Is there any good write up on the gut/brain connection and the effect fecal transplants?
Watching the South Park episode where everyone tries to steal Tom Brady's poo got me wondering why this isn't actually a thing. I can imagine lots of possible explanations, ranging from "because it doesn't have much of an effect if you're healthy" to "because FDA".
On this view, adversarial examples arise from gradient descent being "too smart", not "too dumb": the program is fine; if the test suite didn't imply the behavior we wanted, that's our problem.
Shouldn't we expect to see RL models trained purely on self play not to have these issues then?
My understanding is that even models trained primarily with self play, such as katago, are vulnurable to adversarial attacks. If RL models are vulnurable to the same type of adversarial attacks, isn't that evidence against this theory?
The amount of inference compute isn't baked-in at pretraining time, so there is no tradeoff.
This doesn't make sense to me.
In a subscription based model, for example, companies would want to provide users the strongest completions for the least amount of compute.
If they estimate customers in total will use 1 quadrillion tokens before the release of their next model, they have to decide how much of the compute they are going to be dedicating to training versus inference. As one changes the parameters (subscription price, anticipated users, fixed costs for a ...
With the release of openAI o1, I want to ask a question I've been wondering about for a few months.
Like the chinchilla paper, which estimated the optimal ratio of data to compute, are there any similar estimates for the optimal ratio of compute to spend on inference vs training?
In the release they show this chart:
The chart somewhat gets at what I want to know, but doesn't answer it completely. How much additional inference compute would I need a 1e25 o1-like model to perform as well as a one shotted 1e26?
Additionally, for some x number of queries, what is ...
I agree the conclusion isn't great!
Not so surprisingly, many people read the last section as an endorsement of some version of "RCTism", but it's not actually a view I endorse myself.
What I really wanted to get at in this post was just how pervasive priors are, and how difficult it is to see past them.
Just played through it tonight. This was my first D&D.Sci, found it quite difficult and learned a a few things while working on it.
Initially I tried to figure out the best counters and found a few patterns (flamethrowers were especially good against certain units). I then tried to look and adjust for any chronology, but after tinkering around for a while without getting anywhere I gave up on that. Eventually I just went with a pretty brainless ML approach.
I ended up sending squads for 5 and 6 which managed a 13.89% and 53.15% chance of surviving, I think it's good I'm not in charge of any soldiers in real life!
Overall I had good fun, and I'm looking forward to looking at the next one.
This wouldn't be the first time Deepmind pulled these shenanigans.
My impression of Deepmind is they like playing up the impressiveness of their achievements to give an impression of having 'solved' some issue, never saying anything technically false, while suspiciously leaving out relevant information and failing to do obvious tests of their models which would reveal a less impressive achievement.
For Alphastar they claimed 'grandmaster' level, but didn't show any easily available stats which would make it possible to verify. As someone who was in Gra...
DeepMind's no-search chess engine is surely the furthest anyone has gotten without search.
This is quite possibly not true! The cutting-edge Lc0 networks (BT3/BT4, T3) have much stronger policy and value than the AlphaZero networks, and the Lc0 team fairly regularly make claims of "grandmaster" policy strength.
There's countries where cooperative firms are doing fine. Most of Denmark's supermarket chains are owned by the cooperative coop. Denmark's largest dairy producer Arla is a cooperative too. Both operate in a free market and are out-competing privately owned competitors.
Both also resort to many of the same dirty tricks traditionally structured firms are pulling. Arla, for example, has done tremendous harm to the plant-based industry through aggressive lobbying. Structuring firms as cooperatives doesn't magically make them aligned.
Actually this modification shouldn't matter. After looking into the definition of pass-alive, the dead stones in the adversarial attacks are clearly not pass-alive.
Under both unmodified and pass-alive modified tromp-taylor rules, KataGo would lose here and its surprising that self-play left such a weakness.
The authors are definitely onto something, and my original claim that the attack only works due to kataGo being trained under a different rule-set is incorrect.
