic peers against even something like '20% on scaling', and even against taking AGI and AGI safety seriously at all. This likely made it harder to 'viscerally feel' what some of my beliefs might imply, and especially that it might happen very soon (which also had consequences in delaying when I'd go full-time into working on AI safety; along with thinking I'd have more time to prepare for it, before going all in).
[-]interstice117

I know of only two people who anticipated something like what we are seeing far ahead of time; Hans Moravec and Jan Leike

I didn't know about Jan's AI timelines. Shane Legg also had some decently early predictions of AI around 2030(~2007 was the earliest I knew about)

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6Alexander Gietelink Oldenziel
Oh no uh-oh I think I might have confused Shane Legg with Jan Leike
2interstice
That's probably the one I was thinking of.
-1Noosphere89
Yeah, I do think that Moravec and Leike got the AI situation most correct, and yeah people were wrong to dismiss Yudkowsky for having short timelines. This was the thing they got most correct, which is interesting because unfortunately, Yudkowsky got almost everything else incorrect about how superhuman AIs would work, and also got the alignment situation very wrong as well, which is very important to take note of. LW in general got short timelines and the idea that AI will probably be the biggest deal in history correct, but went wrong in assuming they knew well about how AI would eventually work (remember the times when Eliezer Yudkowsky dismissed neural networks working for capabilities instead of legible logic?) and also got the alignment situation very wrong, due to way overcomplexifying human values and relying on the evopsych frame way too much for human values, combined with not noticing that the differences between humans and evolution that mattered for capabilities also mattered for alignment. I believe a lot of the issue comes down to incorrectly conflating the logical possibility of misalignment with the probability of misalignement being high enough that we should take serious action, and the interlocutors they talked with often denied the possibility that misalignment could happen at all, but LWers then didn't realize that reality doesn't grade on a curve, and though their arguments were better than their interlocutors, that didn't mean they were right.
8quetzal_rainbow
I think this statement is incredibly overconfident, because literally nobody knows how superhuman AI would work. And, I think, this is general shape of problem: incredible number of people got incredibly overindexed on how LLMs worked in 2022-2023 and drew conclusions which seem to be plausible, but not as probable as these people think.
4Noosphere89
Okay, I talked more on what conclusions we can draw from LLMs that actually generalize to superhuman AI here, so go check that out: https://www.lesswrong.com/posts/tDkYdyJSqe3DddtK4/alexander-gietelink-oldenziel-s-shortform#mPaBbsfpwgdvoK2Z2 The really short summary is human values are less complicated and more dependent on data than people thought, and we can specify our values rather easily without it going drastically wrong: This is not a property of LLMs, but of us.
2Garrett Baker
is that supposed to be a link?
4Noosphere89
I rewrote the comment to put the link immediately below the first sentence.
4Noosphere89
The link is at the very bottom of the comment.
[-]Alexander Gietelink Oldenziel108

Yudkowsky didnt dismiss neural networks iirc. He just said that there were a lot of different approaches to AI and from the Outside View it didnt seem clear which was promising - and plausibly on an Inside View it wasnt very clear that aritificial neural networks were going to work and work so well.

Re:alignment I dont follow. We dont know who will be proved ultimately right on alignment so im not sure how you can make such strong statements about whether Yudkowsky was right or wrong on this aspect.

We havent really gained that much bits on this question and plausibly will not gain many until later (by which time it might be too late if Yudkowsky is right).

I do agree that Yudkowsky's statements occasionally feel too confidently and dogmatically pessimistic on the question of Doom. But I would argue that the problem is that we simply dont know well because of irreducible uncertainty - not that Doom is unlikely.

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6Noosphere89
Mostly, I'm annoyed by how much his argumentation around alignment matches the pattern of dismissing various approaches to alignment using similar reasoning to how he dismissed neural networks: Even if it was correct to dismiss neural networks years ago, it isn't now, so it's not a good sign that the arguments rely on this issue: https://www.lesswrong.com/posts/wAczufCpMdaamF9fy/my-objections-to-we-re-all-gonna-die-with-eliezer-yudkowsky#HpPcxG9bPDFTB4i6a I am going to argue that we do have quite a lot of bits on alignment, and the basic argument can be summarized like this: Human values are much less complicated than people thought, and also more influenced by data than people thought 15-20 years ago, and thus much, much easier to specify than people thought 15-20 years ago. That's the takeaway I have from current LLMs handling human values, and I basically agree with Linch's summary of Matthew Barnett's post on the historical value misspecification argument of what that means in practice for alignment: https://www.lesswrong.com/posts/i5kijcjFJD6bn7dwq/evaluating-the-historical-value-misspecification-argument#N9ManBfJ7ahhnqmu7 It's not about LLM safety properties, but about what has been revealed about our values. Another way to say it is that we don't need to reverse-engineer social instincts for alignment, contra @Steven Byrnes, because we can massively simplify what the social instinct parts of our brain that contribute to alignment are doing in code, because while the mechanisms for how humans get their morality and not be psychopaths are complicated, it doesn't matter, because we can replicate it's function with much simpler code and data, and go to a more blank-slate design for AIs: https://www.lesswrong.com/posts/PTkd8nazvH9HQpwP8/building-brain-inspired-agi-is-infinitely-easier-than#If_some_circuit_in_the_brain_is_doing_something_useful__then_it_s_humanly_feasible_to_understand_what_that_thing_is_and_why_it_s_useful__and_to_write_our_own_CPU_code_t
6Alexander Gietelink Oldenziel
It's a plausible argument imho. Time will tell. To my mind an important dimension, perhaps the most important dimensions is how values be evolve under reflection. It's quite plausible to me that starting with an AI that has pretty aligned values it will self-reflect into evil. This is certainly not unheard of in the real world (let alone fiction!). Of course it's a question about the basin of attraction around helpfulness and harmlessness. I guess I have only weak priors on what this might look like under reflection, although plausibly friendliness is magic.
4Garrett Baker
I disagree, but could be a difference in definition of what "perfectly aligned values" means. Eg if the AI is dumb (for an AGI) and in a rush, sure. If its a superintelligence already, even in a rush, seems unlikely. [edit:] If we have found an SAE feature which seems to light up for good stuff, and down for bad stuff 100% of the time, then we clamp it, then yeah, that could go away on reflection.
4Noosphere89
Another way to say it is how values evolve in OOD situations. My general prior, albeit reasonably weak is that the best single way to predict how values evolve is looking at their data sources, as well as what data they received up to now, and the second best way to predict it is looking at what their algorithms are, especially for social situations, and that most of the other factors don't matter nearly as much.
[-]Alexander Gietelink Oldenziel30

Pseudorandom warp fields

A highly exaggerated and intensely oscillatory 1D loss landscape representing a neural network training on a pseudorandom-hard function. The landscape should feature extremely sharp, frequent peaks and valleys, showing an almost chaotic and warped pattern. Include intense fluctuations and dramatic ridges, illustrating a landscape that is incredibly difficult to optimize. The overall visual should convey an impression of a 'cursed' optimization path, with a vibrant color scheme to emphasize the oscillatory and warped nature.

[tl;dr the loss landscape around a set of weights encoding an unlearnable 'pseudorandom' function will be warped in such a way that gradient optimizers will bob around for exponentially long. ]

Unlearnable Functions: Sample Complexity and Time Complexity

Computational learning theory contains numerous 'no-go' results indicating that many functions are not tractably learnable.

The most classical result is probably the VC dimension and PAC learnability. A good example to think about are parity functions. The output is, in some sense, ver... (read more)

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[-]Alexander Gietelink Oldenziel140

I have an embarrasing confession to make. I don't understand why PvsNP is so hard. 

[I'm in good company since apparently Richard Feynmann couldn't be convinced it was a serious open problem.] 

I think I understand PvsNP and its many variants like existence of one-way function is about computational hardness of certain tasks. It is surprising that we have such strong intuitions that some tasks are computationally hard but we fail to be able to prove it!

Of course I don't think I can prove it and I am not foolish enough to spend significant amount of time on trying to prove it. I still would like to understand the deep reasons  why it's so hard to prove computational hardness results. That means I'd like to understand why certain general proof strategies are impossible or very hard. 

There is an old argument by Shannon that proves that almost every* Boolean function has exponential circuit depth. This is a simple counting argument. Basically, there are exponentially many more Boolean functions than there are circuits. It's hard to give explicit examples of  computationally hard functions** but we can easily show they are plentiful. 

This would seem to settle... (read more)

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8Kaarel
I guess a central issue with separating NP from P with a counting argument is that (roughly speaking) there are equally many problems in NP and P. Each problem in NP has a polynomial-time verifier, so we can index the problems in NP by polytime algorithms, just like the problems in P. in a bit more detail: We could try to use a counting argument to show that there is some problem with a (say) <n2 time verifier which does not have any (say) <n1000 time solver. To do this, we'd like to say that there are more n2 verifier problems than n1000 algorithms. While I don't really know how we ought to count these (naively, there are ℵ0 of each), even if we had some decent notion of counting, there would almost certainly just be more <n1000 algorithms than <n2 verifiers (since the n2 verifiers are themselves <n1000 algorithms).
3Alexander Gietelink Oldenziel
Thank you Kaarel - this the kind of answer I was after.
5Dmitry Vaintrob
Looking at this again, I'm not sure I understand the two confusions. P vs. NP isn't about functions that are hard to compute (they're all polynomially computable), rather functions that are hard to invert, or pairs of easily computable functions that hard to prove are equal/not equal to each other. The main difference between circuits and Turing machines is that circuits are finite and bounded to compute whereas the halting time of general Turing machines is famously impossible to determine. There's nothing special about Boolean circuits: they're an essentially complete model of what can be computed in polynomial time (modulo technicalities)
3Dmitry Vaintrob
In particular, it's not hard to produce a computable function that isn't given by a polynomial-sized circuit (parity doesn't work as it's polynomial, but you can write one down using diagonalization -- it would be very long to compute, but computable in some suitably exponentially bounded time). But P vs. NP is not about this: it's a statement that exists fully in the world of polynomially computable functions.
2Noosphere89
Basically, there are 3 main barriers to proving P not equaling NP. One, you have to actually show that there exists a hard function that isn't in P, and it's not enough to prove that there are exponentially many hard functions, because it might be that a circuit computing an NP-complete problem has a linear time bound. And natural proofs argue that unless cryptographically hard functions don't exist, the common way to prove circuit lower bounds also can't prove P vs NP (Technical details are below:) https://en.wikipedia.org/wiki/Natural_proof Also, both of the strategies cannot relativize or algebrize, where relativization means that if we give a fixed oracle tape O consisting of a single problem you can solve instantly to all parties doesn't change the conclusion for all oracle tapes O. Many results like this, including possibly your attempts to prove via counting arguments almost certainly relativize, and even if they don't, they algebrize, and the technical details are below for algebrization are here, since I already explained relativization above. https://www.scottaaronson.com/papers/alg.pdf But that's why proving P vs NP is so hard technically.
1Dmitry Vaintrob
Yeah I think this is a good place to probe assumptions, and it's probably useful to form world models where you probability of P = NP is nonzero (I also like doing this for inconsistency of logic). I don't have an inside view, but like Scott Aaronson on this: https://www.scottaaronson.com/papers/pnp.pdf:  
2Noosphere89
My real view on P vs NP is that at this point, I think P almost certainly not equal to NP, and that any solving of NP-complete problems efficiently to the standard of complexity theorists requires drastically changing the model of computation, which corresponds to drastic changes in our physics assumptions like time travel actually working according to Deutsch's view (and there being no spurious fixed-points).
2Mo Putera
You might be interested in Scott Aaronson's thoughts on this in section 4: Why Is Proving P != NP Difficult?, which is only 2 pages. 
2Dmitry Vaintrob
looks like you referenced the same paper before me while I was making my comment :)
1Mo Putera
Ha, that's awesome. Thanks for including the screenshot in yours :) Scott's "invisible fence" argument was the main one I thought of actually.
2TsviBT
See https://en.wikipedia.org/wiki/Natural_proof
4tailcalled
We know there are difficult computational problems. P vs NP is more narrow than that; it's sometimes phrased as "are there problems that are not difficult to verify but difficult to solve?", where "difficult" means that it cannot be done in asymptotically polynomial time.
2Alexander Gietelink Oldenziel
Yes, I am familiar with the definition of PvsNP. That's not what I am asking.
2Noosphere89
The point is that you can't use the result that there exists a hard function, since all you know is that the function is hard, not whether it's in NP, which is a basic problem for your scheme. Your counting argument for Turing Machines also likely have this problem, and even if not, I see no reason why I couldn't relativize the results, which is a no-go for P vs NP proof attempts.
[-]quetzal_rainbow102

I'm just computational complexity theory enthusiast, but my opinion is that P vs NP centered explanation of computational complexity is confusing. Explanation of NP should happen in the very end of the course.

There is nothing difficult in proving that computationally hard functions exist: time hierarchy theorem implies that, say, P is not equal EXPTIME. Therefore, EXPTIME is "computationally hard". What is difficult is to prove that very specific class of problems which have zero-error polynomial-time verification algorithms is "computationally hard".

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[-]Alexander Gietelink Oldenziel1-2

The pseudorandom lie under the Lava lamp

Our observations are compatible with a world that is generated by a Turing machine with just a couple thousand bits.

That means that all the seemingly random bits we see in Geiger counters, Lava lamps, gasses and the like is only pseudorandomness in actuality.

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2Noosphere89
I'm fairly sure it isn't actually compatible with a world that is generated by a Turing Machine, but the basic problem is all the real number constants in the universe which in QM are infinitely precise, not just arbitrarily precise, which wreaks havoc on Turing Machine models, but Signer has another explanation of another problem that is fatal to the approach.
4Lucius Bushnaq
The randomness of the Geiger counter comes from wave function decoherence. From the perspective of any observers who are part of the world generated by the Turing machine, this is irreducible indexical uncertainty.  I don't know how many of the random bits in Lava lamps come from decoherence.  
1Signer
Yes, but this is kinda incompatible with QM without mangled worlds.
2Alexander Gietelink Oldenziel
Oh ? What do you mean ! I don't know about mangled worlds
1Signer
https://mason.gmu.edu/~rhanson/mangledworlds.html I mean that if turing machine is computing universe according to the laws of quantum mechanics, observers in such universe would be distributed uniformly, not by Born probability. So you either need some modification to current physics, such as mangled worlds, or you can postulate that Born probabilities are truly random.
2TAG
I assume you mean the laws of QM except the collapse postulate. Not at all. The problem is that their observations would mostly not be in a classical basis. Born probability relates to observations, not observers. Or collapse. Mangled worlds is kind of a nothing burger--its a variation on the idea than interference between superposed states leads to both a classical basi and the Born probabilities, which is an old idea, but wihtout making it any more quantiative. ??
1Signer
I phrased it badly, but what I mean is that there is a simulation of Hilbert space, where some regions contain patterns that can be interpreted as observers observing something, and if you count them by similarity, you won't get counts consistent with Born measure of these patterns. I don't think basis matters in this model, if you change basis for observer, observations and similarity threshold simultaneously? Change of basis would just rotate or scale patterns, without changing how many distinct observers you can interpret them as, right? Collapse or reality fluid. The point of mangled worlds or some other modification is to evade postulating probabilities on the level of physics.
2Viliam
Connotationally, even if things are pseudorandom, they still might be "random" for all practical purposes, e.g. if the only way to calculate them is to simulate the entire universe. In other words, we may be unable to exploit the pseudorandomness.
2Alexander Gietelink Oldenziel
Yes, this is exactly the point. 
6TsviBT
IDK why you think that TM is simpler than one that computes, say, QM. But either way, I don't know why to favor (in terms of ascribing reality-juice) worlds that are simple TMs but not worlds that are simple physics equations. You can complain that you don't know how to execute physics equations, but I can also complain that I don't know how to execute state transitions. (Presumably there's still something central and real about some things being more executable than others; I'm just saying it's not clear what that is and how it relates to reality-juice and TMs vs physics.)
4Lucius Bushnaq
I'm confused, in what sense don't we know how to do this? Lattice quantum field theory simulations work fine. 
2TsviBT
For example, we couldn't execute continuum models.
2Noosphere89
Of course, just because we can't execute continuum models, or models of physics that require actually infinite computation, not just unlimited amounts of compute, doesn't mean the universe can't execute such a program.
2TsviBT
Ok, another example is that physical laws are generally descriptive, not fully specified worlds. You can "simulate" the ideal gas law or Maxwell's equations but you're doing extra work beyond just what the equations say (like, you have to run "import diffeq" first, and pick a space topology, and pick EM fields) and it's not a full world.
2Noosphere89
Yes, which is why I explicitly said that the scenario involves actual/manifest infinity of compute to actually implement the equations to actually make it a full world, and if you wanted to analogize physical laws to a computer system, I'd argue that they are analogous to the source code of a computer, or the rules/state of a Turing Machine, and I'm arguing that there is a very vast difference between us simulating Maxwell's equations or the ideal gas law and the universe simulating whatever physical laws we turn out to actually have, and all of the difference is the universe has an actual infinity/manifest infinity of compute like FLOPs/FLOP/s and memory such that you can actually run the equations directly without relying on shortcuts to make the problem more tractable, whereas we have to rely on shortcuts that change the physics a little but get us a reasonable answer in a reasonable time.
2TsviBT
Oh I misparsed your comment somehow, I don't even remember how.
2Alexander Gietelink Oldenziel
This distinction isnt material. The distinction I am getting at is whether our physics (simulation) is using a large K-incompressible seed or not.
4TsviBT
QM doesn't need a random seed!
2Mikhail Samin
* Probability is in the mind. There's no way to achieve entanglement between what's necessary to make these predictions and the state of your brain, so for you, some of these are random. * In multi-worlds, the Turing machine will compute many copies of you, and there might be more of those who see one thing when they open their eyes than of those who see another thing. When you open your eyes, there's some probability of being a copy that sees one thing and a copy that sees the other thing. In a deterministic world with many copies of you, there's "true" randomness in where you end up opening your eyes.
6TsviBT
I think he's saying that there's a simple-ish deterministic machine that uses pseudorandomness to make a world observationally equivalent to ours. Since it's simple, it has a lot of the reality-juice, so it's most of "where we really are".
[-]Alexander Gietelink Oldenziel6-9

God exists because the most reasonable take is the Solomonoff Prior. 

