This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.
Optimistically, we think that engaging with people you disagree with is worth your time, and so is being nice! Pessimistically, there are many dynamics that can lead discussions on Culture War topics to become unproductive. There's a human tendency to divide along tribal lines, praising your ingroup and vilifying your outgroup - and if you think you find it easy to criticize your ingroup, then it may be that your outgroup is not who you think it is. Extremists with opposing positions can feed off each other, highlighting each other's worst points to justify their own angry rhetoric, which becomes in turn a new example of bad behavior for the other side to highlight.
We would like to avoid these negative dynamics. Accordingly, we ask that you do not use this thread for waging the Culture War. Examples of waging the Culture War:
-
Shaming.
-
Attempting to 'build consensus' or enforce ideological conformity.
-
Making sweeping generalizations to vilify a group you dislike.
-
Recruiting for a cause.
-
Posting links that could be summarized as 'Boo outgroup!' Basically, if your content is 'Can you believe what Those People did this week?' then you should either refrain from posting, or do some very patient work to contextualize and/or steel-man the relevant viewpoint.
In general, you should argue to understand, not to win. This thread is not territory to be claimed by one group or another; indeed, the aim is to have many different viewpoints represented here. Thus, we also ask that you follow some guidelines:
-
Speak plainly. Avoid sarcasm and mockery. When disagreeing with someone, state your objections explicitly.
-
Be as precise and charitable as you can. Don't paraphrase unflatteringly.
-
Don't imply that someone said something they did not say, even if you think it follows from what they said.
-
Write like everyone is reading and you want them to be included in the discussion.
On an ad hoc basis, the mods will try to compile a list of the best posts/comments from the previous week, posted in Quality Contribution threads and archived at /r/TheThread. You may nominate a comment for this list by clicking on 'report' at the bottom of the post and typing 'Actually a quality contribution' as the report reason.
Jump in the discussion.
No email address required.
Notes -
AI 2040: Plan A
The AI 2027 authors published a follow-up. Scott Alexander also wrote a separate blogpost and although not in the author list contributed.
It's a very speculative and optimistic timeline of AI's future evolution. It presents five ways or "plans" the US government will intervene. Unsurprisingly, the ASI-pilled authors favor strong, global regulation to ensure alignment. Summaries:
Plan A (recommended): the US makes an international treaty with China, pauses AI training (not inference, i.e. no new models but we keep using existing ones), enforces full transparency of future research, then when alignment research advances enough carefully resumes
Plan S: the US makes an international treaty with China and pauses AI training for as long as possible
Plan B: the US regulates AI at home and demands China also regulate, but doesn't negotiate with them, probably leading to a war
Plan C: the US regulates AI and ignores China, so they overtake it and reach ASI first
Plan D: the US doesn't regulate AI, we get ASI in early 2031 and it probably kills everyone
Personally, I just don't share the optimism of these guys in either direction.
I think politicians will prioritize culture war and the failing economy over AI regulation, and at most pass some executive orders suggesting companies be more careful. But I also doubt we'll have ASI that can solve the abstract problems "take over the world" or even "keep existing world leaders in power" (they're getting old and increasingly unpopular, their parties may remain in power but only if their policies significantly shift).
What I expect from AI:
Basically solve legacy code by rewriting entire codebases, applying very niche domain knowledge, and actually finding and handling edge-cases better than humans
Greatly speedup research, leading to new discoveries and inventions. Important but background things like food preservation and medicine will improve from AI-assisted discoveries. Major advancements in math and theoretical physics
Much better and cheaper education, therapy, initial medical/legal appointments, personal repairs...maybe reducing but not eliminating human jobs, because human experts will offer these services "premium"
Won't replace human artists. Some advertisements and infographics will be AI but even some will still be human. At best it will assist them in a way where the human still fully controls the output, e.g. by generating code leading to new and improved software tools to learn, practice, and create art
Used by the vast majority as a personal assistant, but doesn't replace human relations
Maybe someone here can help me with this.
What is the bull case, beyond drawing lines on a graph, for AI achieving superhuman, or even human, performance on tasks that are not quickly verifiable?
