= 27d567bc2d48d9f40e9ccc20ea370b15)
SarahC comments on Existential Risk and Public Relations - Less Wrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (613)
I am one of those who haven't been convinced by the SIAI line. I have two main objections.
First, EY is concerned about risks due to technologies that have not yet been developed; as far as I know, there is no reliable way to make predictions about the likelihood of the development of new technologies. (This is also the basis of my skepticism about cryonics.) If you're going to say "Technology X is likely to be developed" then I'd like to see your prediction mechanism and whether it's worked in the past.
Second, shouldn't an organization worried about the dangers of AI be very closely in touch with AI researchers in computer science departments? Sure, there's room for pure philosophy and mathematics, but you'd need some grounding in actual AI to understand what future AIs are likely to do.
I think multifoliaterose is right that there's a PR problem, but it's not just a PR problem. It seems, unfortunately, to be a problem with having enough justification for claims, and a problem with connecting to the world of professional science. I think the PR problems arise from being too disconnected from the demands placed on other scientific or science policy organizations. People who study other risks, say epidemic disease, have to get peer-reviewed, they have to get government funding -- their ideas need to pass a round of rigorous criticism. Their PR is better by necessity.
I'm not sure what you refer to by "actual AI." There is a sub-field of academic computer science which calls itself "Artificial Intelligence," but it's not clear that this is anything more than a label, or that this field does anything more than use clever machine learning techniques to make computer programs accomplish things that once seemed to require intelligence (like playing chess, driving a car, etc.)
I'm not sure why it is a requirement that an organization concerned with the behavior of hypothetical future engineered minds would need to be in contact with these researchers.
You have to know some of their math (some of it is interesting, some not) but this does not require getting on the phone with them and asking them to explain their math, to which of course they would tell to you to RTFM instead of calling them.
Yes, the subfield of computer science is what I'm referring to.
I'm not sure that the difference between "clever machine learning techniques" and "minds" is as hard and fast as you make it. A machine that drives a car is doing one of the things a human mind does; it may, in some cases, do it through a process that's structurally similar to the way the human mind does it. It seems to me that machines that can do these simple cognitive tasks are the best source of evidence we have today about hypothetical future thinking machines.
I gave the wrong impression here. I actually think that machine learning might be a good framework for thinking about how parts of the brain work, and I am very interested in studying machine learning. But I am skeptical that more than a small minority of projects where machine learning techniques have been applied to solve some concrete problem have shed any light on how (human) intelligence works.
In other words, I largely agree with Ben Goertzel's assertion that there is a fundamental difference between "narrow AI" and AI research that might eventually lead to machines capable of cognition, but I'm not sure I have good evidence for this argument.
Although one should be very, very careful not to confuse the opinions of someone like Goertzel with those of the people (currently) at SIAI, I think it's fair to say that most of them (including, in particular, Eliezer) hold a view similar to this. And this is the location -- pretty much the only important one -- of my disagreement with those folks. (Or, rather, I should say my differing impression from those folks -- to make an important distinction brought to my attention by one of the folks in question, Anna Salamon.) Most of Eliezer's claims about the importance of FAI research seem obviously true to me (to the point where I marvel at the fuss that is regularly made about them), but the one that I have not quite been able to swallow is the notion that AGI is only decades away, as opposed to a century or two. And the reason is essentially disagreement on the above point.
At first glance this may seem puzzling, since, given how much more attention is given to narrow AI by researchers, you might think that someone who believes AGI is "fundamentally different" from narrow AI might be more pessimistic about the prospect of AGI coming soon than someone (like me) who is inclined to suspect that the difference is essentially quantitative. The explanation, however, is that (from what I can tell) the former belief leads Eliezer and others at SIAI to assign (relatively) large amounts of probability mass to the scenario of a small set of people having some "insight" which allows them to suddenly invent AGI in a basement. In other words, they tend to view AGI as something like an unsolved math problem, like those on the Clay Millennium list, whereas it seems to me like a daunting engineering task analogous to colonizing Mars (or maybe Pluto).