No, the KataGo paper explicitly states at the start of page 4:
"Self play games used Tromp-Taylor rules [21] modified to not require capturing stones within pass-aliveterritory"
Had KataGo been trained on unmodified Tromp-Taylor rules, the attack would not have worked. The attack only works because the authors are having KataGo play under a different ruleset than it was trained on.
If I have the details right, I am honestly very confused about what the authors are trying to prove with this paper. Given their Twitter announcement claimed that the rulesets were...
Actually this modification shouldn't matter. After looking into the definition of pass-alive, the dead stones in the adversarial attacks are clearly not pass-alive.
Under both unmodified and pass-alive modified tromp-taylor rules, KataGo would lose here and its surprising that self-play left such a weakness.
The authors are definitely onto something, and my original claim that the attack only works due to kataGo being trained under a different rule-set is incorrect.
As someone who plays a lot of go, this result looks very suspicious to me. To me it looks like the primary reason this attack works is due to an artifact of the automatic scoring system used in the attack. I don't think this attack would be replicable in other games, or even KataGo trained on a correct implementation.
In the example included on the website, KataGo (White) is passing because it correctly identifies the adversary's (Black) stones as dead meaning the entire outside would be its territory. Playing any move in KataGo's position would gain no poi...
Evaluating the RCT is a chance to train the evaluation-muscle in a well-defined domain with feedback. I've generally found that the people who are best at evaluations in RCT'able domains, are better at evaluating the hard-to-evaluate claims as well.
Often the difficult to evaluate domains have ways of getting feedback, but if you're not in the habit of looking for it, you're less likely to find the creative ways to get data.
I think a much more common failure mode within this community, is that we get way overconfident beliefs about hard-to-evaluate domains, because there aren't many feedback loops and we aren't in the habit of looking for them.
Another issue with greenwashing and safetywashing is that it gives people who earnestly care a false impression that they are meaningfully contributing.
Despite thousands of green initiatives, we're likely to blow way past the 1.5c mark because the far majority of those initiatives failed to address the core causes of climate change. Each plastic-straw ban and reusable diaper gives people an incorrect impression that they are doing something meaningful to improve the climate.
Similarly I worry that many people will convince themselves that they are doing som...
we should be very sceptical of interventions whose first-order effects aren't promising.
This seems reasonable, but I think this suspicion is currently applied too liberally. In general, it seems like second-order effects are often very large. For instance, some AI safety research is currently funded by a billionaire whose path to impact on AI safety was to start a cryptocurrency exchange. I've written about the general distaste for diffuse effects and how that might be damaging here; if you disagree I'd love to hear your response.
In general, I don't think ...
I’m worried about this too, especially since I think it’s surprisingly easy here (relative to most fields/goals) to accidentally make the situation even worse. For example, my sense is people often mistakenly conclude that working on capabilities will help with safety somehow, just because an org's leadership pays lip service to safety concerns—even if the org only spends a small fraction of its attention/resources on safety work, actively tries to advance SOTA, etc.
The primary question on my mind is something like this:
How much retraining is needed for Gato to learn a new task? Given a task, such as "Stack objects and compose a relevant poem" which combines skills it has already learned, yet is a fundamentally different task, does it quickly learn how to perform well at it?
If not, then it seems Deepmind 'merely' managed to get a single agent to do a bunch of tasks we were previously only able to do with multiple agents. If it is also quicker at learning new tasks in similar domains, than an agent trained solely to do it, then it seems like a big step towards general intelligence.
I think wife rolls of the tongue uniquely well here due to 'wife' rhyming with 'life', creating the pun. Outside of that I don't buy it. In Denmark, wife-jokes are common despite wife being a two syllable word (kone) and husband-jokes are rare despite husband being a one syllable word (mand).
My model of why we see this has much more to do with gender norms and normalised misogyny than with catchiness of the words.