AI is quite clearly superhuman at self-contained programming problems. I haven't tried Fable, but I suspect that superhuman open ended software engineering is not far away, though I suspect that humans will have a role in architecture and problem setting as opposed to problem solving for some time more. I expect hardware work will also quickly go down this path, at least to some extent, and really anything that can be RLVR'd. That's enough to account for a huge portion of white collar work and carries serious cyber security risks. Both of those will have serious consequences, politically and militarily.
I am not convinced that AI is improving at anything like this rate for things that can't be RLVR'd, I.e. stuff where you can't generate enormous amounts of useful training data with an answer key. Radiologists continue to do just fine for themselves despite repeated promises of doom. I'm sure someone will chime in to say that the radiologists are there for liability reasons, but it's not as if they are now just hitting thumbs up/thumbs down on AI decisions all day.
Partly this is a sample efficiency question - there simply might not be enough data for them to learn this stuff to human level, and architectural advances that improve sample efficiency may lead to huge gains in quality. But it's not clear to me why people expect this to happen.
I am more uncertain about "superhuman" intelligence, but fairly confident on human intelligence (i.e. as best as the best humans).
My bull case: existing systems have a very significant flaw, in that they're very sample inefficient. They need way more data than a human brain does to learn the same things (don't tell me that a bunch of redundant sensory information counts as extra data). That's a fairly broad critique, applying not just to LLMs.
But we know that there exist systems--not just human but other animal brains--that are orders of magnitude more sample efficient. That suggests there is something fundamental missing from existing learning strategies.
But what existing LLMs do allow is a search over architectures and learning rules. Take a random ML paper off arXiv, and Fable will absolutely be able to implement a Jax Colab notebook for it. No one needs a PhD to do this.
Maybe they can suggest novel ideas, or better prune the combinatorial space of architectures and learning rules. That would speed things up. But that's not necessary: we can automate grad student descent, through brute force. Throw a couple trillion GPU hours at the problem, and if what allows human and animal brains to be as efficient as they are is efficiently implementable on GPUs, we will find it. And on the scale of years, not decades.
"if what allows human and animal brains to be as efficient as they are is efficiently implementable on GPUs" is the biggest question for me, but a negative answer to that just delays the inevitable. Admittedly pushing things a decade or two in the future: if GPUs are a dead end, we have our seasonal AI winter, until the switch to fancy neuromorphic hardware or neural organoids starts scaling.
More options
Context Copy link
Humans existing and being good at these problems shows that it is possible to create an intelligence that can solve these problems to at least the skill level of a highly intelligent and competent human, without needing impossibly huge training sets to do so. The question is if we can replicate this on a computer. The bull case is that this is just a question of finding the right algorithm, and once we do, we will achieve AGI.
Since current AI can clearly help researchers write code faster, it stands to reason that the better AI we have access to, the faster we can improve the algorithm, which leads to a loop where better models are developed faster and faster. Once the models start approaching human-level intelligence they will be able to iteratively improve themselves without researcher oversight. And like that, we have justified drawing lines on the graph.
It doesn't assume that -- it rests solely on the idea that brains are physical objects. This is empirically verified by every single experiment run on a human brain. More generally, it's been borne out on every noun that interacts with the physical world.
"Humans aren't computers" is irrelevant. Brains are physical arrangements of atoms that are capable of intelligently solving problems. This assumes nothing.
(For completeness: you may be completely right about 2. You're sort-of-right about 3, in that the assumption was made and the assumption was mistaken. But I don't think you're right that the current approach avoids singularity. There are absolutely recursive feedback loops in improving the current implementation of AI, because improving AI is made out of tasks, and we can get AI to do tasks. But you're right that the original thesis had a much more directly integrated feedback loop.)
The human brain is made up of very different materials than a datacenter. It is entirely possible that the physical structure of the brain is necessary to create intelligence, and that this structure requires materials which certain properties. Maybe a digital simulation will just always require orders of magnitude more data and power than the real, physical thing.
More options
Context Copy link
This is missing the part where the human brain is an exceptionally well-tuned physical object shaped by millenia of evolutionary pressures that arguably constitute a training set vastly bigger than the laws of physics as we currently understand them say is possible to match with an artifical model, much less do any meaningful computation with.