This -- much more than all the business about fragility of value and recursive self-improvement leading to hard takeoff, which frankly always struck me as pretty obvious, though maybe there is hindsight involved here -- is the area of Eliezer's belief map that, in my opinion, could really use more public, explicit justification.
I don't think this is a good analogy. The problem of colonizing Mars is concrete. You can make a TODO list; you can carve the larger problem up into subproblems like rockets, fuel supply, life support, and so on. Nobody knows how to do that for AI.
OK, but it could still end up being like colonizing Mars if at some point someone realizes how to do that. Maybe komponisto thinks that someone will probably carve AGI in to subproblems before it is solved.
Well, it seems we disagree. Honestly, I see the problem of AGI as the fairly concrete one of assembling an appropriate collection of thousands-to-millions of "narrow AI" subcomponents.
Perhaps another way to put it would be that I suspect the Kolmogorov complexity of any AGI is so high that it's unlikely that the source code could be stored in a small number of human brains (at least the way the latter currently work).
EDIT: When I say "I suspect" here, of course I mean "my impression is". I don't mean to imply that I don't think this thought has occurred to the people at SIAI (though it might be nice if they could explain why they disagree).
The portion of the genome coding for brain architecture is a lot smaller than Windows 7, bit-wise.
An oddly somewhat relevant article on the information needed for specifying the brain. It is a biologist tearing a strip out of kurzweil for suggesting that we'll be able reverse engineer the human brain in a decade by looking at the genome.
P.Z. is misreading a quote from a secondhand report. Kurzweil is not talking about reading out the genome and simulating the brain from that, but about using improvements in neuroimaging to inform input-output models of brain regions. The genome point is just an indicator of the limited number of component types involved, which helps to constrain estimates of difficulty.
Edit: Kurzweil has now replied, more or less along the lines above.
Obviously the genome alone doesn't build a brain. I wonder how many "bits" I should add on for the normal environment that's also required (in terms of how much additional complexity is needed to get the first artificial mind that can learn about the world given additional sensory-like inputs). Probably not too many.
Thanks, this is useful to know. Will revise beliefs accordingly.
What do you think you know and how do you think you know it? Let's say you have a thousand narrow AI subcomponents. (Millions = implausible due to genome size, as Carl Shulman points out.) Then what happens, besides "then a miracle occurs"?
What happens is that the machine has so many different abilities (playing chess and walking and making airline reservations and...) that its cumulative effect on its environment is comparable to a human's or greater; in contrast to the previous version with 900 components, which was only capable of responding to the environment on the level of a chess-playing, web-searching squirrel.
This view arises from what I understand about the "modular" nature of the human brain: we think we're a single entity that is "flexible enough" to think about lots of different things, but in reality our brains consist of a whole bunch of highly specialized "modules", each able to do some single specific thing.
Now, to head off the "Fly Q" objection, Iet me point out that I'm not at all suggesting that an AGI has to be designed like a human brain. Instead, I'm "arguing" (expressing my perception) that the human brain's general intelligence isn't a miracle: intelligence really is what inevitably happens when you string zillions of neurons together in response to some optimization pressure. And the "zillions" part is crucial.
(Whoever downvoted the grandparent was being needlessly harsh. Why in the world should I self-censor here? I'm just expressing my epistemic state, and I've even made it clear that I don't believe I have information that SIAI folks don't, or am being more rational than they are.)
If a thousand species in nature with a thousand different abilities were to cooperate, would they equal the capabilities of a human? If not, what else is missing?
The brain has many different components with specializations, but the largest and human dominant portion, the cortex, is not really specialized at all in the way you outline.
The cortex is no more specialized than your hard drive.
Its composed of a single repeating structure and associated learning algorithm that appears to be universal. The functional specializations that appear in the adult brain arise due to topological wiring proximity to the relevant sensory and motor connections. The V1 region is not hard-wired to perform mathematically optimal gabor-like edge filters. It automatically evolves into this configuration because it is the optimal configuration for modelling the input data at that layer, and it evolves thus soley based on exposure to said input data from retinal ganglion cells.