A funny consequence of that is that Intelligent Design will have a fairly large weight in the Solomonoff prior. Indeed the simulation argument can be seen as a version of Intelligent Design. 

The Abrahamic God hypothesis is still substantially downweighted because it seems to involve many contigent bits - i.e noisy random bits that can't be compressed. The Solomonoff prior therefore has to downweight them. 

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1Nate Showell
Why are you a realist about the Solomonoff prior instead of treating it as a purely theoretical construct?
4Mitchell_Porter
Please demonstrate that the Solomonoff prior favors simulation.
4Thomas Kwa
See e.g. Xu (2020) and recent criticism.
2Mitchell_Porter
I was expecting an argument like "most of the probability measure for a given program, is found in certain embeddings of that program in larger programs". Has anyone bothered to make a quantitative argument, a theorem, or a rigorous conjecture which encapsulates this claim?
4Thomas Kwa
I don't think that statement is true since measure drops off exponentially with program length.
0Alexander Gietelink Oldenziel
This is a common belief around here. Any reason you are skeptical?
0Mitchell_Porter
Thomas Kwa just provided a good reason: "measure drops off exponentially with program length". So embeddings of programs within other programs - which seems to be what a simulation is, in the Solomonoff framework - are considered exponentially unlikely.  edit: One could counterargue that programs simulating programs increase exponentially in number. Either way, I want to see actual arguments or calculations.  
2Thomas Kwa
I just realized what you meant by embedding-- not a shorter program within a longer program, but a short program that simulates a potentially longer (in description length) program. As applied to the simulation hypothesis, the idea is that if we use the Solomonoff prior for our beliefs about base reality, it's more likely to be laws of physics for a simple universe containing beings that simulate this one as it is to be our physics directly, unless we observe our laws of physics to be super simple. So we are more likely to be simulated by beings inside e.g. Conway's Game of Life than to be living in base reality. I think the assumptions required to favor simulation are something like * there are universes with physics 20 bits (or whatever number) simpler than ours in which intelligent beings control a decent fraction >~1/million of the matter/space * They decide to simulate us with >~1/million of their matter/space * There has to be some reason the complicated bits of our physics are more compressible by intelligences than by any compression algorithms simpler than their physics; they can't just be iterating over all permutations of simple universes in order to get our physics * But this seems fairly plausible given that constructing laws of physics is a complex problem that seems way easier if you are intelligent.  Overall I'm not sure which way the argument goes. If our universe seems easy to efficiently simulate and we believe the Solomonoff prior, this would be huge evidence for simulation, but maybe we're choosing the wrong prior in the first place and should instead choose something that takes into account runtime.
[-]Alexander Gietelink Oldenziel40

The Virtue of Comparison Shopping 
Comparison shopping, informed in-depth reviewing, answering customer surveys plausibly have substantial positive externalities. It provides incentives through local actors, avoids preference falsification or social desirability bias, and is non-coercive & market-based. 

Plausibly it is even has a better social impact than many kinds of charitable donations or direct work. This is not so hard since it seems that the latter contains many kinds of interventions that have neglibible or even negative impact. 

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[-]Alexander Gietelink Oldenziel40

Gaussian Tails and Exceptional Performers

West African athletes dominate sprinting events, East Africans excel in endurance running, and despite their tiny population Icelanders have shown remarkable prowess in weightlifting competitions.  We examine the Gaussian approximation for a simple additive genetic model for these observations. 

The Simple Additive Genetic Model

Let's begin by considering a simple additive genetic model. In this model, a trait T is influenced by n independent genes, each contributing a small effect, along with environmental ... (read more)

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[-]Alexander Gietelink Oldenziel4-2

tl;dr 

Salmon swimming upstream to their birthing grounds to breed may be that rare form of group selection. 

Pure Aryan Salmon

Salmon engage in anodromous reproduction; they risk their lives to swim up rivers to return to their original place of birth and reproduce there. 

Most species of salmon die there, only reproducing at the birthing grounds. Many don't make it at all. The ones that survive the run upstream will die shortly after, a biologically triggered death sentence. If the cost is immense - the benefits must be even greater.

The more u... (read more)

Reply221
2Raemon
I am confused about what I'm reading. The magikarp gave me a doubletake and like "wait, are magikarp also just a totally real fish?" but after some googling it seems like "nope, that's really just a pokemon", and now I can't tell if the rest of the post is like a parody or what.
0habryka
This post feels like raw GPT-3 output. It's not even GPT-3.5 or GPT-4 level, which makes this additionally confusing.  Maybe a result of playing around with base models?
4Garrett Baker
This seems fairly normal for an Alexander post to me (actually, more understandable than the median Alexander shortform). I think the magikarp is meant to be 1) an obfuscation of salamon, and 2) a reference to solid gold magikarp. @Raemon 
2habryka
After rereading it like 4 times I am now less convinced it's GPT output. I still feel confused about a lot of sentences, but I think half of it was just the lack of commas in sentences like "One explanationc could be that anodromous reproduction is a stable game-theoretic equilibrium in which the selective pressure on the salmon species is higher encouraging higher biological fitness".
2Richard_Kennaway
Please tell us more about the magikarps.
1Mateusz Bagiński
Bdelloidea are an interesting counterexample: they evolved obligate parthenogenesis ~25 mya.
5Alexander Gietelink Oldenziel
My understanding from reading Mitochondria: Power, Sex, Suicide is that they are not truly asexual but turn out to do some sexual recombination. I don't remember the details and I'm not an expert though so wouldn't put my hand in the fire for it.
[-]Alexander Gietelink Oldenziel15-3

Crypticity, Reverse Epsilon Machines and the Arrow of Time?

[see https://arxiv.org/abs/0902.1209 ]

Our subjective experience of the arrow of time is occasionally suggested to be an essentially entropic phenomenon. 

This sounds cool and deep but crashes headlong into the issue that the entropy rate and the excess entropy of any stochastic process is time-symmetric. I find it amusing that despite hearing this idea often from physicists and the like apparently this rather elementary fact has not prevented their storycrafting. 

Luckily, computational mechanics provides us with a measure that is not time symmetric: the stochastic complexity of the epsilon machine 

For any stochastic process we may also consider the epsilon machine of the reverse process, in other words the machine that predicts the past based on the future. This can be a completely different machine whose reverse stochastic complexity  is not equal to . 

Some processes are easier to predict forward than backward. For example, there is considerable evidence that language is such a process. If the stochastic complexity and the reverse stochastic complexity differ we speak of a causally a... (read more)

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[-]Lucius Bushnaq1512

This sounds cool and deep but crashes headlong into the issue that the entropy rate and the excess entropy of any stochastic process is time-symmetric.
 

It's time symmetric around a starting point  of low entropy. The further  is from , the more entropy you'll have, in either direction. The absolute value  is what matters.


In this case,  is usually taken to be the big bang.  So the further in time you are from the big bang, the less the universe is like a dense uniform soup with little structure that needs description, and the higher your entropy will be. That's how you get the subjective perception of temporal causality. 

Presumably, this would hold to the other side of  as well, if there is one. But we can't extrapolate past , because close to  everything gets really really energy dense, so we'd need to know how to do quantum gravity to calculate what the state on the other side might look like.  So we can't check that.  And the notion of time as we're discussing it here might break down at those energies anyway.

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3cubefox
See also the Past Hypothesis. If we instead take a non-speculative starting point as t0, namely now, we could no longer trust our memories, including any evidence we believe to have about the entropy of the past being low, or about physical laws stating that entropy increases with distance from t0. David Albert therefore says doubting the Past Hypothesis would be "epistemically unstable".
[-]Alexander Gietelink Oldenziel171

What did Yudkoswky get right?

  • The central problem of AI alignment. I am not aware of anything in subsequent work that is not already implicit in Yudkowsky's writing.
  • Short timelines avant le lettre. Yudkowsky was predicting AGI in his lifetime from the very start when most academics, observers, AI scientists, etc considered AGI a fairytale.
  • Inherent and irreducible uncertainty of forecasting, foolishness of precise predictions. 
  • The importance of (Pearlian) causality, Solomonoff Induction as theory of formal epistemology, Bayesian statistics, (Shannon) information theory, decision theory [especially UDT-shaped things].  
  • (?nanotech, ?cryonics)
  • if you had a timemachine to go back to 2010 you should buy bitcoin and write Harry Potter fanfiction
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9sunwillrise
From Akash's summary of the discussion between Conor Leahy and Michael Trazzi on "The Inside View" from ~ 1.5 years ago: In Leahy's own words: Much of the discussion at the time (example) focused on the particular application of this idea in the context of the "Death with Dignity" post, but I think this effect was visible much earlier on, most prominently in the Sequences themselves. As I see it, this did not affect the content that was being communicated so much as it did the vibe, the more ineffable, emotional, and hard-to-describe-using-S2 stylistic packaging that enveloped the specific ideas being conveyed. The latter [1], divorced from Eliezer's presentation of them, could be (and often are) thought of as dry or entirely technical, but his writing gave them a certain life that made them rather unforgettable and allowed them to hit much harder (see "How An Algorithm Feels From the Inside" and "Beyond the Reach of God" as the standard examples of this). 1. ^ Stuff like probability theory, physics (Quantum Mechanics in particular), philosophy of language, etc.
5Garrett Baker
I think I'd agree with everything you say (or at least know what you're looking at as you say it) except for the importance of decision theory. What work are you watching there?
5habryka
As one relevant consideration, I think the topic of "will AI kill all humans" is a question whose answer relies in substantial parts on TDT-ish considerations, and is something that a bunch of value systems I think reasonably care a lot about. Also I think what  superintelligent systems will do will depend a lot on decision-theoretic considerations that seem very hard to answer from a CDT vs. EDT-ish frame.
5Alexander Gietelink Oldenziel
I think I speak for many when I ask you to please elaborate on this!
7habryka
Oh, I thought this was relatively straightforward and has been discussed a bunch. There are two lines of argument I know for why superintelligent AI, even if unaligned, might not literally kill everyone, but keep some humans alive:  1. The AI might care a tiny bit about our values, even if it mostly doesn't share them 2. The AI might want to coordinate with other AI systems that reached superintelligence to jointly optimize the universe. So in a world where there is only a 1% chance that we align AI systems to our values, then even in unaligned worlds we might end up with AI systems that adopt our values as a 1% mixture in its utility function (and also consequently in those 1% of worlds, we might still want to trade away 99% of the universe to the values that the counterfactual AI systems would have had) Some places where the second line of argument has been discussed:  * This comment by Ryan Greenblatt:[1] https://www.lesswrong.com/posts/tKk37BFkMzchtZThx/miri-2024-communications-strategy?commentId=xBYimQtgASti5tgWv  * This comment by Paul Christiano:[2] https://www.lesswrong.com/posts/2NncxDQ3KBDCxiJiP/cosmopolitan-values-don-t-come-free?commentId=ofPTrG6wsq7CxuTXk  1. ^ 2. ^
8Raemon
See also: https://www.lesswrong.com/posts/rP66bz34crvDudzcJ/decision-theory-does-not-imply-that-we-get-to-have-nice 
[-]Alexander Gietelink Oldenziel80

On the word 'theory'. 

The word 'theory' is oft used and abused.

there is two ways 'theory' is used that are different and often lead to confusion. 

Theory in thescientific sense
the way a physicist would use: it's a model of the world that is either right or wrong. there might be competing theories and we neeed to have empirical evidence to figure out which one's right. Ideally, they agree with empirical evidence or at least are highly falsifiable. Importantly, if two theories are to conflict they need to actually speak about the same variables, the... (read more)

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2Mateusz Bagiński
Formal frameworks considered in isolation can't be wrong. Still, they often come with some claims like "framework F formalizes some intuitive (desirable?) property or specifies the right way to do some X and therefore should be used in such-and-such real-world situations". These can be disputed and I expect that when somebody claims like "{Bayesianism, utilitarianism, classical logic, etc} is wrong", that's what they mean.
2Stefan_Schubert
There's a related confusion between uses of "theory" that are neutral about the likelihood of the theory being true, and uses that suggest that the theory isn't proved to be true. Cf the expression "the theory of evolution". Scientists who talk about the "theory" of evolution don't thereby imply anything about its probability of being true - indeed, many believe it's overwhelmingly likely to be true. But some critics interpret this expression differently, saying it's "just a theory" (meaning it's not the established consensus).
[-]Alexander Gietelink Oldenziel223

Current work on Markov blankets and Boundaries on LW is flawed and outdated. State of the art should factor through this paper on Causal Blankets; https://iwaiworkshop.github.io/papers/2020/IWAI_2020_paper_22.pdf

A key problem for accounts of blankets and boundaries I have seen on LW so far is the following elementary problem (from the paper):
"Therefore, the MB [Markov Blanket] formalism forbids interdependencies induced by past events that are kept in memory, but may not directly influence the present state of the blankets.

Thanks to Fernando Rosas telling me about this paper. 

Reply2
1Stephen Fowler
I have only skimmed the paper. Is my intuition correct that in the MB formalism, past events that are causally linked to are not included in the Markov Blanket, but the node corresponding to the memory state still is included in the MB? That is, the influence of the past event is mediated by a node corresponding to having memory of that past event?
3Mateusz Bagiński
Well, past events--before some time t--kind of obviously can't be included in the Markov blanket at time t. As far as I understand it, the MB formalism captures only momentary causal interactions between "Inside" and "Outside" but doesn't capture a kind of synchronicity/fine-tuning-ish statistical dependency that doesn't manifest in the current causal interactions (across the Markov blanket) but is caused by past interactions. For example, if you learned a perfect weather forecast for the next month and then went into a completely isolated bunker but kept track of what day it was, your beliefs and the actual weather would be very dependent even though there's no causal interaction (after you entered the bunker) between your beliefs and the weather. This is therefore omitted by MBs and CBs want to capture that.
[-]Gunnar_Zarncke100

You may want to make this a linkpost to that paper as that can then be tagged and may be noticed more widely.

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[-]Alexander Gietelink Oldenziel40

Why do people like big houses in the countryside /suburbs?

Empirically people move out to the suburbs/countryside when they get children and/or gain wealth. Having a big house with a large yard is the quintessential American dream. 

but why? Dense cities are economoically more productive, commuting is measurably one of the worst factors for happiness and productivity. Raising kids in small houses is totally possible and people have done so at far higher densities in the past. 

Yet people will spend vast amounts of money on living in a large house wi... (read more)

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5Dagon
Note that it could easily be culturally evolved, not genetically.  I think there's a lot of explanatory power in the land=status cultural belief as well.  But really, I think there's a typical mind fallacy that blinds you to the fact that many people legitimately and truly prefer those tradeoffs over denser city living.  Personally, my tastes (and the character of many cities' cores) have noticeably changed over my lifetime - in my youth, I loved the vibrance and variety, and the relatively short commute of being in a city.  Now, I value the privacy and quiet that suburban living (still technically in-city, but in a quiet area) gets me. More importantly, for many coastal American cities, it's simply not true that people pay a lot to live in the suburbs.  Even in the inflationary eras of the 1980s, a standalone single-family house in an area where most neighbors are rich and value education is more investment than expense (or was when they bought the house.  Who knows whether it will be in the future). I don't have good answers for the commuting sucks and density correlates with productivity arguments, except that revealed preference seems to contradict those as being the most important things.  Also, the measurements I've seen seem to include a range of circumstances that make it hard to separate the actual motivations.  Living by choice in "the nice" suburbs is likely a very different experience with different desirability than living in a cheap apartment with a long commute because you can't afford to live in the city.  I'd be interested to see same-age, same-family-situation, similar wealth comparisons of city and suburb dwellers.   
[-]Adam Shai155

I can report my own feelings with regards to this. I find cities (at least the American cities I have experience with) to be spiritually fatiguing. The constant sounds, the lack of anything natural, the smells - they all contribute to a lack of mental openness and quiet inside of myself.

The older I get the more I feel this.