For what it's worth hastily made a spreadsheet and found that regular heavy exercise was by far the largest improvement I could make to my life expectancy. Everything else paled in comparison. That said I only evaluated interventions that were relevant to me. If you smoke, I imagine quitting would score high as well.
For me, this perfectly hits the nail on the head.
This is a somewhat weird question, but like, how do I do that?
I've noticed multiple communities fall into the meta-trap, and even when members notice it can be difficult to escape. While the solution is simply to "stop being meta", that is much harder said than done.
When I noticed this happening in a community I am central in organizing I pushed back by bringing my own focus to output instead of process hoping others would follow suit. This has worked somewhat and we're definitely on a better track. I wonder what dynamics lead to this 'death by meta' syndrome, and if there is a cure.
Really cool concept of drumming with your feet while playing another instrument.
I think it would be really cool to experiment with different trigger sounds. The muscles in your foot severely limits the nuances available to play, and trying to imitate the sounds of a regular drum-set will not go over well.
I think it is possible to achieve much cooler playing, if you skip the idea of your pedals needing to imitate a drum-set entirely. Experiment with some 808 bass, electric kicks, etc.
Combining that with your great piano playing would create an entirely new feel of music, whereas it can easily end up sounding like a good pianist struggling to cooperate with a much worse drummer
I spent 5 minutes searching amazon.de for replacements to the various products recommended and my search came up empty.
Is there someone who has put together the needed list of bright lighting products on amazon.de? I tried doing it myself and ended up hopelessly confused. What I'm asking for is eg. two desk lamps and corresponding light bulbs that live up to the criteria.
I'll pay $50 to the charity of your choice, if I make a purchase based off your list.
And there doesn’t need to be an “overall goodness” of the job that would be anything else than just the combination of those two facts.
There needs to be an "overall goodness" that is exactly equal to the combination of those two facts. I really like the fundamental insight of the post. It's important to recognize that your mind wants to push your perception of the "overall goodness" to the extremes, and that you shouldn't let it do that.
If you now had to make a decision on whether to take the job, how would you use this electrifying zap help you make the decision?
For those who, like me, have the attention span and intelligence of a door hinge the ELI5 edition is:
Outer alignment is trying to find a reward function that is aligned with our values (making it produce good stuff rather than paperclips)
Inner alignment is the act of ensuring our AI actually optimizes the reward function we specify.
An example of poor inner alignment would be us humans in the eyes of evolution. Instead of doing what evolution intended, we use contraceptives so we can have sex without procreation. If evolution had gotten its inner alignment right, we would care as much about spreading our genes as evolution does!
GPT-3's goal is to accurately predict a text sequence. Whether GPT-3 is capable of reason, or whether we can get it to explicitly reason is two different questions.
If I had you read Randall Munroe's book "what if" but tore out one page and asked you to predict what will be written as the answer, there's a few good strategies that come to mind.
One strategy would be to pick random verbs and nouns from previous questions and hope some of them will be relevant for this question as well. This strategy will certainly do better than if yo...
I don't get the divestment argument, please help me understand why I'm wrong.
Here's how I understand it:
If Bob offers to pay Alice whatever Evil-Corp™ would have paid in stock dividends in exchange for what Alice would have paid for an Evil-Corp™ stock, Evil-Corp™ has to find another buyer. Since Alice was the buyer willing to pay the most, Evil-Corp™ now loses the difference between what Alice was willing to pay and the next-most willing buyer, Eve, is willing to pay.
Is that understanding correct, or am I missing...
So I think the divestment argument that Buck is making is the following:
Assume there are 25 investors, from Alice to Ysabel. Each investor is risk-averse, and so is willing to give up a bit of expected value in exchange for reduced variance, and the more anticorrelated their holdings, the less variance they'll have. This means Alice is willing to pay more for her first share of EvilCorp stock than she is for her second share, and so on; suppose EvilCorp has 100 shares, and the equilibrium is that each investor has 4 shares.
Suppose now Alice decides th...