You can think of cortical tissue as a biological 'neuronium'. It has a semi-magical emergent capacity to self-organize into an appropriate set of feature detectors based on what its wired to. more on this
All that being said, the inter-regional wiring itself is currently less understood and is probably more genetically predetermined.
There may be other approaches that are significantly simpler (that we haven't yet found, obviously). Assuming AGI happens, it will have been a race between the specific (type of) path you imagine, and every other alternative you didn't think of. In other words, you think you have an upper bound on how much time/expense it will take.
I'm not a member of SIAI but my reason for thinking that AGI is not just going to be like lots of narrow bits of AI stuck together is that I can see interesting systems that haven't been fully explored (due to difficulty of exploration). These types of systems might solve some of the open problems not addressed by narrow AI.
These are problems such as
Now I also doubt that these systems will develop quickly when people get around to investigating them. And they will have elements of traditional narrow AI in as well, but they will be changeable/adaptable parts of the system, not fixed sub-components. What I think needs is exploring is primarily changes in software life-cycles rather than a change in the nature of the software itself.
I don't think AGI in a few decades is very farfetched at all. There's a heckuvalot of neuroscience being done right now (the Society for Neuroscience has 40,000 members), and while it's probably true that much of that research is concerned most directly with mere biological "implementation details" and not with "underlying algorithms" of intelligence, it is difficult for me to imagine that there will still be no significant insights into the AGI problem after 3 or 4 more decades of this amount of neuroscience research.
Of course there will be significant insights into the AGI problem over the coming decades -- probably many of them. My point was that I don't see AGI as hard because of a lack of insights; I see it as hard because it will require vast amounts of "ordinary" intellectual labor.
I'm having trouble understanding how exactly you think the AGI problem is different from any really hard math problem. Take P != NP, for instance the attempted proof that's been making the rounds on various blogs. If you've skimmed any of the discussion you can see that even this attempted proof piggybacks on "vast amounts of 'ordinary' intellectual labor," largely consisting of mapping out various complexity classes and their properties and relations. There's probably been at least 30 years of complexity theory research required to make that proof attempt even possible.
I think you might be able to argue that even if we had an excellent theoretical model of an AGI, that the engineering effort required to actually implement it might be substantial and require several decades of work (e.g. Von Neumann architecture isn't suitable for AGI implementation, so a great deal of computer engineering has to be done).
If this is your position, I think you might have a point, but I still don't see how the effort is going to take 1 or 2 centuries. A century is a loooong time. A century ago humans barely had powered flight.
I think the following quote is illustrative of the problems facing the field:
-Marvin Minsky, quoted in "AI" by Daniel Crevier.
Some notes and interpretation of this comment:
I think this kind of observation justifies AI-timeframes on the order of centuries.
Edge detection is rather trivial. Visual recognition however is not, and there certainly are benchmarks and comparable results in that field. Have you browsed the recent pubs of Poggio et al at MIT vision lab? There is lots of recent progress, with results matching human levels for quick recognition tasks.
Also, vision is not a tiny part of intelligence. Its the single largest functional component of the cortex, by far. The cortex uses the same essential low-level optimization algorithm everywhere, so understanding vision at the detailed level is a good step towards understanding the whole thing.
And finally and most relevant for AGI, the higher visual regions also give us the capacity for visualization and are critical for higher creative intelligence. Literally all scientific discovery and progress depends on this system.
"visualization is the key to enlightenment" and all that
the visual system
By no means do I want to downplay the difficulty of P vs NP; all the same, I think we have different meanings of "vast" in mind.
The way I think about it is: think of all the intermediate levels of technological development that exist between what we have now and outright Singularity. I would only be half-joking if I said that we ought to have flying cars before we have AGI. There are of course more important examples of technologies that seem easier than AGI, but which themselves seem decades away. Repair of spinal cord injuries; artificial vision; useful quantum computers (or an understanding of their impossibility); cures for the numerous cancers; revival of cryonics patients; weather control. (Some of these, such as vision, are arguably sub-problems of AGI: problems that would have to be solved in the course of solving AGI.)