Jefferson had a quote that might be related, though to be honest I'm not exactly sure what he was getting at:
 

I think our governments will remain virtuous for many centuries; as long as they are chiefly agricultural; and this will be as long as there shall be vacant lands in any part of America. When they get piled upon one another in large cities, as in Europe, they will become corrupt as in Europe. Above all things I hope the education of the common people will be attended to; convinced that on their good sense we may rely with the most security for the preservation of a due degree of liberty.

One interpretation of this is that Jefferson thought there was something spiritually corrupting of cities. This supported by another quote:
 


I view great cities as pestilential to the morals, the health and the liberties of man. true, they nourish some of the eleg

... (read more)
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[-]Alexander Gietelink Oldenziel13820

My mainline prediction scenario for the next decades.

My mainline prediction * :

  • LLMs will not scale to AGI. They will not spawn evil gremlins or mesa-optimizers. BUT Scaling laws will continue to hold and future LLMs will be very impressive and make a sizable impact on the real economy and science over the next decade. EDIT: since there is a lot of confusion about this point. BY LLM I mean the paradigm of pre-trained transformers. This does not include different paradigms that follow pre-trained transformers but are still called large language models.  EDIT2: since I'm already anticipating confusion on this point: when I say scaling laws will continue to hold that means that the 3-way relation between model size, compute, data will probably continue to hold. It has been known for a long time that amount of data used by gpt-4 level models is already within perhaps an OOM of the maximum. ]
  • there is a single innovation left to make AGI-in-the-alex sense work, i.e. coherent, long-term planning agents (LTPA) that are effective and efficient in data sparse domains over long horizons.
  • that innovation will be found within the next 10-15 years
  • It will be clear to the general public that t
... (read more)
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1lemonhope
So somebody gets an agent which efficiently productively indefinitely works on any specified goal, then they just let the government find out and take it? No countermeasures?
[-]Steven Byrnes5540

governments will act quickly and (relativiely) decisively to  bring these agents under state-control. national security concerns will dominate.

I dunno, like 20 years ago if someone had said “By the time somebody creates AI that displays common-sense reasoning, passes practically any written test up including graduate-level,  (etc.), obviously governments will be flipping out and nationalizing AI companies etc.”, to me that would have seemed like a reasonable claim. But here we are, and the idea of the USA govt nationalizing OpenAI seems a million miles outside the Overton window.

Likewise, if someone said “After it becomes clear to everyone that lab leaks can cause pandemics costing trillions of dollars and millions of lives, then obviously governments will be flipping out and banning the study of dangerous viruses—or at least, passing stringent regulations with intrusive monitoring and felony penalties for noncompliance etc,” then that would also have sounded reasonable to me! But again, here we are.

So anyway, my conclusion is that when I ask my intuition / imagination whether governments will flip out in thus-and-such circumstance, my intuition / imagination is really ba... (read more)

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2Lucius Bushnaq
Registering that it does not seem that far out the Overton window to me anymore. My own advance prediction of how much governments would be flipping out around this capability level has certainly been proven a big underestimate.   
5ryan_greenblatt
One strong reason to think the AI case might be different is that US national security will be actively using AI to build weapons and thus it will be relatively clear and salient to US national security when things get scary.
8Steven Byrnes
For one thing, COVID-19 obviously had impacts on military readiness and operations, but I think that fact had very marginal effects on pandemic prevention. For another thing, I feel like there’s a normal playbook for new weapons-development technology, which is that the military says “Ooh sign me up”, and (in the case of the USA) the military will start using the tech in-house (e.g. at NRL) and they’ll also send out military contracts to develop the tech and apply it to the military. Those contracts are often won by traditional contractors like Raytheon, but in some cases tech companies might bid as well. I can’t think of precedents where a tech was in wide use by the private sector but then brought under tight military control in the USA. Can you? The closest things I can think of is secrecy orders (the US military gets to look at every newly-approved US patent and they can choose to declare them to be military secrets) and ITAR (the US military can declare that some area of tech development, e.g. certain types of high-quality IR detectors that are useful for night vision and targeting, can’t be freely exported, nor can their schematics etc. be shared with non-US citizens). Like, I presume there are lots of non-US-citizens who work for OpenAI. If the US military were to turn OpenAI’s ongoing projects into classified programs (for example), those non-US employees wouldn’t qualify for security clearances. So that would basically destroy OpenAI rather than control it (and of course the non-USA staff would bring their expertise elsewhere). Similarly, if the military was regularly putting secrecy orders on OpenAI’s patents, then OpenAI would obviously respond by applying for fewer patents, and instead keeping things as trade secrets which have no normal avenue for military review. By the way, fun fact: if some technology or knowledge X is classified, but X is also known outside a classified setting, the military deals with that in a very strange way: people with cl
2johnvon
'when things get scary' when then? 
1James Anthony
What "coherent, long-term planning agents" means, and what is possible with these agents, is not clear to me. How would they overcome lack of access to knowledge, as was highlighted by F.A. Hayek in "The Use of Knowledge in Society"? What actions would they plan? How would their planning come to replace humans' actions? (Achieving control over some sectors of battlefields would only be controlling destruction, of course, it would not be controlling creation.)  Some discussion is needed that recognizes and takes into account differences among governance structures. What seems the most relevant to me are these cases: (1) totalitarian governments, (2) somewhat-free governments, (3) transnational corporations, (4) decentralized initiatives. This is a new kind of competition, but the results will be like with major wars: Resilient-enough groups will survive the first wave or new groups will re-form later, and ultimately the competition will be won by the group that outproduces the others. In each successive era, the group that outproduces the others will be the group that leaves people the freest. 
[-]Daniel Murfet3830

I think this will look a bit outdated in 6-12 months, when there is no longer a clear distinction between LLMs and short term planning agents, and the distinction between the latter and LTPAs looks like a scale difference comparable to GPT2 vs GPT3 rather than a difference in kind.  At what point do you imagine a national government saying "here but no further?".

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1cubefox
So you are predicting that within 6-12 months, there will no longer be a clear distinction between LLMs and "short term planning agents". Do you mean that agentic LLM scaffolding like Auto-GPT will qualify as such?
[-]Daniel Murfet150

I think scaffolding is the wrong metaphor. Sequences of actions, observations and rewards are just more tokens to be modeled, and if I were running Google I would be busy instructing all work units to start packaging up such sequences of tokens to feed into the training runs for Gemini models. Many seemingly minor tasks (e.g. app recommendation in the Play store) either have, or could have, components of RL built into the pipeline, and could benefit from incorporating LLMs, either by putting the RL task in-context or through fine-tuning of very fast cheap models.

So when I say I don't see a distinction between LLMs and "short term planning agents" I mean that we already know how to subsume RL tasks into next token prediction, and so there is in some technical sense already no distinction. It's a question of how the underlying capabilities are packaged and deployed, and I think that within 6-12 months there will be many internal deployments of LLMs doing short sequences of tasks within Google. If that works, then it seems very natural to just scale up sequence length as generalisation improves.

Arguably fine-tuning a next-token predictor on action, observation, reward sequences, or doing it in-context, is inferior to using algorithms like PPO. However, the advantage of knowledge transfer from the rest of the next-token predictor's data distribution may more than compensate for this on some short-term tasks.

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2Noosphere89
I think o1 is a partial realization of your thesis, and the only reason it's not more successful is because the compute used for GPT-o1 and GPT-4o were essentially the same: https://www.lesswrong.com/posts/bhY5aE4MtwpGf3LCo/openai-o1 And yeah, the search part was actually quite good, if a bit modest in it's gains.
8Alexander Gietelink Oldenziel
 As far as I can tell Strawberry is proving me right: it's going beyond pre-training and scales inference - the obvious next step.  A lot of people said just scaling pre-trained transformers would scale to AGI. I think that's silly and doesn't make sense. But now you don't have to believe me - you can just use OpenAIs latest model.  The next step is to do efficient long-horizon RL for data-sparse domains.   Strawberry working suggest that this might not be so hard. Don't be fooled by the modest gains of Strawberry so far. This is a new paradigm that is heading us toward true AGI and superintelligence. 
8Daniel Murfet
Yeah actually Alexander and I talked about that briefly this morning. I agree that the crux is "does this basic kind of thing work" and given that the answer appears to be "yes" we can confidently expect scale (in both pre-training and inference compute) to deliver significant gains. I'd love to understand better how the RL training for CoT changes the representations learned during pre-training. 
[-]Alexander Gietelink Oldenziel125

in my reading, Strawberry is showing that indeed scaling just pretraining transformers will *not* lead to AGI. The new paradigm is inference-scaling - the obvious next step is doing RL on long horizons and sparse data domains. I have been saying this ever since gpt-3 came out. 

For the question of general intelligence imho the scaling is conceptually a red herring: any (general purpose) algorithm will do better when scaled. The key in my mind is the algorithm not the resource, just like I would say a child is generally intelligent while a pocket calculator is not even if the child can't count to 20 yet. It's about the meta-capability to learn not the capability. 

As we spoke earlier - it was predictable that this was going to be the next step. It was likely it was going to work, but there was a hopeful world in which doing the obvious thing turned out to be harder. That hope has been dashed - it suggests longer horizons might be easy too. This means superintelligence within two years is not out of the question. 

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2Noosphere89
We have been shown that this search algorithm works, and we not yet have been shown that the other approaches don't work. Remember, technological development is disjunctive, and just because you've shown that 1 approach works, doesn't mean that we have been shown that only that approach works. Of course, people will absolutely try to scale this one up now that they found success, and I think that timelines have definitely been shortened, but remember that AI progress is closer to a disjunctive scenario than conjunctive scenario: I agree with this quote below, but I wanted to point out the disjunctiveness of AI progress: https://gwern.net/forking-path
4Alexander Gietelink Oldenziel
strong disagree. i would be highly surprised if there were multiple essentially different algorithms to achieve general intelligence*.  I also agree with the Daniel Murfet's quote. There is a difference between a disjunction before you see the data and a disjunction after you see the data. I agree AI development is disjunctive before you see the data - but in hindsight all the things that work are really minor variants on a single thing that works.  *of course "essentially different" is doing a lot of work here. some of the conceptual foundations of intelligence haven't been worked out enough (or Vanessa has and I don't understand it yet) for me to make a formal statement here. 
2Noosphere89
Re different algorithms, I actually agree with both you and Daniel Murfet in that conditional on non-reversible computers, there is at most 1-3 algorithms to achieve intelligence that can scale arbitrarily large, and I'm closer to 1 than 3 here. But once reversible computers/superconducting wires are allowed, all bets are off on how many algorithms are allowed, because you can have far, far more computation with far, far less waste heat leaving, and a lot of the design of computers is due to heat requirements.
2Alexander Gietelink Oldenziel
Reversible computing and superconducting wires seem like hardware innovations. You are saying that this will actually materially change the nature of the algorithm you'd want to run? I'd bet against. I'd be surprised if this was the case. As far as I can tell everything we have so seen so far points to a common simple core of general intelligence algorithm (basically an open-loop RL algorithm on top of a pre-trained transformers). I'd be surprised if there were materially different ways to do this. One of the main takeaways of the last decade of deep learning process is just how little architecture matters - it's almost all data and compute (plus I claim one extra ingredient, open-loop RL that is efficient on long horizons and sparse data novel domains) I don't know for certain of course. If I look at theoretical CS though the universality of computation makes me skeptical of radically different algorithms. 
6Seth Herd
Agreed on all points except a couple of the less consequential, where I don't disagree. Strongest agreement: we're underestimating the importance of governments for alignment and use/misuse. We haven't fully updated from the inattentive world hypothesis. Governments will notice the importance of AGI before it's developed, and will seize control. They don't need to nationalize the corporations, they just need to have a few people embedded at theh company and demand on threat of imprisonment that they're kept involved with all consequential decisions on its use. I doubt they'd even need new laws, because the national security implications are enormous. But if they need new laws, they'll create them as rapidly as necessary. Hopping borders will be difficult, and just put a different government in control. Strongest disagreement: I think it's likely that zero breakthroughs are needed to add long term planning capabilities to LLM-based systems, and so long term planning agents (I like the terminology) will be present very soon, and  improve as LLMs continue to improve. I have specific reasons for thinking this. I could easily be wrong, but I'm pretty sure that the rational stance is "maybe". This maybe advances the timelines dramatically. Also strongly agree on AGI as a relatively discontinuous improvement; I worry that this is glossed over in modern "AI safety" discussions, causing people to mistake controlling LLMs for aligning the AGIs we'll create on top of them. AGI alignment requires different conceptual work.
[-]Thomas Kwa138

I think a single innovation left to create LTPA is unlikely because it runs contrary to the history of technology and of machine learning. For example, in the 10 years before AlphaGo and before GPT-4, several different innovations were required-- and that's if you count "deep learning" as one item. ChatGPT actually understates the number here because different components of the transformer architecture like attention, residual streams, and transformer++ innovations were all developed separately. 

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2Alexander Gietelink Oldenziel
I mostly regard LLMs = [scaling a feedforward network on large numbers of GPUs and data] as a single innovation.
[-]Thomas Kwa2922

Then I think you should specify that progress within this single innovation could be continuous over years and include 10+ ML papers in sequence each developing some sub-innovation.

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2Garrett Baker
Do you think the final big advance happens within or with-out labs?
4Alexander Gietelink Oldenziel
Probably within.
[-]ryan_greenblatt213

I'm a bit confused by what you mean by "LLMs will not scale to AGI" in combination with "a single innovation is all that is needed for AGI".

E.g., consider the following scenarios:

  • AGI (in the sense you mean) is achieved by figuring out a somewhat better RL scheme and massively scaling this up on GPT-6.
  • AGI is achieved by doing some sort of architectural hack on top of GPT-6 which makes it able to reason in neuralese for longer and then doing a bunch of training to teach the model to use this well.
  • AGI is achieved via doing some sort of iterative RL/synth data/self-improvement process for GPT-6 in which GPT-6 generates vast amounts of synthetic data for itself using various tools.

IMO, these sound very similar to "LLMs scale to AGI" for many practical purposes:

  • LLM scaling is required for AGI
  • LLM scaling drives the innovation required for AGI
  • From the public's perspective, it maybe just looks like AI is driven by LLMs getting better over time and various tweaks might be continuously introduced.

Maybe it is really key in your view that the single innovation is really discontinuous and maybe the single innovation doesn't really require LLM scaling.

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[-]Alexander Gietelink Oldenziel134

Why no prediction markets for large infrastructure projects?

Been reading this excellent piece on why prediction markets aren't popular. They say that without subsidies prediction markets won't be large enough; the information value of prediction markets is often nog high enough. 

Large infrastructure projects undertaken by governments, and other large actors often go overbudget, often hilariously so: 3x,5x,10x or more is not uncommon, indeed often even the standard.

One of the reasons is that government officials deciding on billion dollar infrastructure projects don't have enough skin in the game. Politicians are often not long enough in office to care on the time horizons of large infrastructure projects. Contractors don't gain by being efficient or delivering on time. To the contrary, infrastructure projects are huge cashcows. Another problem is that there are often far too many veto-stakeholders. All too often the initial bid is wildly overoptimistic. 

Similar considerations apply to other government projects like defense procurement or IT projects.

Okay - how to remedy this situation? Internal prediction markets theoretically could prove beneficial. All stakeholders &... (read more)

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2Jeremy Gillen
Doesn't the futarchy hack come up here? Contractors will be betting that competitors timelines and cost will be high, in order to get the contract. 
8Carl Feynman
The standard reply is that investors who know or suspect that the market is being systematically distorted will enter the market on the other side, expecting to profit from the distortion. Empirically, attempts to deliberately sway markets in desired directions don’t last very long.
[-]Alexander Gietelink Oldenziel190

Problem of Old Evidence, the Paradox of Ignorance and Shapley Values

Paradox of Ignorance

Paul Christiano presents the "paradox of ignorance" where a weaker, less informed agent appears to outperform a more powerful, more informed agent in certain situations. This seems to contradict the intuitive desideratum that more information should always lead to better performance.

The example given is of two agents, one powerful and one limited, trying to determine the truth of a universal statement ∀x:ϕ(x) for some Δ0 formula ϕ. The limited agent treats each new value of ϕ(x) as a surprise and evidence about the generalization ∀x:ϕ(x). So it can query the environment about some simple inputs x and get a reasonable view of the universal generalization.

In contrast, the more powerful agent may be able to deduce ϕ(x) directly for simple x. Because it assigns these statements prior probability 1, they don't act as evidence at all about the universal generalization ∀x:ϕ(x). So the powerful agent must consult the environment about more complex examples and pay a higher cost to form reasonable beliefs about the generalization.

Is it really a problem?