I think that's a very fair way to put it, yes. One way this becomes very apparent, is that you can have a conversation with a starcraft player while he's playing. It will be clear the player is not paying you his full attention at particularly demanding moments, however.
Novel strategies are thought up inbetween games and refined through dozens of practice games. In the end you have a mental decision tree of how to respond to most situations that could arise. Without having played much chess, I imagine this is how people do chess openers do as wel...
I think the abstract question of how to cognitively manage a "large action space" and "fog of war" is central here.
In some sense StarCraft could be seen as turn based, with each turn lasting for 1 microsecond, but this framing makes the action space of a beginning-to-end game *enormous*. Maybe not so enormous that a bigger data center couldn't fix it? In some sense, brute force can eventually solve ANY problem tractable to a known "vaguely O(N*log(N))" algorithm.
BUT facing "a limit that forces meta-cognition"...
Before doing the whole EA thing, I played starcraft semi-professionally. I was consistently ranked grandmaster primarily making money from coaching players of all skill levels. I also co-authored a ML paper on starcraft II win prediction.
TL;DR: Alphastar shows us what it will look like when humans are beaten in completely fair fight.
I feel fundamentally confused about a lot of the discussion surrounding alphastar. The entire APM debate feels completely misguided to me and seems to be born out of fundamental misunderstandings of what it means to be good at ...
I think your feelings stem from you considering it to be enough If AS simply beats human players while APM whiners would like AS to learn all the aspect of Starcraft skill it can reasonably be expected to learn.
The agents on ladder don't scout much and can't react accordingly. They don't tech switch midgame and some of them get utterly confused in ways a human wouldn't. Game 11 agent vs MaNa couldn't figure out it could build 1 phoenix to kill the warp prism and chose to follow it with 3 oracles (units which cant shoot at flying units). The ladder agents d
..."Science confirms video games are good" is essentially the same statement as "The bible confirms video games are bad" just with the authority changed. Luckily there remains a closer link between the authroity "Science" and truth than the authority "The bible" and truth so it's still an improvement.
Most people still update their worldview based upon whatever their tribe as agreed upon as their central authority. I'm having a hard time critisising people for doing this, however. This is something we all do! ...
I really like this line of thinking. I don't think it is necessarily opposed to the typical map-territory model, however.
You could in theory explain all there is to know about the territory with a single map, however that map would become really dense and hard to decipher. Instead having multiple maps, one with altitude, another with temperature, is instrumentally useful for best understanding the territory.
We cannot comprehend the entire territory at once, so it's instrumentally useful to view the world through different lenses and see what new ...
Believing the notion that one can 'deconfuse' themself on any topic, is an archetypal mistake of the rationalist. Only in the spirit of all things that are essential to our personal understanding, can we expect our beliefs to conform to the normality of our existence. Asserting that one can know anything certain of the physical world is, by its definition, a foolhardy pursuit only someone with a narrow and immature understanding of physicality would consider meaningful.
Believing that any 'technique' could be used to train ones mind in t...
I'll crosspost the comment I left on substack:
In Denmark the government has a service (ROFUS), which anyone can voluntarily sign up for to exclude themselves from all gambling providers operating in Denmark. You can exclude yourself for a limited duration or permanently. The decision cannot be revoked.
Before discussing whether gambling should be legal or illegal, I would encourage Americans to see how far they can get with similar initiatives first.
This exists in the US as well. Because legalized gambling is regulated by the state governments the self exclusion programs are also run by them. Here is one for the state of Massachusetts https://massgaming.com/about/voluntary-self-exclusion/
A similar service exists in the UK - https://www.gamstop.co.uk/
I don't know if "don't even discuss other methods until you've tried this first" seems right to me, but I do think such services seem pretty great, and would guess that expanding/building on them (including e.g. requiring that any gambling advertising included an ad for them) would be a lot more tractable than pursuing harder bans.
What actually works is clearly the most important thing here, but aesthetically I do like the mechanism of "give people the ability to irreversibly self exclude" as a response to predatory/addictive systems.