Actually, think of math problems if you like. Surely there are conjectures in existence now -- probably some of them already famous -- that will take mathematicians more than a century from now to prove (assuming no Singularity or intelligence enhancement before then). Is AGI significantly easier than the hardest math problems around now? This isn't my impression -- indeed, it looks to me more analogous to problems that are considered "hopeless", like the "problem" of classifying all groups, say.
I hate to go all existence proofy on you, but we have an existence proof of a general intelligence - accidentally sneezed out by natural selection, no less, which has severe trouble building freely rotating wheels - and no existence proof of a proof of P != NP. I don't know much about the field, but from what I've heard, I wouldn't be too surprised if proving P != NP is harder than building FAI for the unaided human mind. I wonder if Scott Aaronson would agree with me on that, even though neither of us understand the other's field? (I just wrote him an email and asked, actually; and this time remembered not to say my opinion before asking for his.)
Why is AGI a math problem? What is abstract about it?
We don't need math proofs to know if AGI is possible. It is, the brain is living proof.
We don't need math proofs to know how to build AGI - we can reverse engineer the brain.
...but you don't really know - right?
You can't say with much confidence that there's no AIXI-shaped magic bullet.
That's right; I'm not an expert in AI. Hence I am describing my impressions, not my fully Aumannized Bayesian beliefs.
AIXI-shaped magic bullet?
AIXI's contribution is more philosophical than practical. I find a depressing over-emphasis of bayesian probability theory here as the 'math' of choice vs computational complexity theory, which is the proper domain.
The most likely outcome of a math breakthrough will be some rough lower and or upper bounds on the shape of the intelligence over space/time complexity function. And right now the most likely bet seems to be that the brain is pretty well optimized at the circuit level, and that the best we can do is reverse engineer it.
EY and the math folk here reach a very different conclusion, but I have yet to find his well considered justification. I suspect that the major reason the mainstream AI community doesn't subscribe to SIAI's math magic bullet theory is that they hold the same position outline above: ie that when we get the math theorems, all they will show is what we already suspect: human level intelligence requires X memory bits and Y bit ops/second, where X and Y are roughly close to brain levels.
This, if true, kills the entirety of the software recursive self-improvement theory. The best that software can do is approach the theoretical optimum complexity class for the problem, and then after that point all one can do is fix it into hardware for a further large constant gain.
I explore this a little more here
The article linked to in the parent is entitled:
"Created in the Likeness of the Human Mind: Why Strong AI will necessarily be like us"
Good quality general-purpose data-compression would "break the back" of the task of buliding synthetic intelligent agents - and that's a "simple" math problem - as I explain on: http://timtyler.org/sequence_prediction/
At least it can be stated very concisely. Solutions so far haven't been very simple - but the brain's architecture offers considerable hope for a relatively simple solution.
That seems like crazy talk to me. The brain is not optimal - not its hardware or software - and not by a looooong way! Computers have already steam-rollered its memory and arithmetic -units - and that happened before we even had nanotechonolgy computing components. The rest of the brain seems likely to follow.
Note that allowing for a possibility of sudden breakthrough is also an antiprediction, not a claim for a particular way things are. You can't know that no such thing is possible, without having understanding of the solution already at hand, hence you must accept the risk. It's also possible that it'll take a long time.
I'm reading through and catching up on this thread, and rather strongly agreed with your statement:
However, pondering it again, I realize there is an epistemological spectrum ranging from math on the one side to engineering on the other. Key insights into new algorithms can undoubtedly speed up progress, and such new insights often can be expressed as pure math, but at the end of the day it is a grand engineering (or reverse engineering) challenge.
However, I'm somewhat taken aback when you say, "the notion that AGI is only decades away, as opposed to a century or two."
A century or two?
One obvious piece of evidence is that many forms of narrow learning are mathematically incapable of doing much. There are for example a whole host of theorems about what different classes of neural networks can actually recognize, and the results aren't very impressive. Similarly, support vector machine's have a lot of trouble learning anything that isn't a very simple statistical model, and even then humans need to decide which stats are relevant. Other linear classifiers run into similar problems.