However, I argue that the more powerful agent is act... (read more)

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6abramdemski
The matter seems terribly complex and interesting to me. Notions of Accuracy? Suppose p1 is a prior which has uncertainty about ϕ(x1),ϕ(x2),... and uncertainty about ∀nϕ(xn). This is the more ignorant prior. Consider p2 some prior which has the same beliefs about the universal statement -- p1(∀nϕ(xn))=p2(∀nϕ(xn)) -- but which knows ϕ(x1) and ϕ(x2). We observe that p1 can increase its credence in the universal statement by observing the first two instances, ϕ(x1) and ϕ(x2), while p2 cannot do this -- p2 needs to wait for further evidence. This is interpreted as a defect. The moral is apparently that a less ignorant prior can be worse than a more ignorant one; more specifically, it can learn more slowly. However, I think we need to be careful about the informal notion of "more ignorant" at play here. We can formalize this by imagining a numerical measure of the accuracy of a prior. We might want it to be the case that more accurate priors are always better to start with. Put more precisely: a more accurate prior should also imply a more accurate posterior after updating. Paul's example challenges this notion, but he does not prove that no plausible notion of accuracy will have this property; he only relies on an informal notion of ignorance. So I think the question is open: when can a notion of accuracy fail to follow the rule "more accurate priors yield more accurate posteriors"? EG, can a proper scoring rule fail to meet this criterion? This question might be pretty easy to investigate. Conditional probabilities also change? I think the example rests on an intuitive notion that we can construct p2 by imagining p1 but modifying it to know ϕ(x1) and ϕ(x2). However, the most obvious way to modify it so is by updating on those sentences. This fails to meet the conditions of the example, however; p2 would already have an increased probability for the universal statement. So, in order to move the probability of ϕ(x1) and ϕ(x2) upwards to 1 without also increasi
4abramdemski
(continued..) Explanations? Alexander analyzes the difference between p1 and p2 in terms of the famous "explaining away" effect. Alexander supposes that p2 has learned some "causes": Postulating these causes adds something to the scenario. One possible view is that Alexander is correct so far as Alexander's argument goes, but incorrect if there are no such Cj to consider. However, I do not find myself endorsing Alexander's argument even that far. If C1 and C2 have a common form, or are correlated in some way -- so there is an explanation which tells us why the first two sentences, ϕ(x1) and ϕ(x2), are true, and which does not apply to n>2 -- then I agree with Alexander's argument. If C1 and C2 are uncorrelated, then it starts to look like a coincidence. If I find a similarly uncorrelated C3 for ϕ(x3), C4 for ϕ(x4), and a few more, then it will feel positively unexplained. Although each explanation is individually satisfying, nowhere do I have an explanation of why all of them are turning up true. I think the probability of the universal sentence should go up at this point. So, what about my "conditional probabilities also change" variant of Alexander's argument? We might intuitively think that ϕ(x1) and ϕ(x2) should be evidence for the universal generalization, but p2 does not believe this -- its conditional probabilities indicate otherwise.  I find this ultimately unconvincing because the point of Paul's example, in my view, is that more accurate priors do not imply more accurate posteriors. I still want to understand what conditions can lead to this (including whether it is true for all notions of "accuracy" satisfying some reasonable assumptions EG proper scoring rules). Another reason I find it unconvincing is because even if we accepted this answer for the paradox of ignorance, I think it is not at all convincing for the problem of old evidence.  What is the 'problem' in the problem of old evidence? ... to be further expanded later ...
1cubefox
This post sounds intriguing, but is largely incomprehensible to me due to not sufficiently explaining the background theories.
3kromem
It's funny that this has been recently shown in a paper. I've been thinking a lot about this phenomenon regarding fields with little to no capacity for testable predictions like history. I got very into history over the last few years, and found there was a significant advantage to being unknowledgeable that was not available to the knowledged, and it was exactly what this paper is talking about. By not knowing anything, I could entertain multiple bizarre ideas without immediately thinking "but no, that doesn't make sense because of X." And then, each of those ideas becomes in effect its own testable prediction. If there's something to it, as I learn more about the topic I'm going to see significantly more samples of indications it could be true and few convincing to the contrary. But if it probably isn't accurate, I'll see few supporting samples and likely a number of counterfactual examples. You kind of get to throw everything at the wall and see what sticks over time. In particular, I found that it was especially powerful at identifying clustering trends in cross-discipline emerging research in things that were testable, such as archeological finds and DNA results, all within just the past decade, which despite being relevant to the field of textual history is still largely ignored in the face of consensus built on conviction. It reminds me a lot of science historian John Helibron's quote, "The myth you slay today may contain a truth you need tomorrow." If you haven't had the chance to slay any myths, you also haven't preemptively killed off any truths along with it.
[-]gwern1410

One of the interesting thing about AI minds (such as LLMs) is that in theory, you can turn many topics into testable science while avoiding the 'problem of old evidence', because you can now construct artificial minds and mold them like putty. They know what you want them to know, and so you can see what they would predict in the absence of knowledge, or you can install in them false beliefs to test out counterfactual intellectual histories, or you can expose them to real evidence in different orders to measure biases or path dependency in reasoning.

With humans, you can't do that because they are so uncontrolled: even if someone says they didn't know about crucial piece of evidence X, there is no way for them to prove that, and they may be honestly mistaken and have already read about X and forgotten it (but humans never really forget so X has already changed their "priors", leading to double-counting), or there is leakage. And you can't get people to really believe things at the drop of a hat, so you can't make people imagine, "suppose Napoleon had won Waterloo, how do you predict history would have changed?" because no matter how you try to participate in the spirit of the exerci... (read more)

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1kromem
While I agree that the potential for AI (we probably need a better term than LLMs or transformers as multimodal models with evolving architectures grow beyond those terms) in exploring less testable topics as more testable is quite high, I'm not sure the air gapping on information can be as clean as you might hope. Does the AI generating the stories of Napoleon's victory know about the historical reality of Waterloo? Is it using something like SynthID where the other AI might inadvertently pick up on a pattern across the stories of victories distinct from the stories preceding it? You end up with a turtles all the way down scenario in trying to control for information leakage with the hopes of achieving a threshold that no longer has impact on the result, but given we're probably already seriously underestimating the degree to which correlations are mapped even in today's models I don't have high hopes for tomorrow's. I think the way in which there's most impact on fields like history is the property by which truth clusters across associated samples whereas fictions have counterfactual clusters. An AI mind that is not inhibited by specialization blindness or the rule of seven plus or minus two and better trained at correcting for analytical biases may be able to see patterns in the data, particularly cross-domain, that have eluded human academics to date (this has been my personal research interest in the area, and it does seem like there's significant room for improvement). And yes, we certainly could be. If you're a fan of cosmology at all, I've been following Neil Turok's CPT symmetric universe theory closely, which started with the Baryonic asymmetry problem and has tackled a number of the open cosmology questions since. That, paired with a QM interpretation like Everett's ends up starting to look like the symmetric universe is our reference and the MWI branches are variations of its modeling around quantization uncertainties. (I've found myself thinking of
2Alexander Gietelink Oldenziel
Beautifully illustrated and amusingly put, sir! A variant of what you are saying is that AI may once and for all allow us to calculate the true counterfactual     Shapley value of scientific contributions. ( re: ancestor simulations I think you are onto something here. Compare the Q hypothesis:     https://twitter.com/dalcy_me/status/1780571900957339771 see also speculations about Zhuangzi hypothesis here  )
3gwern
Yup. Who knows but we are all part of a giant leave-one-out cross-validation computing counterfactual credit assignment on human history? Schmidhuber-em will be crushed by the results.
6Jeremy Gillen
This doesn't feel like it resolves that confusion for me, I think it's still a problem with the agents he describes in that paper. The causes Cj are just the direct computation of Φ for small values of x. If they were arguments that only had bearing on small values of x and implied nothing about larger values (e.g. an adversary selected some x to show you, but filtered for x such that Φ(x)), then it makes sense that this evidence has no bearing on∀x:Φ(x). But when there was no selection or other reason that the argument only applies to small x, then to me it feels like the existence of the evidence (even though already proven/computed) should still increase the credence of the forall.
4Alexander Gietelink Oldenziel
I didn't intend the causes Cj to equate to direct computation of \phi(x) on the x_i. They are rather other pieces of evidence that the powerful agent has that make it believe \phi(x_i). I don't know if that's what you meant. I agree seeing x_i such that \phi(x_i) should increase credence in \forall x \phi(x) even in the presence of knowledge of C_j. And the Shapely value proposal will do so. (Bad tex. On my phone)
[-]Alexander Gietelink Oldenziel296

My timelines are lengthening. 

I've long been a skeptic of scaling LLMs to AGI *. To me I fundamentally don't understand how this is even possible. It must be said that very smart people give this view credence. davidad, dmurfet. on the other side are vanessa kosoy and steven byrnes. When pushed proponents don't actually defend the position that a large enough transformer will create nanotech or even obsolete their job. They usually mumble something about scaffolding.

I won't get into this debate here but I do want to note that my timelines have lengthened, primarily because some of the never-clearly-stated but heavily implied AI developments by proponents of very short timelines have not materialized. To be clear, it has only been a year since gpt-4 is released, and gpt-5 is around the corner, so perhaps my hope is premature. Still my timelines are lengthening. 

A year ago, when gpt-3 came out progress was blindingly fast. Part of short timelines came from a sense of 'if we got surprised so hard by gpt2-3, we are completely uncalibrated, who knows what comes next'.

People seemed surprised by gpt-4 in a way that seemed uncalibrated to me. gpt-4 performance was basically in li... (read more)

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3Stephen McAleese
State-of-the-art models such as Gemini aren't LLMs anymore. They are natively multimodal or omni-modal transformer models that can process text, images, speech and video. These models seem to me like a huge jump in capabilities over text-only LLMs like GPT-3.
4DanielFilan
Links to Dan Murfet's AXRP interview: * Transcript * Video
5zeshen
Agreed. I'm also pleasantly surprised that your take isn't heavily downvoted.
4Daniel Murfet
I don't recall what I said in the interview about your beliefs, but what I meant to say was something like what you just said in this post, apologies for missing the mark.
0Daniel Murfet
Mumble.
3Stephen McAleese
Chain-of-thought prompting makes models much more capable. In the original paper "Chain-of-Thought Prompting Elicits Reasoning in Large Language Models", PaLM 540B with standard prompting only solves 18% of problems but 57% of problems with chain-of-thought prompting. I expect the use of agent features such as reflection will lead to similar large increases in capabilities as well in the near future.
6Alexander Gietelink Oldenziel
Those numbers don't really accord with my experience actually using gpt-4. Generic prompting techniques just don't help all that much.
5Stephen McAleese
I just asked GPT-4 a GSM8K problem and I agree with your point. I think what's happening is that GPT-4 has been fine-tuned to respond with chain-of-thought reasoning by default so it's no longer necessary to explicitly ask it to reason step-by-step. Though if you ask it to "respond with just a single number" to eliminate the chain-of-thought reasoning it's problem-solving ability is much worse.
6Adam Shai
Lengthening from what to what?
4Alexander Gietelink Oldenziel
I've never done explicit timelines estimates before so nothing to compare to. But since it's a gut feeling anyway, I'm saying my gut is lengthening.
8faul_sname
Can you expand on what you mean by "create nanotech?" If improvements to our current photolithography techniques count, I would not be surprised if (scaffolded) LLMs could be useful for that. Likewise for getting bacteria to express polypeptide catalysts for useful reactions, and even maybe figure out how to chain several novel catalysts together to produce something useful (again, referring to scaffolded LLMs with access to tools). If you mean that LLMs won't be able to bootstrap from our current "nanotech only exists in biological systems and chip fabs" world to Drexler-style nanofactories, I agree with that, but I expect things will get crazy enough that I can't predict them long before nanofactories are a thing (if they ever are). Likewise, I don't think LLMs can immediately obsolete all of the parts of my job. But they sure do make parts of my job a lot easier. If you have 100 workers that each spend 90% of their time on one specific task, and you automate that task, that's approximately as useful as fully automating the jobs of 90 workers. "Human-equivalent" is one of those really leaky abstractions -- I would be pretty surprised if the world had any significant resemblance to the world of today by the time robotic systems approached the dexterity and sensitivity of human hands for all of the tasks we use our hands for, whereas for the task of "lift heavy stuff" or "go really fast" machines left us in the dust long ago. Iterative improvements on the timescale we're likely to see are still likely to be pretty crazy by historical standards. But yeah, if your timelines were "end of the world by 2026" I can see why they'd be lengthening now.
2Alexander Gietelink Oldenziel
My timelines were not 2026. In fact, I made bets against doomers 2-3 years ago, one will resolve by next year. I agree iterative improvements are significant. This falls under "naive extrapolation of scaling laws". By nanotech I mean something akin to drexlerian nanotech or something similarly transformative in the vicinity. I think it is plausible that a true ASI will be able to make rapid progress (perhaps on the order of a few years or a decade) on nanotech. I suspect that people that don't take this as a serious possibility haven't really thought through what AGI/ASI means + what the limits and drivers of science and tech really are; I suspect they are simply falling prey to status-quo bias.
[-]Vladimir_Nesov102

With scale, there is visible improvement in difficulty of novel-to-chatbot ideas/details that is possible to explain in-context, things like issues with the code it's writing. If a chatbot is below some threshold of situational awareness of a task, no scaffolding can keep it on track, but for a better chatbot trivial scaffolding might suffice. Many people can't google for a solution to a technical issue, the difference between them and those who can is often subtle.

So modest amount of scaling alone seems plausibly sufficient for making chatbots that can do whole jobs almost autonomously. If this works, 1-2 OOMs more of scaling becomes both economically feasible and more likely to be worthwhile. LLMs think much faster, so they only need to be barely smart enough to help with clearing those remaining roadblocks.

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2Alexander Gietelink Oldenziel
You may be right. I don't know of course.  At this moment in time, it seems scaffolding tricks haven't really improved the baseline performance of models that much. Overwhelmingly, the capability comes down to whether the rlfhed base model can do the task.
4Vladimir_Nesov
That's what I'm also saying above (in case you are stating what you see as a point of disagreement). This is consistent with scaling-only short timeline expectations. The crux for this model is current chatbots being already close to autonomous agency and to becoming barely smart enough to help with AI research. Not them directly reaching superintelligence or having any more room for scaling.
[-]Alexander Gietelink Oldenziel10-2

Yes agreed.

What I don't get about this position: If it was indeed just scaling - what's AI research for ? There is nothing to discover, just scale more compute. Sure you can maybe improve the speed of deploying compute a little but at the core of it it seems like a story that's in conflict with itself?

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5Nathan Helm-Burger
My view is that there's huge algorithmic gains in peak capability, training efficiency (less data, less compute), and inference efficiency waiting to be discovered, and available to be found by a large number of parallel research hours invested by a minimally competent multimodal LLM powered research team. So it's not that scaling leads to ASI directly, it's: 1. scaling leads to brute forcing the LLM agent across the threshold of AI research usefulness 2. Using these LLM agents in a large research project can lead to rapidly finding better ML algorithms and architectures. 3. Training these newly discovered architectures at large scales leads to much more competent automated researchers. 4. This process repeats quickly over a few months or years. 5. This process results in AGI. 6. AGI, if instructed (or allowed, if it's agentically motivated on its own to do so) to improve itself will find even better architectures and algorithms. 7. This process can repeat until ASI. The resulting intelligence / capability / inference speed goes far beyond that of humans.  Note that this process isn't inevitable, there are many points along the way where humans can (and should, in my opinion) intervene. We aren't disempowered until near the end of this.
4Alexander Gietelink Oldenziel
Why do you think there are these low-hanging algorithmic improvements?
[-]Carl Feynman130

Here are two arguments for low-hanging algorithmic improvements.

First, in the past few years I have read many papers containing low-hanging algorithmic improvements.  Most such improvements are a few percent or tens of percent.  The largest such improvements are things like transformers or mixture of experts, which are substantial steps forward.  Such a trend is not guaranteed to persist, but that’s the way to bet.

Second, existing models are far less sample-efficient than humans.  We receive about a billion tokens growing to adulthood.  The leading LLMs get orders of magnitude more than that.  We should be able to do much better.  Of course, there’s no guarantee that such an improvement is “low hanging”.  