It is also missing the part where the human brain is the most complicated object in the universe, as it is the only currently known object capable of of understanding these questions well enough to even ask them. And even it does not fully understand itself.
That's a bit too restricted: animal brains in general are extraordinarily skilled at learning what's necessary for success in their environments.
Phrasing it in terms of human brains make it seem some spectacular, rare success of evolution, and let's you rest on anthropocentric biases. But what about other primates? Dogs, rats, birds, cuttlefish? Some have radically different architectures than mammal brains, and yet they're extremely intelligent, moreso than humans, within the demands of their particular niche.
The question should be whether AI is able to match the intelligence of any animal that has a CNS. Can an AI be as smart as a pigeon? Currently, it's not, within the scope of the physical world and the rewards the pigeon is seeking. That's something that's interesting and under considered.
Yes, animal brains are extrodinary examples of specialized, niche intelligence. What very clearly sets homo sapiens apart is general intelligence, and the ability to learn things devoid of instinctual context.
More options
Context Copy link
More options
Context Copy link
Not true: a group of human brains, or a human + tools|AI, or humans + tools|AI, are smarter and more complicated.
Uh, object is a singular noun. You are describing collective and/or plural nouns. It is trivially true that they are more complicated in the multiple, but in no way renders the claim incorrect.
What makes a collection of atoms one object or multiple?
Is a building an object? Forest? Planet? Galaxy?
Then why would a group of people and technology not be an object? It could be a temporary physical space when they're together, like an occupied lab.
More options
Context Copy link
More options
Context Copy link
Panpsychism is not a parsimonious theory.
Why not?
The question that spawned this thread
is much less important if AI can’t, but a single human-AI hybrid can. For example
Some radiologists would always be employed, but much less who work much more efficiently.
We could even get exponentially increasing intelligence, although only by directly linking an AI chip to a human brain.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
I think your points are good, and I am myself a bit of an AI sceptic. But I do see where the AI safety crowd is coming from. It may not be particularly likely that we get AGI in the near future. But the fact is that the possibility is there, and is significant enough that it currently cannot be dismissed out of hand. Thus it makes sense to halt development until we are certain that this research won't doom us all.
I find it ironic that this is the logic used by a group that pretty much universally rejects Pascal's Wager. Also, it wouldn't be the first time humanity has made this particular calculation- when the first atomic bomb was tested at Trinity, Oppenheimer was "pretty sure" it wouldn't cause a neutron chain reaction and ignite the atmosphere in a nuclear hellstorm, but he couldn't guarantee it. Infinite stakes do not necessarily require infinite caution.
Even if you take Pascal's wager seriously, it is not actually very useful. There are multiple religions that each claim their god created the world, with most of them being mutually exclusive. Thus Pascal's wager works about as well as an argument for believing in the Christian God as it does for believing in Allah.
Regarding the atomic bomb, they did the math which showed that a chain reaction was impossible prior to the test. We have no such proof against the dangers of AI. The equivalent would be a paper that shows the theoretical limits of how intelligent LLM's can get, and thus prove that the line will stop going up before we reach the point of AGI.
I find it as useful as AI safetyism- a highly uncertain system with allegedly vast consequences easily subverted by defectors. Fortunately I'm very much on Team AI Fizzle, so the fact that its clearly unworkable nonsense doesnt matter to me.
No, they did the math to suggest that based on what they knew at the time, a chain reaction was unlikely to a fairly high, but very much not unity, degree of certainty. And as the Castle Bravo whoopsie showed a few years later, that same group of very smart people was capable of making some fundamental errors resulting in catastrophic consequences.
More options
Context Copy link
Pascal's wager is nearly the earliest example of decision theory, and it hardly makes sense to say that the many religions concern simply breaks decision theory. One can do a variety of things to analyze the probability space as well as the payoff space. For an example simplification, suppose there are two possible mutually exclusive levers you could pull, each with some chance of giving you massively large/infinite utility, and P(A pays out)=0.999 while P(B pays out)=0.001. (This is obviously an extreme case, but that's just to build intuition.) Alternatively, one can adjust probabilities such that maybe there's a third mutually exclusive lever that you can pull which has a guaranteed payoff of 1 or whatever. One can make further refinements.