I work in this field, and was under approximately the opposite impression; that voice and visual recognition are rapidly approaching human levels. If I'm wrong and there are sharp limits, I'd like to know. Thanks!
Machine intelligence has surpassed "human level" in a number of narrow domains. Already, humans can't manipulate enough data to do anything remotely like a search engine or a stockbot can do.
The claim seems to be that in narrow domains there are often domain-specific "tricks" - that wind up not having much to do with general intelligence - e.g. see chess and go. This seems true - but narrow projects often broaden out. Search engines and stockbots really need to read and understand the web. The pressure to develop general intelligence in those domains seems pretty strong.
Those who make a big deal about the distinction between their projects and "mere" expert systems are probably mostly trying to market their projects before they are really experts at anything.
One of my videos discusses the issue of whether the path to superintelligent machines will be "broad" or "narrow":
http://alife.co.uk/essays/on_general_machine_intelligence_strategies/
Thanks, it always is good to actually have input from people who work in a given field. So please correct me if I'm wrong but I'm under the impression that
1) neutral networks cannot in general detect connected components unless the network has some form of recursion. 2) No one knows how to make a neural network with recursion learn in any effective, marginally predictable fashion.
This is the sort of thing I was thinking of. Am I wrong about 1 or 2?
Not sure what you mean about by 1), but certainly, recurrent neural nets are more powerful. 2) is no longer true; see for example the GeneRec algorithm. It does something much like backpropagation, but with no derivatives explicitly calculated, there's no concern with recurrent loops.
On the whole, neural net research has slowed dramatically based on the common view you've expressed; but progress continues apace, and they are not far behind cutting edge vision and speech processing algorithms, while working much more like the brain does.
Thanks. GeneRec sounds very interesting. Will take a look. Regarding 1, I was thinking of something like the theorems in chapter 9 in Perceptrons which shows that there are strong limits on what topological features of input a non-recursive neural net can recognize.
There needs to be an article on this point. In the absence of a really good way of deciding what technologies are likely to be developed, you are still making a decision. You haven't signed up yet; whether you like it or not, that is a decision. And it's a decision that only makes sense if you think technology X is unlikely to be developed, so I'd like to see your prediction mechanism and whether it's worked in the past. In the absence of really good information, we sometimes have to decide on the information we have.
EDIT: I was thinking about cryonics when I wrote this, though the argument generalizes.
My point, with this, is that everybody is risk-averse and everybody has a time preference. The less is known about the prospects of a future technology, the less willing people are to invest resources into ventures that depend on the future development of that technology. (Whether to take advantage of the technology -- as in cryonics -- or to mitigate its dangers -- as in FAI.) Also, the farther in the future the technology is, the less people care about it; we're not willing to spend much to achieve benefits or forestall risks in the far future.
I don't think it's reasonable to expect people to change these ordinary features of economic preference. If you're going to ask people to chip in to your cause, and the time horizon is too far, or the uncertainty too high, they're not going to want to spend their resources that way. And they'll be justified.
Note: yes, there ought to be some magnitude of benefit or cost that overcomes both risk aversion and time preference. Maybe you're going to argue that existential risk and cryonics are issues of such great magnitude that they outweigh both risk aversion and time preference.
But: first of all, the importance of the benefit or cost is also an unknown (and indeed subjective.) How much do you value being alive? And, second of all, nobody says our risk and time preferences are well-behaved. There may be a date so far in the future that I don't care about anything that happens then, no matter how good or how bad. There may be loss aversion -- an amount of money that I'm not willing to risk losing, no matter how good the upside. I've seen some experimental evidence that this is common.
From what I understand this applies to most people but not everyone, especially outside of contrived laboratory circumstances. Overconfidence and ambition essentially amount to risk-loving choices for some major life choices.
What is it that is making you think that whatever SarahC hasn't "signed up" to is having a positive effect - and that she can't do something better with her resources?