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3Vladimir_Nesov
Capturing this would probably be a big deal, but a counterpoint is that compute necessary to achieve an autonomous researcher using such sample efficient method might still be very large. Possibly so large that training an LLM with the same compute and current sample-inefficient methods is already sufficient to get a similarly effective autonomous researcher chatbot. In which case there is no effect on timelines. And given that the amount of data is not an imminent constraint on scaling, the possibility of this sample efficiency improvement being useless for the human-led stage of AI development won't be ruled out for some time yet.
2Alexander Gietelink Oldenziel
Could you train an LLM on pre 2014 Go games that could beat AlphaZero? I rest my case.
2Vladimir_Nesov
The best method of improving sample efficiency might be more like AlphaZero. The simplest method that's more likely to be discovered might be more like training on the same data over and over with diminishing returns. Since we are talking low-hanging fruit, I think it's reasonable that first forays into significantly improved sample efficiency with respect to real data are not yet much better than simply using more unique real data.
2Alexander Gietelink Oldenziel
I would be genuinely surprised if training a transformer on the pre2014 human Go data over and over would lead it to spontaneously develop alphaZero capacity. I would expect it to do what it is trained to: emulate / predict as best as possible the distribution of human play. To some degree I would anticipate the transformer might develop some emergent ability that might make it slightly better than Go-Magnus - as we've seen in other cases - but I'd be surprised if this would be unbounded. This is simply not what the training signal is.
2Vladimir_Nesov
We start with an LLM trained on 50T tokens of real data, however capable it ends up being, and ask how to reach the same level of capability with synthetic data. If it takes more than 50T tokens of synthetic data, then it was less valuable per token than real data. But at the same time, 500T tokens of synthetic data might train an LLM more capable than if trained on the 50T tokens of real data for 10 epochs. In that case, synthetic data helps with scaling capabilities beyond what real data enables, even though it's still less valuable per token. With Go, we might just be running into the contingent fact of there not being enough real data to be worth talking about, compared with LLM data for general intelligence. If we run out of real data before some threshold of usefulness, synthetic data becomes crucial (which is the case with Go). It's unclear if this is the case for general intelligence with LLMs, but if it is, then there won't be enough compute to improve the situation unless synthetic data also becomes better per token, and not merely mitigates the data bottleneck and enables further improvement given unbounded compute. I expect that if we could magically sample much more pre-2014 unique human Go data than was actually generated by actual humans (rather than repeating the limited data we have), from the same platonic source and without changing the level of play, then it would be possible to cheaply tune an LLM trained on it to play superhuman Go.
4Alexander Gietelink Oldenziel
I don't know what you mean by 'general intelligence' exactly but I suspect you mean something like human+ capability in a broad range of domains. I agree LLMs will become generally intelligent in this sense when scaled, arguably even are, for domains with sufficient data. But that's kind of the sticker right? Cave men didn't have the whole internet to learn from yet somehow did something that not even you seem to claim LLMs will be able to do: create the (date of the) Internet. (Your last claim seems surprising. Pre-2014 games don't have close to the ELO of alphaZero. So a next-token would be trained to simulate a human player up tot 2800, not 3200+. )
4Vladimir_Nesov
Models can be thought of as repositories of features rather than token predictors. A single human player knows some things, but a sufficiently trained model knows all the things that any of the players know. Appropriately tuned, a model might be able to tap into this collective knowledge to a greater degree than any single human player. Once the features are known, tuning and in-context learning that elicit their use are very sample efficient. This framing seems crucial for expecting LLMs to reach researcher level of capability given a realistic amount of data, since most humans are not researchers, and don't all specialize in the same problem. The things researcher LLMs would need to succeed in learning are cognitive skills, so that in-context performance gets very good at responding to novel engineering and research agendas only seen in-context (or a certain easier feat that I won't explicitly elaborate on). Possibly the explanation for the Sapient Paradox, that prehistoric humans managed to spend on the order of 100,000 years without developing civilization, is that they lacked cultural knowledge of crucial general cognitive skills. Sample efficiency of the brain enabled their fixation in language across cultures and generations, once they were eventually distilled, but it took quite a lot of time. Modern humans and LLMs start with all these skills already available in the data, though humans can more easily learn them. LLMs tuned to tap into all of these skills at the same time might be able to go a long way without an urgent need to distill new ones, merely iterating on novel engineering and scientific challenges, applying the same general cognitive skills over and over.
1Carl Feynman
When I brought up sample inefficiency, I was supporting Mr. Helm-Burger‘s statement that “there's huge algorithmic gains in …training efficiency (less data, less compute) … waiting to be discovered”.  You’re right of course that a reduction in training data will not necessarily reduce the amount of computation needed.  But once again, that’s the way to bet.
2Vladimir_Nesov
I'm ambivalent on this. If the analogy between improvement of sample efficiency and generation of synthetic data holds, synthetic data seems reasonably likely to be less valuable than real data (per token). In that case we'd be using all the real data we have anyway, which with repetition is sufficient for up to about $100 billion training runs (we are at $100 million right now). Without autonomous agency (not necessarily at researcher level) before that point, there won't be investment to go over that scale until much later, when hardware improves and the cost goes down.
4Nathan Helm-Burger
My answer to that is currently in the form of a detailed 2 hour lecture with a bibliography that has dozens of academic papers in it, which I only present to people that I'm quite confident aren't going to spread the details. It's a hard thing to discuss in detail without sharing capabilities thoughts. If I don't give details or cite sources, then... it's just, like, my opinion, man. So my unsupported opinion is all I have to offer publicly. If you'd like to bet on it, I'm open to showing my confidence in my opinion by betting that the world turns out how I expect it to.
4Vladimir_Nesov
The story involves phase changes. Just scaling is what's likely to be available to human developers in the short term (a few years), it's not enough for superintelligence. Autonomous agency secures funding for a bit more scaling. If this proves sufficient to get smart autonomous chatbots, they then provide speed to very quickly reach the more elusive AI research needed for superintelligence. It's not a little speed, it's a lot of speed, serial speedup of about 100x plus running in parallel. This is not as visible today, because current chatbots are not capable of doing useful work with serial depth, so the serial speedup is not in practice distinct from throughput and cost. But with actually useful chatbots it turns decades to years, software and theory from distant future become quickly available, non-software projects get to be designed in perfect detail faster than they can be assembled.
3Alexander Gietelink Oldenziel
In my mainline model there are only a few innovations needed, perhaps only a single big one to product an AGI which just like the Turing Machine sits at the top of the Chomsky Hierarchy will be basically the optimal architecture given resource constraints. There are probably some minor improvements todo with bridging the gap between theoretically optimal architecture and the actual architecture, or parts of the algorithm that can be indefinitely improved but with diminishing returns (these probably exist due to Levin and possibly.matrix.multiplication is one of these). On the whole I expect AI research to be very chunky. Indeed, we've seen that there was really just one big idea to all current AI progress: scaling, specifically scaling GPUs on maximally large undifferentiated datasets. There were some minor technical innovations needed to pull this off but on the whole that was the clinger. Of course, I don't know. Nobody knows. But I find this the most plausible guess based on what we know about intelligence, learning, theoretical computer science and science in general.
2Vladimir_Nesov
(Re: Difficult to Parse react on the other comment I was confused about relevance of your comment above on chunky innovations, and it seems to be making some point (for which what it actually says is an argument), but I can't figure out what it is. One clue was that it seems like you might be talking about innovations needed for superintelligence, while I was previously talking about possible absence of need for further innovations to reach autonomous researcher chatbots, an easier target. So I replied with formulating this distinction and some thoughts on the impact and conditions for reaching innovations of both kinds. Possibly the relevance of this was confusing in turn.)
2Vladimir_Nesov
There are two kinds of relevant hypothetical innovations: those that enable chatbot-led autonomous research, and those that enable superintelligence. It's plausible that there is no need for (more of) the former, so that mere scaling through human efforts will lead to such chatbots in a few years regardless. (I think it's essentially inevitable that there is currently enough compute that with appropriate innovations we can get such autonomous human-scale-genius chatbots, but it's unclear if these innovations are necessary or easy to discover.) If autonomous chatbots are still anything like current LLMs, they are very fast compared to humans, so they quickly discover remaining major innovations of both kinds. In principle, even if innovations that enable superintelligence (at scale feasible with human efforts in a few years) don't exist at all, extremely fast autonomous research and engineering still lead to superintelligence, because they greatly accelerate scaling. Physical infrastructure might start scaling really fast using pathways like macroscopic biotech even if drexlerian nanotech is too hard without superintelligence or impossible in principle. Drosophila biomass doubles every 2 days, small things can assemble into large things.
9Marcus Williams
Wasn't the surprising thing about GPT-4 that scaling laws did hold? Before this many people expected scaling laws to stop before such a high level of capabilities. It doesn't seem that crazy to think that a few more OOMs could be enough for greater than human intelligence. I'm not sure that many people predicted that we would have much faster than scaling law progress (at least until ~human intelligence AI can speed up research)? I think scaling laws are the extreme rate of progress which many people with short timelines worry about.
3Alexander Gietelink Oldenziel
To some degree yes, they were not guaranteed to hold. But by that point they held for over 10 OOMs iirc and there was no known reason they couldn't continue. This might be the particular twitter bubble I was in but people definitely predicted capabilities beyond simple extrapolation of scaling laws.
[-]Alexander Gietelink Oldenziel9-10

Wildlife Welfare Will Win

The long arc of history bend towards gentleness and compassion. Future generations will look with horror on factory farming. And already young people are following this moral thread to its logical conclusion; turning their eyes in disgust to mother nature, red in tooth and claw. Wildlife Welfare Done Right, compassion towards our pets followed to its forceful conclusion would entail the forced uploading of all higher animals, and judging by the memetic virulences of shrimp welfare to lower animals as well. 

Morality-upon-reflex... (read more)

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[-]Alexander Gietelink Oldenziel30

Does internal bargaining and geometric rationality explain ADHD & OCD?

Self- Rituals as Schelling loci for Self-control and OCD

Why do people engage in non-social Rituals 'self-rituals'? These are very common and can even become pathological (OCD). 

High-self control people seem to more often have OCD-like symptoms. 

One way to think about self-control is as a form of internal bargaining between internal subagents. From this perspective, Self-control, time-discounting can be seen as a resource. In the absence of self-control the superagent 
D... (read more)

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[-]quetzal_rainbow125

I feel like the whole "subagent" framework suffers from homunculus problem: we fail to explain behavior using the abstraction of coherent agent, so we move to the abstraction of multiple coherent agents, and while it can be useful, I don't think it displays actual mechanistic truth about minds.

When I plan something and then fail to execute plan it's mostly not like "failure to bargain". It's just when I plan something I usually have good consequences of plan in my imagination and this consequences make me excited and then I start plan execution and get hit by multiple unpleasant details of reality. Coherent structure emerges from multiple not-really-agentic pieces.

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2Alexander Gietelink Oldenziel
You are taking subagents too literally here. If you prefer take another word like shard, fragment, component, context-dependent action impulse generator etc
2quetzal_rainbow
When I read word "bargaining" I assume that we are talking about entities that have preferences, action set, have beliefs about relations between actions and preferences and exchange information (modulo acausal interaction) with other entities of the same composition. Like, Kelly betting is good because it equals to Nash bargaining between versions of yourself from inside different outcomes and this is good because we assume that you in different outcomes are, actually, agent with all arrtibutes of agentic system. Saying "systems consist of parts, this parts interact and sometimes result is a horrific incoherent mess" is true, but doesn't convey much of useful information.
[-]Alexander Gietelink Oldenziel40

Why (talk-)Therapy 

Therapy is a curious practice.  Therapy sounds like a scam, quackery, pseudo-science but it seems RCT consistently show therapy has benefits above and beyond medication & placebo. 

Therapy has a long history. The Dodo verdict states that it doesn't matter which form of therapy you do - they all work equally well. It follows that priests and shamans served the functions of a therapist.  In the past, one would confessed ones sins to a priest, or spoken with the local shaman. 

There is also the thing that therapy ... (read more)

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1lemonhope
I suspect that past therapists existed in your community and knew what you're actually like so were better able to give you actual true information instead of having to digest only your bullshit and search for truth nuggets in it. Furthermore, I suspect they didn't lose their bread when they solve your problem! We have a major incentive issue in the current arrangement!
2M. Y. Zuo
There's a market for lemons problem, similar to the used car market, where neither the therapist nor customer can detect all hidden problems, pitfalls, etc., ahead of time. And once you do spend enough time to actually form a reasonable estimate there's no takebacks possible. So all the actually quality therapists will have no availability and all the lower quality therapists will almost by definition be associated with those with availability. Edit: Game Theory suggests that you should never engage in therapy or at least never with someone with available time, at least until someone invents the certified pre-owned market.
2ChristianKl
That would be prediction-based medicine. It works in theory, it's just that someone would need to put it into practice. 
2Garrett Baker
This style of argument proves too much. Why not see this dynamic with all jobs and products ever?
5lemonhope
Have you ever tried hiring someone or getting a job? Mostly lemons all around (apologies for the offense, jobseekers, i'm sure you're not the lemon)
2localdeity
Yup.  Many programmer applicants famously couldn't solve FizzBuzz.  Which is probably because:
2Garrett Baker
But such people are very obvious. You just give them a FizzBuzz test! This is why we have interviews, and work-trials.
2Alexander Gietelink Oldenziel
If therapist quality would actually matter why don't we see this reflected in RCTs?
8ChristianKl
We see it reflected in RCTs. One aspect of therapist quality is for example therapist empathy and empathy is a predictor for treatment outcomes.  The style of therapy does not seem to be important according to RCTs but that doesn't mean that therapist skill is irrelevant. 
4Alexander Gietelink Oldenziel
Thank you practicing the rationalist virtue of scholarship Christian. I was not aware of this paper.  You will have to excuse me for practicing rationalist vice and not believing nor investigating further this paper. I have been so traumatized by the repeated failures of non-hard science, I reject most social science papers as causally confounded p-hacked noise unless it already confirms my priors or is branded correct by somebody I trust. 
2ChristianKl
As far as this particular paper goes I just searched for one on the point in Google Scholar.  I'm not sure what you believe about Spencer Greenberg but he has two interviews with people who believe that therapist skills (where empathy is one of the academic findings) matter: https://podcast.clearerthinking.org/episode/070/scott-miller-why-does-psychotherapy-work-when-it-works-at-all/ https://podcast.clearerthinking.org/episode/192/david-burns-cognitive-behavioral-therapy-and-beyond/
2Alexander Gietelink Oldenziel
I internalized the Dodo verdict and concluded that the specific therapist or therapist style didn't matter anyway. A therapist is just a human mirror. The answer was inside of you all along Miles
[-]Alexander Gietelink Oldenziel50

Four levels of information theory

There are four levels of information theory. 

Level 1:  Number Entropy 

Information is measured by Shannon entropy

Level 2: Random variable 

look at the underlying random variable ('surprisal')   of which entropy is the expectation.

Level 3: Coding functions

Shannon's source coding theorem says entropy of a source  is the expected number of bits for an optimal encoding of samples of .

Related quantity like mutual information, relative entropy, cross e... (read more)

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[-]Alexander Gietelink Oldenziel270

Encrypted Batteries 

(I thank Dmitry Vaintrob for the idea of encrypted batteries. Thanks to Adam Scholl for the alignment angle. Thanks to the Computational Mechanics at the receent compMech conference. )

There are no Atoms in the Void just Bits in the Description. Given the right string a Maxwell Demon transducer can extract energy from a heatbath. 

Imagine a pseudorandom heatbath + nano-Demon. It looks like a heatbath from the outside but secretly there is a private key string that when fed to the nano-Demon allows it to extra lots of energy from the heatbath. 

 

P.S. Beyond the current ken of humanity lies a generalized concept of free energy that describes the generic potential ability or power of an agent to achieve goals. Money, the golden calf of Baal is one of its many avatars. Could there be ways to encrypt generalized free energy batteries to constraint the user to only see this power for good? It would be like money that could be only spent on good things. 

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[-]gwern133

Imagine a pseudorandom heatbath + nano-Demon. It looks like a heatbath from the outside but secretly there is a private key string that when fed to the nano-Demon allows it to extra lots of energy from the heatbath.

What would a 'pseudorandom heatbath' look like? I would expect most objects to quickly depart from any sort of private key or PRNG. Would this be something like... a reversible computer which shuffles around a large number of blank bits in a complicated pseudo-random order every timestep*, exposing a fraction of them to external access? so a daemon with the key/PRNG seed can write to the blank bits with approaching 100% efficiency (rendering it useful for another reversible computer doing some actual work) but anyone else can't do better than 50-50 (without breaking the PRNG/crypto) and that preserves the blank bit count and is no gain?

* As I understand reversible computing, you can have a reversible computer which does that for free: if this is something like a very large period loop blindly shuffling its bits, it need erase/write no bits (because it's just looping through the same states forever, akin to a time crystal), and so can be computed indefinitely at arbitrarily low energy cost. So any external computer which syncs up to it can also sync at zero cost, and just treat the exposed unused bits as if they were its own, thereby saving power.

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2Alexander Gietelink Oldenziel
That is my understanding, yes.
2mako yass
Yeah I'm pretty sure you would need to violate heisenberg uncertainty in order to make this and then you'd have to keep it in a 0 kelvin cleanroom forever. A practical locked battery with tamperproofing would mostly just look like a battery.
[-]Alexander Gietelink Oldenziel60

Clem's Synthetic- Physicalist Hypothesis

The mathematico-physicalist hypothesis states that our physical universe is actually a piece of math. It was famously popularized by Max Tegmark. 

It's one of those big-brain ideas that sound profound when you first hear about it, then you think about it some more and you realize it's vacuous. 

Recently, in a conversation with Clem von Stengel they suggested a version of the mathematico-physicalist hypothesis that I find provoking. 

Synthetic mathematics 

'Synthetic' mathematics is a bit of weird name... (read more)

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[-]Alexander Gietelink Oldenziel4125

Novel Science is Inherently Illegible

Legibility, transparency, and open science are generally considered positive attributes, while opacity, elitism, and obscurantism are viewed as negative. However, increased legibility in science is not always beneficial and can often be detrimental.

Scientific management, with some exceptions, likely underperforms compared to simpler heuristics such as giving money to smart people or implementing grant lotteries. Scientific legibility suffers from the classic "Seeing like a State" problems. It constrains endeavors to the least informed stakeholder, hinders exploration, inevitably biases research to be simple and myopic, and exposes researchers to constant political tug-of-war between different interest groups poisoning objectivity. 