The issue with mutually exclusive religions is that if you pull P(A) and it doesn't pay out, actually it was another faith all along, then you face infinite suffering for being an infidel who foolishly worshipped Jesus as God. You are incentivized to believe in whatever religion has the greatest punishment for nonbelievers to minimize your downside. But then that incentivizes others to make up religions with increasingly worse punishments in the afterlife in order to force you to adhere to the demands of their faith.
It is just not sustainable as there is no way to distinguish between a religion that is made up by humans and one that is actually correct. Playing that game is hopeless from the start.
More options
Context Copy link
More options
Context Copy link
Since there are more than one interpretations of p(doom), and they are mutually exclusive, pascal's wager is not a good argument for believing one of them over the other.
Why then do people invoke some principle of precaution when it comes to AI and not God? Both are claimed to be possible or extant based on unfalsifiable metaphysical assumptions.
And before you make one, remember that claims the metaphysics can be arrived at through intuitions about extant objects exist for both. But they all require an article of faith to arrive at the extraordinary conclusion.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
I recall the excellent Westworld, Season 1 (and deny that anything else came after that) that the dividing line of sentience is a mostly illusory one: that it is a emergent property of the self-concept, of the internal monologue. That notions of a soul may either be chauvinist hubris: or perhaps God will endow them with one, as Providence dictates.
Since there's no way to ascertain that any individual has consciousness from without for certain, we have to extend the benefit of the doubt to our fellow human beings. Is it possible for superintelligent AGIs, on the line of Helios from DEUS EX? Uncertain. But I am fairly certain that LLMs will reach human capacity in my lifetime, or at the very least reach a level of sociability that it will be monstrous to treat them any less than equals. If the technology stalls out at that level it will still very be much worth it: I will reserve at least 16gb of vram for my new friends.
Westworld is materialist propaganda of course. But S1 was still art because you could simply watch it as a tragedy of humans masturbating with defective robots and fooling themselves into loving them to the point that it destroys them.
But I must ask you: who has more hubris, the man who sees his own ability as unique, or the man who thinks he has the power to elevate all to his condition?
Because that story was also about that.
More options
Context Copy link
Ultimately that's really the point of Turing's Imitation Game. It was not to be a real serious test to use as a measure. It illustrates that we are not even able to discern sentience in other humans, we just assume it, and that if we afford the same leeway to machines, we will eventually end up with machines that have just as good a claim to it as other humans do to us. And as early as ELIZA, once it was clear machines could manage grammar and human language, it was obvious that eventually, without even needing a real paradigm change, we'd end up with machines that would be capable of fooling us.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
The bull case is that AI research and hardware research is enough of an RLVR amenable problem that drawing lines on graphs will improve capabilities enough so that the AI will be drawing the rest of the general and super intelligent owls.
It's not implausible, but by the very nature of the argument it's not falsifiable or reliably predictable at all.
I agree that the current paradigm seems unlikely to lead to the AGI/ASI these pieces treat as imminent without significant paradigm improvements, which could be 1, 10 or 100 years away for all we know.
More options
Context Copy link
I would say Fable is already superhuman at software in general. It's much faster than I am at writing and debugging code and exhibits a high degree of decent taste. The only problem is I run out of tokens so fast. The writing code part is impressive enough but the way it can just look at buggy programs and bang out 50 line test scripts to isolate bugs and test hypotheses is something else entirely. I just watch in astonishment as it does debug cycles that would take me 1-3 hours at a time (plus one coffee) that it does in a minute or two. This is all from my weak user reports like "it doesn't work when I do thing X".
If I were an employer I would definitely pay something like $500-1000/day to arm a senior developer with Fable than I would hire a second senior developer.
But LLMs are getting freakishly good at things they haven't been specifically trained on. Their intelligence does generalize.
Perhaps we only need to RL them in a few more domains to clinch the rest of generalized superintelligence. E.g. you can have them pilot robots and put them in virtual environments and RL fast them there, or real environments like an academy (a warehouse) a bit less fast.
I agree the sample efficiency is terrible and a large limiter and it falls back to RL and we need at least one more architectural breakthrough. But in 2026 I certainly wouldn't bet against AI labs with armies of Fable agents at their disposal and seemingly infinite investment dollars sorting this out.