Let's keep in mind that your estimated probabilities of various technological advancements occurring and your level of confidence in those estimates are completely distinct... In particular, here you seem to express low estimated probabilities of various advancements occurring, and you justify this by saying "we really have no idea". This seems like a complete non sequitur. Maybe you have a correct argument in your mind, but you're not giving us all the pieces.
If you haven't demonstrated 1 -- if it's still unknown -- you can't expect me to believe 3. The burden of proof is on whoever's asking for money for a new risk-mitigating venture, to give strong evidence that the risk is real.
So you think a danger needs to likely arrive in a few decades for it to merit attention?
I think that is quite irresponsible. No law of physics states that all problems can certainly be solved very well in a few decades (the solutions for some problems might even necessarily involve political components, btw), so starting preparations earlier can be necessary.
I see "burden of proof" as a misconcept in the same way that someone "deserving" something is. A better way of thinking about this: "You seem to be making a strong claim. Mind sharing the evidence for your claim for me? ...I disagree that the evidence you present justifies your claim."
For what it's worth, I also see "must _" as a misconcept--although "must _ to _" is not. It's an understandable usage if the "to _*" clause is implicit, but that doesn't seem true in this case. So to fix up SIAI's argument, you could say that these are the statements whose probabilities are being contested:
And depending on their probabilities, the following may or may not be true:
Pretty much anything you say that's not relevant to one of statements 1 or 2 (including statements that certain people haven't been "responsible" enough in supporting their claims) is completely irrelevant to the question of whether you want to take action Y. You already have (or ought to be able to construct) probability estimates for each of 1 and 2.
Your grasp of decision theory is rather weak if you are suggesting that when Technology X is developed is irrelevant to SarahC's decision. Similarly, you seem to suggest that the ratio of value to cost is irrelevant and that all that matters is which is bigger. Wrong again.
But your real point was not to set up a correct decision problem, but rather to suggest that her questions about whether "certain people" have been "responsible" are irrelevant. Well, I have to disagree. If action Y is giving money to "certain people", then their level of "responsibility" is very relevant.
I did enjoy your observations regarding "burden of proof" and "must", though probably not as much as you did.
Of course that is important. I didn't want to include a lot of qualifiers.
I'm not trying to make a bulletproof argument so much as concisely give you an idea of why I think SarahC's argument is malformed. My thinking is that should be enough for intellectually honest readers, as I don't have important insights to offer beyond the concise summary. If you think I ought to write longer posts with more qualifications for readers who aren't good at taking ideas seriously feel free to say that.
Really? So in some circumstances it is rational to take an action for which the expected cost is greater than the expected value? Or it is irrational to take an action for which the expected value exceeds the expected cost? (I'm using "rational" to mean "expected utility maximizing", "cost" to refer to negative utility, and "value" to refer to positive utility--hopefully at this point my thought process is transparent.)
It would be a well-formed argument to say that because SIAI folks make strong claims without justifying them, they won't use money SarahC donates well. As far as I can tell, SarahC has not explicitly made that argument. (Recall I said that she might have a correct argument in her mind but she isn't giving us all the pieces.)
Please no insults, this isn't you versus me is it?
No, your error was in the other direction. If you look back carefully, you will notice that the ratio is being calculated conditionally on Technology X being developed. Given that the cost is sunk regardless of whether the technology appears, it is possible that SarahC should not act even though the (conditionally) expected return exceeds the cost.
Shouldn't be. Nor you against her. I was catty only because I imagined that you were being catty. If you were not, then I surely apologize.
I edited my post before I saw your response :-P
I'm sorry, I don't see any edits that matter for the logic of the thread. What am I missing?
OK, my mistake.
I didn't say what SarahC should do with the probabilities once she had them. All I said was that they were pretty much all was relevant to the question of whether she should donate. Unless I didn't, in which case I meant to.
Prediction is hard, especially about the future.
One thing that intrigues me is snags. Did anyone predict how hard to would be to improve batteries, especially batteries big enough for cars?