I think the above would be considered relatively uncontroversial in EA circles.  But I posit there is something deeper going on: 

Novel research is inherently illegible. If it were legible, someone else would have already pursued it. As science advances her concepts become increasingly counterintuitive and further from common sense. Most of the legible low-hanging fruit has already been picked, and novel research requires venturing higher into the tree, pursuing illegible paths with indirect and hard-to-foresee impacts.

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3ChristianKl
It might also be that a legible path would be low status to pursue in the existing scientific communities and thus nobody pursues it. If you look at a low-hanging fruit that was unpicked for a long time, airborne transmission of many viruses like the common cold, is a good example. There's nothing illegible about it.
2Alexander Gietelink Oldenziel
mmm Good point. Do you have more examples?
2ChristianKl
The core reason for holding the belief is because the world does not look to me like there's little low hanging fruit in a variety of domains of knowledge I have thought about over the years. Of course it's generally not that easy to argue for the value of ideas that the mainstream does not care about publically. Wei Dei recently wrote: If you look at the broader field of rationality, the work of Judea Pearl and that of Tetlock both could have been done twenty years earlier. Conceptually, I think you can argue that their work was some of the most important work that was done in the last decades. Judea Pearl writes about how allergic people were against the idea of factoring in counterfactuals and causality. 
[-]Thomas Kwa110

Novel research is inherently illegible.

I'm pretty skeptical of this and think we need data to back up such a claim. However there might be bias: when anyone makes a serendipitous discovery it's a better story, so it gets more attention. Has anyone gone through, say, the list of all Nobel laureates and looked at whether their research would have seemed promising before it produced results?

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[-]Alexander Gietelink Oldenziel115

Thanks for your skepticism, Thomas. Before we get into this, I'd like to make sure actually disagree. My position is not that scientific progress is mostly due to plucky outsiders who are ignored for decades. (I feel something like this is a popular view on LW). Indeed, I think most scientific progress is made through pretty conventional (academic) routes.

I think one can predict that future scientific progress will likely be made by young smart people at prestigious universities and research labs specializing in fields that have good feedback loops and/or have historically made a lot of progress: physics, chemistry, medicine, etc

My contention is that beyond very broad predictive factors like this, judging whether a research direction is fruitful is hard & requires inside knowledge. Much of this knowledge is illegible, difficult to attain because it takes a lot of specialized knowledge etc.

Do you disagree with this ?

I do think that novel research is inherently illegible. Here are some thoughts on your comment :

1.Before getting into your Nobel prize proposal I'd like to caution for Hindsight bias (obvious reasons).

  1. And perhaps to some degree I'd like to argue the burden of proo

... (read more)
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[-]aysja132

I guess I'm not sure what you mean by "most scientific progress," and I'm missing some of the history here, but my sense is that importance-weighted science happens proportionally more outside of academia. E.g., Einstein did his miracle year outside of academia (and later stated that he wouldn't have been able to do it, had he succeeded at getting an academic position), Darwin figured out natural selection, and Carnot figured out the Carnot cycle, all mostly on their own, outside of academia. Those are three major scientists who arguably started entire fields (quantum mechanics, biology, and thermodynamics). I would anti-predict that future scientific progress, of the field-founding sort, comes primarily from people at prestigious universities, since they, imo, typically have some of the most intense gatekeeping dynamics which make it harder to have original thoughts. 

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8Alexander Gietelink Oldenziel
Good point.  I do wonder to what degree that may be biased by the fact that there were vastly less academic positions before WWI/WWII. In the time of Darwin and Carnot these positions virtually didn't exist. In the time of Einstein they did exist but they were quite rare still.  How many examples do you know of this happening past WWII? Shannon was at Bell Labs iirc As counterexample of field-founding happening in academia: Godel, Church, Turing were all in academia. 
6Thomas Kwa
Oh, I actually 70% agree with this. I think there's an important distinction between legibility to laypeople vs legibility to other domain experts. Let me lay out my beliefs: * In the modern history of fields you mentioned, more than 70% of discoveries are made by people trying to discover the thing, rather than serendipitously. * Other experts in the field, if truth-seeking, are able to understand the theory of change behind the research direction without investing huge amounts of time. * In most fields, experts and superforecasters informed by expert commentary will have fairly strong beliefs about which approaches to a problem will succeed. The person working on something will usually have less than 1 bit advantage about whether their framework will be successful than the experts, unless they have private information (e.g. already did the crucial experiment). This is the weakest belief and I could probably be convinced otherwise just by anecdotes. * The successful researchers might be confident they will succeed, but unsuccessful ones could be almost as confident on average. So it's not that the research is illegible, it's just genuinely hard to predict who will succeed. * People often work on different approaches to the problem even if they can predict which ones will work. This could be due to irrationality, other incentives, diminishing returns to each approach, comparative advantage, etc. If research were illegible to other domain experts, I think you would not really get Kuhnian paradigms, which I am pretty confident exist. Paradigm shifts mostly come from the track record of an approach, so maybe this doesn't count as researchers having an inside view of others' work though.
[-]Alexander Gietelink Oldenziel1312

Thank you, Thomas. I believe we find ourselves in broad agreement. The distinction you make between lay-legibility and expert-legibility is especially well-drawn.

One point: the confidence of researchers in their own approach may not be the right thing to look at. Perhaps a better measure is seeing who can predict not only their own approach will succed but explain in detail why other approaches won't work. Anecdotally, very succesful researchers have a keen sense of what will work out and what won't - in private conversation many are willing to share detailed models why other approaches will not work or are not as promising. I'd have to think about this more carefully but anecdotally the most succesful researchers have many bits of information over their competitors not just one or two. (Note that one bit of information means that their entire advantage could be wiped out by answering a single Y/N question. Not impossible, but not typical for most cases)

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2Garrett Baker
I don’t think the application to EA itself would be uncontroversial.
5Seth Herd
What areas of science are you thinking of? I think the discussion varies dramatically. I think allowing less legibility would help make science less plodding, and allow it to move in larger steps. But there's also a question of what direction it's plodding. The problem I saw with psych and neurosci was that it tended to plod in nearly random, not very useful directions. And what definition of "smart"? I'm afraid that by a common definition, smart people tend to do dumb research, in that they'll do galaxy brained projects that are interesting but unlikely to pay off. This is how you get new science, but not useful science. In cognitive psychology and neuroscience, I want to see money given to people who are both creative and practical. They will do new science that is also useful. In psychology and neuroscience, scientists pick the grantees, and they tend to give money to those whose research they understand. This produces an effect where research keeps following one direction that became popular long ago. I think a different method of granting would work better, but the particular method matters a lot. Thinking about it a little more, having a mix of personality types involved would probably be useful. I always appreciated the contributions of the rare philospher who actually learned enough to join a discussion about psych or neurosci research. I think the most important application of meta science theory is alignment research.
[-]Alexander Gietelink Oldenziel62

Know your scientific competitors. 

In trading, entering a market dominated by insiders without proper research is a sure-fire way to lose a lot of money and time.  Fintech companies go to great lengths to uncover their competitors' strategies while safeguarding their own.

A friend who worked in trading told me that traders would share subtly incorrect advice on trading Discords to mislead competitors and protect their strategies.

Surprisingly, in many scientific disciplines researchers are often curiously incurious about their peers' work.

The long f... (read more)

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2Viliam
Makes sense, but wouldn't this also result in even fewer replications (as a side effect of doing less superfluous work)?
[-]Alexander Gietelink Oldenziel165

Feature request: author-driven collaborative editing [CITATION needed] for the Good and Glorious Epistemic Commons.

Often I find myself writing claims which would ideally have citations but I don't know an exact reference, don't remember where I read it, or am simply too lazy to do the literature search. 

This is bad for scholarship is a rationalist virtue. Proper citation is key to preserving and growing the epistemic commons. 

It would be awesome if my lazyness were rewarded by giving me the option to add a [CITATION needed] that others could then suggest (push) a citation, link or short remark which the author (me) could then accept. The contribution of the citator is acknowledged of course. [even better would be if there was some central database that would track citations & links like with crosslinking etc like wikipedia] 

a sort hybrid vigor of Community Notes and Wikipedia if you will. but It's collaborative, not adversarial*

author: blablablabla

sky is blue [citation Needed]

blabblabla

intrepid bibliographer: (push) [1] "I went outside and the sky was blue", Letters to the Empirical Review

 

*community notes on twitter has been a universally lauded concept when it first launched. We are already seeing it being abused unfortunately, often used for unreplyable cheap dunks. I still think it's a good addition to twitter but it does show how difficult it is to create shared agreed-upon epistemics in an adverserial setting. 

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[-]Alexander Gietelink Oldenziel40

[This is joint thinking with Sam Eisenstat. Also thanks to Caspar Oesterheld for his thoughtful comments. Thanks to Steve Byrnes for pushing me to write this out.]


The Hyena problem in long-term planning  

Logical induction is a nice framework to think about bounded reasoning. Very soon after the discovery of logical induction people tried to make logical inductor decision makers work. This is difficult to make work: one of two obstacles is

Obstacle 1: Untaken Actions are not Observable

Caspar Oesterheld brilliantly solved this problem by using auction ma... (read more)

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[-]Alexander Gietelink Oldenziel50

Idle thoughts about UDASSA I: the Simulation hypothesis 

I was talking to my neighbor about UDASSA the other day. He mentioned a book I keep getting recommended but never read where characters get simulated and then the simulating machine is progressively slowed down. 

One would expect one wouldn't be able to notice from inside the simulation that the simulating machine is being slowed down.

This presents a conundrum for simulation style hypotheses: if the simulation can be slowed down 100x without the insiders noticing, why not 1000x or 10^100x or ... (read more)

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2Dagon
In most conceptions of simulation, there is no meaning to "slowed down", from the perspective of the simulated universe.  Time is a local phenomenon in this view - it's just a compression mechanism so the simulators don't have to store ALL the states of the simulation, just the current state and the rules to progress it.    Note that this COULD be said of a non-simulated universe as well - past and future states are determined but not accessible, and the universe is self-discovering them by operating on the current state via physics rules.  So there's still no inside-observable difference between simulated and non-simulated universes. UDASSA seems like anthropic reasoning to include Boltzmann Brain like conceptions of experience.  I don't put a lot of weight on it, because all anthropic reasoning requires an outside-view of possible observations to be meaningful. And of course, none of this relates to upload, where a given sequence of experiences can span levels of simulation.  There may or may not be a way to do it, but it'd be a copy, not a continuation.
2Alexander Gietelink Oldenziel
The point you make in the your first paragraph is contained in the original shortform post. The point of the post is exactly that an UDASSA-style argument can nevertheless recover something like a 'distribution of likely slowdown factors'. This seems quite curious. I suggest reading Falkovich's post on UDASSA to get a sense whats so intriguing abouy the UDASSA franework.
[-]Alexander Gietelink Oldenziel80

[see also Hanson on rot, generalizations of the second law to nonequilibrium systems (Baez-Pollard, Crutchfield et al.) ]

Imperfect Persistence of Metabolically Active Engines

All things rot. Indidivual organisms, societies-at-large, businesses, churches, empires and maritime republics, man-made artifacts of glass and steel, creatures of flesh and blood.

Conjecture #1 There is a lower bound on the amount of dissipation / rot that any metabolically-active engine creates. 

Conjecture #2 Metabolic Rot of an engine is proportional to (1) size and complexity o... (read more)

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[-]Alexander Gietelink Oldenziel21

All concepts can be learnt. All things worth knowing may be grasped. Eventually.

All can be understood - given enough time and effort.

For Turing-complete organism, there is no qualitive gap between knowledge and ignorance. 

No qualitive gap but one. The true qualitative difference: quantity. 

Often we simply miss a piece of data. The gap is too large - we jump and never reach the other side. A friendly hominid who has trodden the path before can share their journey. Once we know the road, there is no mystery. Only effort and time. Some hominids choose not to share their journey. We keep a special name for these singular hominids: genius.

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4Viliam
Well, that's exactly the problem.
[-]Alexander Gietelink Oldenziel31

(conversation with Scott Garrabrant)

Destructive Criticism

Sometimes you can say something isn't quite right but you can't provide an alternative.

  • rejecting the null hypothesis
  • give a (partial) countermodel that shows that certain proof methods can't prove $A$ without proving $\neg A$. 
  • Looking at Scott Garrabrant's game of life board - it's not white noise but I can't say why

Difference between 'generation of ideas' and 'filtration of ideas' - i.e. babble and prune. 

ScottG: Bayesian learning assumes we are in a babble-rich environment and only does pr... (read more)

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[-]Alexander Gietelink Oldenziel156

Pockets of Deep Expertise 

Why am I so bullish on academic outreach? Why do I keep hammering on 'getting the adults in the room'? 

It's not that I think academics are all Super Smart. 

I think rationalists/alignment people correctly ascertain that most professors don't have much useful to say about alignment & deep learning and often say silly things. They correctly see that much of AI congress is fueled by labs and scale not ML academia. I am bullish on non-ML academia, especially mathematics, physics and to a lesser extent theoretical CS, neuroscience, some parts of ML/ AI academia. This is because while I think 95 % of academia is bad and/or useless there are Pockets of Deep Expertise. Most questions in alignment are close to existing work in academia in some sense - but we have to make the connection!

A good example is 'sparse coding' and 'compressed sensing'. Lots of mech.interp has been rediscovering some of the basic ideas of sparse coding. But there is vast expertise in academia about these topics. We should leverage these!

Other examples are singular learning theory, computational mechanics, etc

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[-]Alexander Gietelink Oldenziel20

Abnormalised sampling?
Probability theory talks about sampling for probability distributions, i.e. normalized measures. However, non-normalized measures abound: weighted automata, infra-stuff, uniform priors on noncompact spaces, wealth in logical-inductor esque math, quantum stuff?? etc.

Most of probability theory constructions go through just for arbitrary measures, doesn't need the normalization assumption. Except, crucially, sampling. 

What does it even mean to sample from a non-normalized measure? What is unnormalized abnormal sampling?

I don't know.... (read more)

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[-]Alexander Gietelink Oldenziel4110

Why don't animals have guns? 

Or why didn't evolution evolve the Hydralisk?

Evolution has found (sometimes multiple times) the camera, general intelligence, nanotech, electronavigation, aerial endurance better than any drone, robots more flexible than any human-made drone, highly efficient photosynthesis, etc. 

First of all let's answer another question: why didn't evolution evolve the wheel like the alien wheeled elephants in His Dark Materials?

Is it biologically impossible to evolve?

Well, technically, the flagella of various bacteria is a proper wheel.

No the likely answer is that wheels are great when you have roads and suck when you don't. Roads are build by ants to some degree but on the whole probably don't make sense for an animal-intelligence species. 

Aren't there animals that use projectiles?

Hold up. Is it actually true that there is not a single animal with a gun, harpoon or other projectile weapon?