Such as?
There's been impressive seeming advances in robotics, though I'm not keeping up too closely. I don't see the connection between operating a warehouse and superintelligence though. Certainly the humans operating the warehouse are not superintelligent.
GPT-5.6 Sol Ultra produces proof of the Cycle Double Cover Conjecture
It's almost certainly the case that LLMs are RLVF'd on math.
More options
Context Copy link
More options
Context Copy link
This already seems like such a skeptic's lens that any example I provide will be dismissed as "but it was in the training data lolol".
It's hilarious that I'm apparently a skeptic despite saying right off the bat that I expect transformational impact on much of white collar work.
More options
Context Copy link
But that's the crux of it. One main thesis of why LLMs work as well they do is indeed that the inferences were in the training data.
I ask you then. If it wasn't, then where does it come from?
And if it was, then the question becomes: how much of intelligence is encoded is all recorded human language, and that's not something anybody knows.
We don't even really know if humans can encode more than they can fathom.
How much smarter are humans than animals? That's the quick-and-dirty way to find out.
I also think the answer is instructive, as regards AI: humans are, individually, not much smarter than smart animals. Your average crow, for instance, can solve probably 90% of the problems a human realistically faces: locomotion, foraging food, reproduction, shelter, forming a community. Many humans fail to even succeed at these.
But collectively because we can communicate via language, we're able to do very impressive and intelligent things that individual humans would not be able to manage. And it's specifically that collective intelligence – the sum of all that communication – that AI is trained on.
That's perceptive. Perhaps the real unique quality of transformers is that they reify this distributed intelligence is some entity capable of appearing as a singleton.
I picture that one true ending from Deus Ex: Invisible War where humans all contribute data to some AI in the statue of liberty that acts as a Rousseau's General Will and allows us to be ruled fairly by a collective consciousness that fully understands us.
I don't see the dream of good government happening because like all scifi it still analogizes AI as a will. But it's as close as I've seen fiction ponder on the real political implications. I do think wars will be fought over who gets to bias the dataset.
More options
Context Copy link
More options
Context Copy link
The thing is that with such a loose definition of "in the training data", the hypothesis that AIs will only be able to do what's in the training data is not reassuring against doom. Persuasive propaganda is in the training data. Mass murder is in the training data. Deadly diseases are in the training data. World wars are in the training data. Doing all those things hundreds of times faster and cheaper than humans, like the current set of programming and science tasks where AI doing them faster and cheaper is being dismissed as uninteresting because it was all in the training data, would be more than enough to largely end humanity.
More options
Context Copy link
The exact outputs usually aren't in the training data. Although similar outputs are, you can take any human idea and decompose it into similar older ideas and maybe an infinitesimal amount of chance. That doesn't mean AI will reach human-level intelligence, but makes it impossible to disprove.
That's a claim. You don't know this to be true. Nobody does, that's my point. We can't reason about something we don't understand.
Whether one wants to take drastic measures or do nothing in the face of the unknown only reveals their bias to action or inaction.
More options
Context Copy link
More options
Context Copy link
My steelman of @sarker is: yeah LLMs are cool but the real advances come from RL which is narrow and special and difficult to do in non-easily verifiable contexts. General superintelligence is therefore not coming soon.
My counter is something like: just from pre training alone we see huge leaps towards general intelligence and some glimmers of superintelligence. LLMs even in GPT4 era are surprisingly good at chess despite no specific training in chess, for example.
We may not need RL across every possible domain to get general superintelligence, just poking at enough diverse points in the frontier may solve the whole.
And there's lots of room to poke at it through RL approaches: revisiting the DeepMind stuff for example, build a bot that can kick ass at every video game with the same training set. Including building a robot hand that can operate a controller and robot eye that sees what's going on by watching the TV. (Despite all of the hype DeepMind was nowhere close to any of this). I have a hard time believing that nailing that narrow seeming RL problem can't generalize widely.