I think there are ways to make these predictions. On the most layman level I would point out that IBM build a robot that beats people at Jeopardy. Yes, I am aware that this is a complete machine-learning hack (this is what I could gather from the NYT coverage) and is not true cognition, but it surprised even me (I do know something about ML). I think this is useful to defeat the intuition of "machines cannot do that". If you are truly interested I think you can (I know you're capable) read Norvig's AI book, and than follow up on the parts of it that most resemble human cognition; I think serious progress is made in those areas. BTW, Norvig does take FAI issues seriously, including a reference to EY paper in the book.
I think they should, I have no idea if this is being done; but if I would do it I would not do it publicly, as it may have very counterproductive consequences. So until you or I become SIAI fellows we will not know, and I cannot hold such lack of knowledge against them.
First, I'm not really claiming "machines cannot do that." I can see advances in machine learning and I can imagine the next round of advances being pretty exciting. But I'm thinking in terms of maybe someday a machine being able to distinguish foreground from background, or understand a sentence in English, not being a superintelligence that controls Earth's destiny. The scales are completely different. One scale is reasonable; one strains credibility, I'm afraid.
Thanks for the book recommendation; I'll be sure to check it out.
I think controlling Earth's destiny is only modestly harder than understanding a sentence in English - in the same sense that I think Einstein was only modestly smarter than George W. Bush. EY makes a similar point.
You sound to me like someone saying, sixty years ago: "Maybe some day a computer will be able to play a legal game of chess - but simultaneously defeating multiple grandmasters, that strains credibility, I'm afraid." But it only took a few decades to get from point A to point B. I doubt that going from "understanding English" to "controlling the Earth" will take that long.
Well said. I shall have to try to remember that tagline.
There's a problem with it, though. Some decades ago you'd have just as eagerly subscribed to this statement: "Controlling Earth's destiny is only modestly harder than playing a good game of chess", which we now know to be almost certainly false.
I agree with Rain. Understanding implies a much deeper model than playing. To make the comparison to chess, you would have to change it to something like, "Controlling Earth's destiny is only modestly harder than making something that can learn chess, or any other board game, without that game's mechanics (or any mapping from the computer's output to game moves) being hard-coded, and then play it at an expert level."
Not obviously false, I think.
It's the word "understanding" in the quote which makes it presume general intelligence and/or consciousness without directly stating it. The word "playing" does not have such a connotation, at least to me. I don't know if I would think differently back when chess required intelligence.
Hey, remember this tagline: "I think controlling Earth's destiny is only modestly harder than understanding a sentence in English."
(Again:) Hey, remember this tagline: "I think controlling Earth's destiny is only modestly harder than understanding a sentence in English."
I agree completely. The reason why I framed my top level post in the way that I did was so that it would be relevant to readers of a variety of levels of confidence in SIAI's claims.
As I indicate here, I personally wouldn't be interested in funding SIAI as presently constituted even if there was no PR problem.
As was mentioned in other threads, SIAI's main arguments rely on disjunctions and antipredictions more than conjunctions and predictions. That is, if several technology scenarios lead to the same broad outcome, that's a much stronger claim than one very detailed scenario.
For instance, the claim that AI presents a special category of existential risk is supported by such a disjunction. There are several technologies today which we know would be very dangerous with the right clever 'recipe'– we can make simple molecular nanotech machines, we can engineer custom viruses, we can hack into some very sensitive or essential computer systems, etc. What these all imply is that a much smarter agent with a lot of computing power is a severe existential threat if it chooses to be.
Yes. It's hardly urgent, since AI researchers are nowhere near a runaway intelligence. But on the other hand, control of AI is going to be crucial+difficult eventually, and it would be good for researchers to be aware of it, if they aren't.
Sadly, there's no guarantee of that.
Right, it's just (in my and most other AI researchers'[*] opinion) overwhelmingly likely that we are in fact nowhere near (the capability of) it. Although it's interesting to me that I don't feel there's that much difference in probability of "(good enough to) run away improving itself quickly past human level AI" in the next year, and in the next 10 years - both extremely close to 0 is the most specific I can be at this point. That suggests I haven't really quantified my beliefs exactly yet.
[*] I actually only work on natural language processing using really dumb machine learning, i.e. not general AI.