Porcupines have quils, some snakes spit venom, a type of fish spits water as a projectile to kick insects of leaves than eats insects. Bombadier beetles can produce an explosive chemical mixture. Skunks use some other chemicals. Some snails shoot harpoons from very c... (read more)

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6Daniel Murfet
Please develop this question as a documentary special, for lapsed-Starcraft player homeschooling dads everywhere.
1nim
Animals do have guns. Humans are animals. Humans have guns. Evolution made us, we made guns, therefore guns indirectly exist because of evolution. Or do you mean "why don't animals have something like guns but permanently attached to them instead of regular guns?" There, I'd start with wondering why humans prefer to have our guns separate from our bodies, compared to affixing them permanently or semi-permanently to ourselves. All the drawbacks of choosing a permanently attached gun would also disadvantage a hypothetical creature that got the accessory through a longer, slower selection process.
4Tao Lin
Another huge missed opportunity is thermal vision. Thermal infrared vision is a gigantic boon for hunting at night, and you might expect eg owls and hawks to use it to spot prey hundreds of meters away in pitch darkness, but no animals do (some have thermal sensing, but only extremely short range)
6Carl Feynman
Snakes have thermal vision, using pits on their cheeks to form pinhole cameras. It pays to be cold-blooded when you’re looking for nice hot mice to eat.
2Alexander Gietelink Oldenziel
Woah great example didn't know bout that. Thanks Tao
5quetzal_rainbow
Thermal vision for warm-blooded animals has obvious problems with noise.
2Alexander Gietelink Oldenziel
Care to explain? Noise?
5quetzal_rainbow
If you are warm, any warm-detectors inside your body will detect mostly you. Imagine if blood vessels in your own eye radiated in visible spectrum with the same intensity as daylight environment.
4Alexander Gietelink Oldenziel
Can't you filter that out? . How do fighter planes do it?
8Carl Feynman
It‘s possible to filter out a constant high value, but not possible to filter out a high level of noise.  Unfortunately warmth = random vibration = noise.  If you want a low noise thermal camera, you have to cool the detector, or only look for hot things, like engine flares.  Fighter planes do both.
6Nathan Helm-Burger
Most uses of projected venom or other unpleasant substance seem to be defensive rather than offensive. One reason for this is that it's expensive to make the dangerous substance, and throwing it away wastes it. This cost is affordable if it is used to save your own life, but not easily affordable to acquire a single meal. This life vs meal distinction plays into a lot of offense/defense strategy expenses. For the hunting options, usually they are also useful for defense. The hunting options all seem cheaper to deploy: punching mantis shrimp, electric eel, fish spitting water... My guess it that it's mostly a question of whether the intermediate steps to the evolved behavior are themselves advantageous. Having a path of consistently advantageous steps makes it much easier for something to evolve. Having to go through a trough of worse-in-the-short-term makes things much less likely to evolve. A projectile fired weakly is a cost (energy to fire, energy to producing firing mechanism, energy to produce the projectile, energy to maintain the complexity of the whole system despite it not being useful yet). Where's the payoff of a weakly fired projectile? Humans can jump that gap by intuiting that a faster projectile would be more effective. Evolution doesn't get to extrapolate and plan like that.
7Carl Feynman
Jellyfish have nematocysts, which is a spear on a rope, with poison on the tip.  The spear has barbs, so when it goes in, it sticks.  Then the jellyfish pulls in its prey.  The spears are microscopic, but very abundant.
4Nathan Helm-Burger
Yes, but I think snake fangs and jellyfish nematocysts are a slightly different type of weapon. Much more targeted application of venom. If the jellyfish squirted their venom as a cloud into the water around them when a fish came near, I expect it would not be nearly as effective per unit of venom. As a case where both are present, the spitting cobra uses its fangs to inject venom into its prey. However, when threatened, it can instead (wastefully) spray out its venom towards the eyes of an attacker. (the venom has little effect on unbroken mammal skin, but can easily blind if it gets into their eyes).
4Alexander Gietelink Oldenziel
Fair argument I guess where I'm lost is that I feel I can make the same 'no competitive intermediate forms' for all kinds of wondrous biological forms and functions that have evolved, e.g. the nervous system. Indeed, this kind of argument used to be a favorite for ID advocates.
3Carl Feynman
There are lots of excellent applications for even very simple nervous systems.  The simplest surviving nervous systems are those of jellyfish.  They form a ring of coupled oscillators around the periphery of the organism.  Their goal is to synchronize muscular contraction so the bell of the jellyfish contracts as one, to propel the jellyfish efficiently.  If the muscles contracted independently, it wouldn’t be nearly as good. Any organism with eyes will profit from having a nervous system to connect the eyes to the muscles.  There’s a fungus with eyes and no nervous system, but as far as I know, every animal with eyes also has a nervous system. (The fungus in question is Pilobolus, which uses its eye to aim a gun.  No kidding!)
5Garrett Baker
My naive hypothesis: Once you're able to launch a projectile at a predator or prey such that it breaks skin or shell, if you want it to die, its vastly cheaper to make venom at the ends of the projectiles than to make the projectiles launch fast enough that there's a good increase in probability the adversary dies quickly.
4Alexander Gietelink Oldenziel
Why don't lions, tigers, wolves, crocodiles, etc have venom-tipped claws and teeth? (Actually, apparently many ancestral mammal species like did have venom spurs, similar to the male platypus)
9JBlack
My completely naive guess would be that venom is mostly too slow for creatures of this size compared with gross physical damage and blood loss, and that getting close enough to set claws on the target is the hard part anyway. Venom seems more useful as a defensive or retributive mechanism than a hunting one.
[-]Alexander Gietelink Oldenziel3-3

Reasonable interpretations of Recursive Self Improvement are either trivial, tautological or false?

  1. (Trivial)  AIs will do RSI by using more hardware - trivial form of RSI
  2.  (Tautological) Humans engage in a form of (R)SI when they engage in meta-cognition. i.e. therapy is plausibly a form of metacognition. Meta-cognition is  plausible one of the remaining hallmarks of true general intelligence. See Vanessa Kosoy's "Meta-Cognitive Agents". 
    In this view, AGIs will naturally engage in meta-cognition because they're generally intelligent. The
... (read more)
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3Vladimir_Nesov
SGD finds algorithms. Before the DL revolution, science studied such algorithms. Now, the algorithms become inference without as much as a second glance. With sufficient abundance of general intelligence brought about by AGI, interpretability might get a lot out of studying the circuits SGD discovers. Once understood, the algorithms could be put to more efficient use, instead of remaining implicit in neural nets and used for thinking together with all the noise that remains from the search.
1lemonhope
I think the AI will improve (itself) via better hardware and algorithms, and it will be a slog. The AI will frequently need to do narrow tasks where the general algorithm is very inefficient.
2Alexander Gietelink Oldenziel
As I state in the OP I don't feel these examples are nontrivial examples of RSI.
2Michaël Trazzi
I think most interpretations of RSI aren't useful. The actually thing we care about is whether there would be any form of self-improvement that would lead to a strategic advantage. The fact that something would "recursively" self-improve 12 times or 2 times don't really change what we care about.  With respect to your 3 points. 1) could happen by using more hardware, but better optimization of current hardware / better architecture is the actually scary part (which could lead to the discovery of "new physics" that could enable an escape even if the sandbox was good enough for the model before a few iterations of the RSI). 2) I don't think what you're talking about in terms of meta-cognition is relevant to the main problem. Being able to look at your own hardware or source code is though. 3) Cf. what I said at the beginning. The actual "limit" is I believe much higher than the strategic advantage threshold.
2niplav
:insightful reaction: I give this view ~20%: There's so much more info in some datapoints (curvature, third derivative of the function, momentum, see also Empirical Bayes-like SGD, the entire past trajectory through the space) that seems so available and exploitable!
2acertain
What about specialized algorithms for problems (e.g. planning algorithms)?
2Alexander Gietelink Oldenziel
What do you mean exactly? There are definitely domains in which humans have not yet come close to optimal algorithms.
1acertain
I guess this is sorta about your 3, which I disbelieve (though algorithms for tasks other than learning are also important). Currently, Bayesian inference vs SGD is a question of how much data you have (where SGD wins except for very little data). For small to medium amounts of data, even without AGI, I expect SGD to lose eventually due to better inference algorithms. For many problems I have the intuition that it's ~always possible to improve performance with more complicated algorithms (eg sat solvers). All that together makes me expect there to be inference algorithms that scale to very large amounts of data (that aren't going to be doing full Bayesian inference but rather some complicated approximation).
2Thomas Kwa
What about automated architecture search?
2Alexander Gietelink Oldenziel
Architectures mostly don't seem to matter, see 3.  When they do (like in Vanessa's meta-MDPs) I think it's plausible automated architecture search is a simply an instantiation of the algorithm for general intelligence (see 2.)
[-]Alexander Gietelink Oldenziel20

SLT and phase transitions

The morphogenetic SLT story says that during training the Bayesian posterior concentrates around a series of subspaces  with rlcts   and losses . As the size of the data sample  is scaled the Bayesian posterior makes transitions  trading off higher complexity (higher ) for better accuracy (lower loss ).

This is the radical new framework of SLT: phase transitions happen i... (read more)

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[-]Alexander Gietelink Oldenziel24

Alignment by Simulation?

I've heard this alignment plan that is a variation of 'simulate top alignment researchers' with an LLM. Usually the poor alignment researcher in question is Paul. 

This strikes me as deeply unserious and I am confused why it is having so much traction. 

That AI-assisted alignment is coming (indeed, is already here!) is undeniable. But even somewhat accurately simulating a human from textdata is a crazy sci-fi ability, probably not even physically possible. It seems to ascribe nearly magical abilities to LLMs. 

Predicting... (read more)

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[-]Alexander Gietelink Oldenziel131

Fractal Fuzz: making up for size

GPT-3 recognizes 50k possible tokens. For a 1000 token context window that means there are  possible prompts. Astronomically large. If we assume the output of a single run of gpt is 200 tokens then for each possible prompt there are  possible continuations. 

GPT-3 is probabilistic, defining for each possible prompt  () a distribution  on a set of size , in other words a  dimensional space. [1]

Mind-boggingly large. Compared to these numbers the amount of data (40 trillion tokens??) and the size of the model (175 billion parameters) seems absolutely puny in comparison.

I won't be talking about the data, or 'overparameterizations' in this short, that is well-explained by Singular Learning Theory. Instead, I will be talking about nonrealizability.

Nonrealizability & the structure of natural data

Recall the setup of (parametric) Bayesian learning: there is a sample space , a true distribution  on  and a parameterized family of probability distributions .

It is often assumed that the true distrib... (read more)

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1Zach Furman
Very interesting, glad to see this written up! Not sure I totally agree that it's necessary for W to be a fractal? But I do think you're onto something. In particular you say that "there are points y in the larger dimensional space that are very (even arbitrarily) far from W," but in the case of GPT-4 the input space is discrete, and even in the case of e.g. vision models the input space is compact. So the distance must be bounded. Plus if you e.g. sample a random image, you'll find there's usually a finite distance you need to travel in the input space (in L1, L2, etc) until you get something that's human interpretable (i.e. lies on the data manifold). So that would point against the data manifold being dense in the input space. But there is something here, I think. The distance usually isn't that large until you reach a human interpretable image, and it's quite easy to perturb images slightly to have completely different interpretations (both to humans and ML systems). A fairly smooth data manifold wouldn't do this. So my guess is that the data "manifold" is in fact not a manifold globally, but instead has many self-intersections and is singular. That would let it be close to large portions of input space without being literally dense in it. This also makes sense from an SLT perspective. And IIRC there's some empirical evidence that the dimension of the data "manifold" is not globally constant.
2Alexander Gietelink Oldenziel
The input and output spaces etc Ω are all discrete but the spaces of distributions Δ(Ω) on those spaces are infinite (but still finite-dimensional).  It depends on what kind of metric one uses, compactness assumptions etc whether or not you can be arbitrarily far. I am being rather vague here. For instance, if you use the KL-divergence, then K(q|puniform) is always bounded -  indeed it equals the entropy of the true distribution H(q)! I don't really know what ML people mean by the data manifold so won't say more about that.  I am talking about the space W of parameter values of a conditional probability distribution p(x|w).   I think that W having nonconstant local dimension doesn't seem that relevant since the largest dimensional subspace would dominate? Self-intersections and singularities could certainly occur here. (i) singularities in the SLT sense have to do with singularities in the level sets of the KL-divergence (or loss function)  - don't see immediately how these are related to the singularities that you are talking about here (ii) it wouldn't increase the dimensionality (rather the opposite).  The fractal dimension is important basically because of space-filling curves : a space that has a low-dimensional parameterization can nevertheless have a very large effective dimensions when embedded fractally into a larger-dimensional space. These embeddings can make a low-dimensional parameterization effectively have higher dimension. 
1Zach Furman
Sorry, I realized that you're mostly talking about the space of true distributions and I was mainly talking about the "data manifold" (related to the structure of the map x↦p(x∣w∗) for fixed w∗). You can disregard most of that. Though, even in the case where we're talking about the space of true distributions, I'm still not convinced that the image of W under p(x∣w) needs to be fractal. Like, a space-filling assumption sounds to me like basically a universal approximation argument - you're assuming that the image of W densely (or almost densely) fills the space of all probability distributions of a given dimension. But of course we know that universal approximation is problematic and can't explain what neural nets are actually doing for realistic data.
3Alexander Gietelink Oldenziel
Obviously this is all speculation but maybe I'm saying that the universal approximation theorem implies that neural architectures are fractal in space of all distributtions (or some restricted subset thereof)? Curious what's your beef with universal approximation? Stone-weierstrass isn't quantitative - is that the reason? If true it suggest the fractal dimension (probably related to the information dimension I linked to above) may be important.
1Zach Furman
Oh I actually don't think this is speculation, if (big if) you satisfy the conditions for universal approximation then this is just true (specifically that the image of W is dense in function space). Like, for example, you can state Stone-Weierstrass as: for a Hausdorff space X, and the continuous functions under the sup norm C(X,R), the Banach subalgebra of polynomials is dense in C(X,R). In practice you'd only have a finite-dimensional subset of the polynomials, so this obviously can't hold exactly, but as you increase the size of the polynomials, they'll be more space-filling and the error bound will decrease. The problem is that the dimension of W required to achieve a given ϵ error bound grows exponentially with the dimension d of your underlying space X. For instance, if you assume that weights depend continuously on the target function, ϵ-approximating all Cn functions on [0,1]d with Sobolev norm ≤1 provably takes at least O(ϵ−d/n) parameters (DeVore et al.). This is a lower bound. So for any realistic d universal approximation is basically useless - the number of parameters required is enormous. Which makes sense because approximation by basis functions is basically the continuous version of a lookup table. Because neural networks actually work in practice, without requiring exponentially many parameters, this also tells you that the space of realistic target functions can't just be some generic function space (even with smoothness conditions), it has to have some non-generic properties to escape the lower bound.
2Alexander Gietelink Oldenziel
Ooooo okay so this seems like it's directly pointing to the fractal story! Exciting!
2Alexander Gietelink Oldenziel
Obviously this is all speculation but maybe I'm saying that the universal approximation theorem implies that neural architectures are fractal in space of all distributtions (or some restricted subset thereof)? Stone-weierstrass isn't quantitative. If true it suggest the fractal dimension (probably related to the information dimension I linked to above) may be important.
[-]Alexander Gietelink Oldenziel30

Trivial but important

Aumann agreement can fail for purely epistemic reasons because real-world minds do not do Bayesian updating. Bayesian updating is intractable so realistic minds sample from the prior. This is how e.g. gradient descent works and also how human minds work.

In this situation a two minds can end in two different basins with similar loss on the data. Because of computational limitations. These minds can have genuinely different expectation for generalization.

(Of course this does not contradict the statement of the theorem which is correct.)

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[-]Alexander Gietelink Oldenziel20

[Edit 15/05/2024: I currently think that both forward and backward chaining paradigms are missing something important. Instead, there is something like 'side-chaining' or 'wide-chaining' where you are investigating how things are related forwardly, backwardly and sideways to make use of synergystic information ]

 

Optimal Forward-chaining versus backward-chaining.

In general, this is going to depend on the domain. In environments for which we have many expert samples and there are many existing techniques backward-chaining is key.  (i.e. deploying r... (read more)

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[-]Alexander Gietelink Oldenziel164

Corrupting influences

The EA AI safety strategy has had a large focus on placing EA-aligned people in A(G)I labs. The thinking was that having enough aligned insiders would make a difference on crucial deployment decisions & longer-term alignment strategy. We could say that the strategy is an attempt to corrupt the goal of pure capability advance & making money towards the goal of alignment. This fits into a larger theme that EA needs to get close to power to have real influence. 

[See also the large donations EA has made to OpenAI & Anthropic. ]

Whether this strategy paid off...  too early to tell.

What has become apparent is that the large AI labs & being close to power have had a strong corrupting influence on EA epistemics and culture. 

  • Many people in EA now think nothing of being paid Bay Area programmer salaries for research or nonprofit jobs.
  •  There has been a huge influx of MBA blabber being thrown around. Bizarrely EA funds are often giving huge grants to for profit organizations for which it is very unclear whether they're really EA-aligned in the long-term or just paying lip service. Highly questionable that EA should be trying to do venture
... (read more)
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7Daniel Murfet
As a supervisor of numerous MSc and PhD students in mathematics, when someone finishes a math degree and considers a job, the tradeoffs are usually between meaning, income, freedom, evil, etc., with some of the obvious choices being high/low along (relatively?) obvious axes. It's extremely striking to see young talented people with math or physics (or CS) backgrounds going into technical AI alignment roles in big labs, apparently maximising along many (or all) of these axes! Especially in light of recent events I suspect that this phenomenon, which appears too good to be true, actually is.
0Noosphere89
I'd arguably say this is good, primarily because I think EA was already in danger of it's AI safety wing becoming unmoored from reality by ignoring key constraints, similar to how early Lesswrong before the deep learning era around 2012-2018 turned out to be mostly useless due to how much everything was stated in a mathematical way, and not realizing how many constraints and conjectured constraints applied to stuff like formal provability, for example..
5RHollerith
Yes!
6Thomas Kwa
I'm not too concerned about this. ML skills are not sufficient to do good alignment work, but they seem to be very important for like 80% of alignment work and make a big difference in the impact of research (although I'd guess still smaller than whether the application to alignment is good) * Primary criticisms of Redwood involve their lack of experience in ML * The explosion of research in the last ~year is partially due to an increase in the number of people in the community who work with ML. Maybe you would argue that lots of current research is useless, but it seems a lot better than only having MIRI around * The field of machine learning at large is in many cases solving easier versions of problems we have in alignment, and therefore it makes a ton of sense to have ML research experience in those areas. E.g. safe RL is how to get safe policies when you can optimize over policies and know which states/actions are safe; alignment can be stated as a harder version of this where we also need to deal with value specification, self-modification, instrumental convergence etc.
4Alexander Gietelink Oldenziel
I mostly agree with this. I should have said 'prestige within capabilities research' rather than ML skills which seems straightforwardly useful. The former is seems highly corruptive.
[-]Alexander Gietelink Oldenziel20

Thin versus Thick Thinking

 

Thick: aggregate many noisy sources to make a sequential series of actions in mildly related environments, model-free RL

carnal sins: failure of prioritization / not throwing away enough information , nerdsnipes, insufficient aggegration, trusting too much in any particular model,  indecisiveness, overfitting on noise, ignoring consensus of experts/ social reality

default of the ancestral environment

CEOs, general, doctors, economist, police detective in the real world, trader

Thin: precise, systematic analysis, preferably ... (read more)

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[-]Alexander Gietelink Oldenziel20

[Thanks to Vlad Firoiu for helping me]

An Attempted Derivation of the Lindy Effect
Wikipedia:

The Lindy effect (also known as Lindy's Law[1]) is a theorized phenomenon by which the future life expectancy of some non-perishable things, like a technology or an idea, is proportional to their current age.

Laplace Rule of Succesion 

What is the probability that the Sun will rise tomorrow, given that is has risen every day for 5000 years? 

Let  denote the probability that the Sun will rise tomorrow. A priori we have no information on the value of&... (read more)

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2JBlack
I haven't checked the derivation in detail, but the final result is correct. If you have a random family of geometric distributions, and the density around zero of the decay rates doesn't go to zero, then the expected lifetime is infinite. All of the quantiles (e.g. median or 99%-ile) are still finite though, and do depend upon n in a reasonable way.
[-]Alexander Gietelink Oldenziel30

Imprecise Information theory 

Would like a notion of entropy for credal sets. Diffractor suggests the following:

let  be a credal set. 

Then the entropy of  is defined as

where  denotes the usual Shannon entropy.

I don't like this since it doesn't satisfy the natural desiderata below. 


Instead, I suggest the following. Let  denote the (absolute) maximum entropy distribution, i.e.  and let .

Desideratum 1: ... (read more)

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[-]Alexander Gietelink Oldenziel20

Generalized Jeffrey Prior for singular models?

For singular models the Jeffrey Prior is not well-behaved for the simple fact that it will be zero at minima of the loss function. 
Does this mean the Jeffrey prior is only of interest in regular models? I beg to differ. 

Usually the Jeffrey prior is derived as parameterization invariant prior. There is another way of thinking about the Jeffrey prior as arising from an 'indistinguishability prior'.

The argument is delightfully simple: given two weights  if they encode the same distributi... (read more)

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1Daniel Murfet
You might reconstruct your sacred Jeffries prior with a more refined notion of model identity, which incorporates derivatives (jets on the geometric/statistical side and more of the algorithm behind the model on the logical side).
2Alexander Gietelink Oldenziel
Is this the jet prior I've been hearing about? I argued above that given two weights w1,w2 such that they have (approximately) the same conditional distribution p(x|y,w1)∼=p(x|y,w2) the 'natural' or 'canonical' prior should assign them equal prior weights ϕ(w1)=ϕ(w2). A more sophisticated version of this idea is used to argue for the Jeffrey prior as a canonical prior.  Some further thoughts: * imposing this uniformity condition would actually contradict some version of Occam's razor. Indeed, w1 could be algorithmically much more complex (i.e. have much higher description length) than w2 but they still might have similar or the same predictions.  * The difference between same-on-the-nose versus similar might be very material. Two conditional probability distributions might be quite similar [a related issue here is that the KL-divergence is assymetric so similarity is a somewhat ill-defined concept], yet one intrinsically requires far more computational resources.  * A very simple example is the uniform distribution puniform(x)=1N and another distribution p′(x) that is a small perturbation of the uniform distribution but whose exact probabilities p′(x) have decimal expansions that have very large description length (this can be produced by adding long random strings to the binary expansion).  * [caution: CompMech propaganda incoming] More realistic examples do occur i.e. in finding optimal predictors of dynamical systems at the edge of chaos. See the section on 'intrinsic computation of the period-doubling cascade', p.27-28 of calculi of emergence for a classical example.   * Asking for the prior ϕ to restrict to be uniform for weights wi that have equal/similar conditional distributions p(x|y,wi) seems very natural but it doesn't specify how the prior should relate weights with different conditional distributions. Let's say we have two weights w1, w2 with very different conditional probability distributions. Let Wi={w∈W|p(x|y,w)∼=p(x|y,wi)}. How sh
5Daniel Murfet
I think there's no such thing as parameters, just processes that produce better and better approximations to parameters, and the only "real" measures of complexity have to do with the invariants that determine the costs of those processes, which in statistical learning theory are primarily geometric (somewhat tautologically, since the process of approximation is essentially a process of probing the geometry of the governing potential near the parameter). From that point of view trying to conflate parameters w1,w2 such that p(x|w1)≈p(x|w2) is naive, because w1,w2 aren't real, only processes that produce better approximations to them are real, and so the ∂∂w derivatives of p(x|w1),p(x|w2) which control such processes are deeply important, and those could be quite different despite p(x|w1)≈p(x|w2) being quite similar. So I view "local geometry matters" and "the real thing are processes approximating parameters, not parameters" as basically synonymous.
[-]Alexander Gietelink Oldenziel40

Latent abstractions Bootlegged.

Let  be random variables distributed according to a probability distribution  on a sample space . 

Defn. A (weak) natural latent of  is a random variable  such that

(i)   are independent conditional on 

(ii) [reconstructability]   for all 

[This is not really reconstructability, more like a stability property. The information is contained in many parts of the system... I might al... (read more)

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[-]Alexander Gietelink Oldenziel120

Inspired by this Shalizi paper defining local causal states. The idea is so simple and elegant I'm surprised I had never seen it before. 

Basically, starting with a a factored probability distribution  over a dynamical DAG  we can use Crutchfield causal state construction locally to construct a derived causal model factored over  the dynamical DAG as  where  is defined by considering the past and forward lightcone of  defined as  all those points/ variables  which influence  respectively are influenced by  (in a causal interventional sense) . Now take define the equivalence relatio on realization  of   (which includes  by definition)[1] whenever the conditional probability distribution   on the future light cones are equal. 

These factored probability distributions over dynamical DAGs are called 'fields' by physicists. Given any field  we define a derived local causal state field  in the above way. Woah!

 ... (read more)

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3Dalcy
Just finished the local causal states paper, it's pretty cool! A couple of thoughts though: I don't think the causal states factorize over the dynamical bayes net, unlike the original random variables (by assumption). Shalizi doesn't claim this either. * This would require proving that each causal state is conditionally independent of its nondescendant causal states given its parents, which is a stronger theorem than what is proved in Theorem 5 (only conditionally independent of its ancestor causal states, not necessarily all the nondescendants) Also I don't follow the Markov Field part - how would proving: ... show that the causal states is a markov field (aka satisfies markov independencies (local or pairwise or global) induced by an undirected graph)? I'm not even sure what undirected graph the causal states would be markov with respect to. Is it the ... * ... skeleton of the dynamical Bayes Net? that would require proving a different theorem: "if we condition on parents and children of the patch, then we get independence of all the other states" which would prove local markov independency * ... skeleton of the dynamical Bayes Net + edges for the original graph for each t? that would also require proving a different theorem: "if we condition on present neighbors, parents, and children of the patch, then we get independence of all the other states" which would prove local markov independency Also for concreteness I think I need to understand its application in detecting coherent structures in cellular automata to better appreciate this construction, though the automata theory part does go a bit over my head :p
8johnswentworth
That condition doesn't work, but here's a few alternatives which do (you can pick any one of them): * Λ=(x↦P[X=x|Λ]) - most conceptually confusing at first, but most powerful/useful once you're used to it; it's using the trick from Minimal Map. * Require that Λ be a deterministic function of X, not just any latent variable. * H(Λ)=I(X,Λ) (The latter two are always equivalent for any two variables X,Λ and are somewhat stronger than we need here, but they're both equivalent to the first once we've already asserted the other natural latent conditions.)
[-]Alexander Gietelink Oldenziel40

Reasons to think Lobian Cooperation is important

Usually the modal Lobian cooperation is dismissed as not relevant for real situations but it is plausible that Lobian cooperation extends far more broadly than what is proved currently.

 It is plausible that much of cooperation we see in the real world is actually approximate Lobian cooperation rather than purely given by traditional game-theoretic incentives. 
Lobian cooperation is far stronger in cases where the players resemble each other and/or have access to one another's blueprint. This is ... (read more)

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5Noosphere89
I definitely agree that cooperation can definitely be way better in the future, and Lobian cooperation, especially with Payor's Lemma, might well be enough to get coordination across entire solar system. That stated, it's much more tricky to expand this strategy to galactic scales, assuming our physical models aren't wrong, because light speed starts to become a very taut constraint under a galaxy wide brain, and acausal strategies will require a lot of compute to simulate entire civilizations. Even worse, they depend on some common structure of values, and I suspect it's impossible to do in the fully general case.
[-]Alexander Gietelink Oldenziel10

Evidence Manipulation and Legal Admissible Evidence

[This was inspired by Kokotaljo's shortform on comparing strong with weak evidence] 


In the real world the weight of many pieces of weak evidence is not always comparable to a single piece of strong evidence. The important variable here is not strong versus weak per se but the source of the evidence. Some sources of evidence are easier to manipulate in various ways. Evidence manipulation, either consciously or emergently, is common and a large obstactle to truth-finding. 

Consider aggregating many ... (read more)

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2ChristianKl
In other cases like medicine, many people argue that direct observation should be ignored ;)
[-]Alexander Gietelink Oldenziel33

Roko's basilisk is a thought experiment which states that an otherwise benevolent artificial superintelligence (AI) in the future would be incentivized to create a virtual reality simulation to torture anyone who knew of its potential existence but did not directly contribute to its advancement or development.

Why Roko's basilisk probably doesn't work for simulation fidelity reasons: 

Roko's basilisk threatens to simulate and torture you in the future if you don't comply. Simulation cycles cost resources. Instead of following through on torturing our wo... (read more)

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1Richard_Kennaway
I have always taken Roko's Basilisk to be the threat that the future intelligence will torture you, not a simulation, for not having devoted yourself to creating it.
1TAG
How do you know you are not in a low fidelity simulation right now? What could you compare it against?
2Vladimir_Nesov
If the agents follow simple principles, it's simple to simulate those principles with high fidelity, without simulating each other in all detail. The obvious guide to the principles that enable acausal coordination is common knowledge of each other, which could be turned into a shared agent that adjudicates a bargain on their behalf.
[-]Alexander Gietelink Oldenziel10

Imagine a data stream 

 

assumed infinite in both directions for simplicity. Here  represents the current state ( the "present") and while  and  represents the future

Predictible Information versus Predictive Information

Predictible information is the maximal information (in bits) that you can derive about the future given the access to the past. Predictive information is the amount of bits that you need from the past to make that optimal prediction.

Suppose you are... (read more)

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[-]Alexander Gietelink Oldenziel20

"The links between logic and games go back a long way. If one thinks of a debate as a kind of game, then Aristotle already made the connection; his writings about syllogism are closely intertwined with his study of the aims and rules of debating. Aristotle’s viewpoint survived into the common medieval name for logic: dialectics. In the mid twentieth century Charles Hamblin revived the link between dialogue and the rules of sound reasoning, soon after Paul Lorenzen had connected dialogue to constructive foundations of logic." from the Stanford Encyclopedia ... (read more)

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[-]Alexander Gietelink Oldenziel70

EDIT 06/11/2024 My thinking has crystallized more on these topics. The current version is lacking but I believe may be steelmanned to a degree. 

"I dreamed I was a butterfly, flitting around in the sky; then I awoke. Now I wonder: Am I a man who dreamt of being a butterfly, or am I a butterfly dreaming that I am a man?"- Zhuangzi

Questions I have that you might have too:

  • why are we here?
  • why do we live in such an extraordinary time?  
  • Is the simulation hypothesis true? If so, is there a base reality?
  • Why do we know we're not a Boltzmann brain?
  • Is exist
... (read more)
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2Richard_Kennaway
In this comment I will try and write the most boring possible reply to these questions. 😊 These are pretty much my real replies. "Ours not to reason why, ours but to do or do not, there is no try." Someone must. We happen to be among them. A few lottery tickets do win, owned by ordinary people who are perfectly capable of correctly believing that they have won. Everyone should be smart enough to collect on a winning ticket, and to grapple with living in interesting (i.e. low-probability) times. Just update already. It is false. This is base reality. But I can still appreciate Eliezer's fiction on the subject. The absurdity heuristic. I don't take BBs seriously. Even in classical physics there is no observation without interaction. Beyond that, no, however many quantum physicists interpret their findings to the public with those words, or even to each other. Not that I know of. (This is not the same as a flat "no", but for most purposes rounds off to that.) Either nothing in the case of x-risk, nothing of interest in the case of a final singleton, or wonders far beyond our contemplation, which may not even involve anything we would recognise as "computing". By definition, I can't say what that would be like, beyond guessing that at some point in the future it would stand in a similar relation to the present that our present does to prehistoric times. Look around you. Is this utopia? Then that future won't be either. But like the present, it will be worth having got to. Consider a suitable version of The Agnostic Prayer inserted here against the possibility that there are Powers Outside the Matrix who may chance to see this. Hey there! I wouldn't say no to having all the aches and pains of this body fixed, for starters. Radical uplift, we'd have to talk about first.
[-]Alexander Gietelink Oldenziel132

The Vibes of Mathematics:

Q: What is it like to understand advanced mathematics? Does it feel analogous to having mastery of another language like in programming or linguistics?

A: It's like being stranded on a tropical island where all your needs are met, the weather is always perfect, and life is wonderful.

Except nobody wants to hear about it at parties.

Vibes of Maths: Convergence and Divergence

level 0: A state of ignorance.  you live in a pre-formal mindset. You don't know how to formalize things. You don't even know what it would even mean 'to prove something mathematically'. This is perhaps the longest. It is the default state of a human. Most anti-theory sentiment comes from this state. Since you've neve

You can't productively read Math books. You often decry that these mathematicians make books way too hard to read. If they only would take the time to explain things simply you would understand. 

level 1 : all math is amorphous blob

You know the basic of writing an epsilon-delta proof. Although you don't know why the rules of maths are this or that way you can at least follow the recipes. You can follow simple short proofs, albeit slowly. 

You know there are differen... (read more)

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3Daniel Murfet
  You seem to do OK...  This is an interesting one. I field this comment quite often from undergraduates, and it's hard to carve out enough quiet space in a conversation to explain what they're doing wrong. In a way the proliferation of math on YouTube might be exacerbating this hard step from tourist to troubadour.
[-]PhilGoetz143

I say that knowing particular kinds of math, the kind that let you model the world more-precisely, and that give you a theory of error, isn't like knowing another language.  It's like knowing language at all.  Learning these types of math gives you as much of an effective intelligence boost over people who don't, as learning a spoken language gives you above people who don't know any language (e.g., many deaf-mutes in earlier times).

The kinds of math I mean include:

  • how to count things in an unbiased manner; the methodology of polls and other data-gathering
  • how to actually make a claim, as opposed to what most people do, which is to make a claim that's useless because it lacks quantification or quantifiers
    • A good example of this is the claims in the IPCC 2015 report that I wrote some comments on recently.  Most of them say things like, "Global warming will make X worse", where you already know that OF COURSE global warming will make X worse, but you only care how much worse.
    • More generally, any claim of the type "All X are Y" or "No X are Y", e.g., "Capitalists exploit the working class", shouldn't be considered claims at all, and can accomplish nothing except foment arg
... (read more)
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1Mo Putera
Thanks for writing this. I only wish it was longer.
[-]Alexander Gietelink Oldenziel60

Agent Foundations Reading List [Living Document]
This is a stub for a living document on a reading list for Agent Foundations. 

Causality

Book of Why, Causality - Pearl

Probability theory 
Logic of Science - Jaynes

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[-]Alexander Gietelink Oldenziel20

Ambiguous Counterfactuals

[Thanks to Matthias Georg Mayer for pointing me towards ambiguous counterfactuals]

Salary is a function of eXperience and Education

We have a candidate  with given salary, experience  and education .

Their current salary is given by 


We 'd like to consider the counterfactual where they didn't have the education . How do we evaluate their salary in this counterfactual?

This is slightly ambiguous - there are two counterfactuals:

 or  

In the second c... (read more)

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[-]Alexander Gietelink Oldenziel10

Hopfield Networks = Ising Models = Distributions over Causal models?

Given a joint probability distributions  famously there might be many 'Markov' factorizations. Each corresponds with a different causal model.

Instead of choosing a particular one we might have a distribution of beliefs over these different causal models. This feels basically like a Hopfield Network/ Ising Model. 

You have a distribution over nodes and an 'interaction' distribution over edges. 

The distribution over nodes corresponds to the joint probability di... (read more)

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[-]Alexander Gietelink Oldenziel20

Insights as Islands of Abductive Percolation?

I've been fascinated by this beautiful paper by Viteri & DeDeo. 

What is a mathematical insight? We feel intuitively that proving a difficult theorem requires discovering one or more key insights. Before we get into what the Dedeo-Viteri paper has to say about (mathematical) insights let me recall some basic observations on the nature of insights:

(see also my previous shortform)

  • There might be a unique decomposition, akin to prime factorization. Alternatively, there might many roads to Rome: some theorems
... (read more)
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