But even bleeding edge LLMs will still try to make blatantly illegal moves (unless you have their output run through some sort of harness to prevent it). Not saying their chess playing isn't impressive, it just makes me wonder if "intelligence" is the right way to describe what they are doing/what they possess.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
I meant to use "warehouse" to de-hand wave "an academy". Like just put robots in a big space far away from people and give them diverse tasks to train on. I did not mean to literally imply we'd put them to work in a warehouse and simulate them.
The aim is not directly "build better box stacking robots", it's "we're reaching limits on what we can teach by training on words/code/math so maybe we can get the rest of the way there by doing enough different real world tasks and just from having robots amble about in an environment that we unlock general intelligence".
Training on words on the internet has limits so next lets train agents embodied in spaces, virtual and physical.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
My simplified argument, as distilled from Lesswrong (i.e. Yud) and other books.
The ceiling of capabilities for what we call 'intelligence' is extraordinarily high. Computation can be done many orders of magnitude more efficiently than you think, in the extreme case.
The floor for something 'superintelligent' (right now, I'm using the definition 'smarter than humanity itself as a collective') is substantially below that.
Human brain architecture is NOT anywhere near the most efficient way to instantiate intelligence. (This follows naturally if you accept 1.)
Humans are capable enough to build electronic hardware that can outperform their own brains in computation efficiency.
Thus, eventually, humanity might stumble into or intentionally build a coherent entity that is superintelligent, and sooner than we 'expect.'
Focus in on 4, too. What specific task do you think human brains can perform that we're MAXIMALLY efficient at, such that no electronic version can beat us?
The conceit is that there is no such task, and so its only a matter of time, and adding capabilities to existing models, until the human capabilities are exceeded on all fronts. If the resulting entity is able to do self-improvement, it by definition will do so faster and more efficiently than humanity can track.
I remain unconvinced about (1): it reads as plausible, but I don't think the existence of "superintelligence" is obvious. It seems just as likely that if intelligence is, say, predictive ability, then it could be bounded by the scale of input data with diminishing returns. As an idea, we can train a human to a decent fraction of what cutting-edge models do without needing anything near the scope of training material that the Big Kids are crunching, and with under a hundred watts for 20 years or so.
But first we'd need to iron out what intelligence is, which seems murky still beyond "I'll know it when I see it" a la the Turing test. Is it essentially connected to consciousness (what is that, too)?
I think 'intelligence' if defined in 'practical' terms is "the efficiency with which one can absorb and process the information in an environment, then utilize (or at least theorize how) the material in the local environment to achieve particular goals."
The more complex the goals one can achieve, and the more efficiently they can achieve them, the higher the intelligence.
The Von Neumann/Manhattan Project parallel I'm drawing makes this point. Given all the materials necessary to make a nuclear weapon, how quickly can a particular group of humans go from merely theorizing about the possibility to actually getting one built.
A group of humans that includes Von Neumann and other Physics PhDs, with the backing of the U.S. military, can get it done in, say, 5 years.
A similarly sized group of humans of utterly average intelligence (as measured by IQ)... probably never. Even WITH the backing of the U.S. military.
One Von Neumann and a bunch of average IQ humans... well I don't know.
A whole bunch of Von Neumans working together...
I don't think that's a terrible definition, but it still ends up bounded by the amount of information in the environment available to feed into your intelligence. A third eye would give humans "more information", but probably wouldn't improve our intelligence substantially. I'm sure there are some perfectly capable blind physicists out there.
The other question is what a bunch of Von Neumann clones could do today. IIRC the idea of an atomic bomb was at least known before the Manhattan Project started. It's hard to know in foresight what sort of advances could be made in the next five years, and which will prove intractable. It'd be awesome to solve fusion power, but it's taken well more than five years so far. I'm not sure that the geography of "the possible future" is well enough known to make great claims about what could be there: not all advances that can be seen are inherently terrible.
I expect a LOT. Assuming they could cooperate, which I think they would. This guy literally founded Game Theory among other things.
Like, the other path to superintelligence might be to clone like 10 Von Neumanns, raise them according to best practices, and get them interested in the idea of creating Friendly AI, then give them a lab with a trillion dollars in funding.
Yes yes, lets bound it to "useful," "nonredundant" information. Still, a superintelligence should be able to make use of almost all information it receives second-to-second to make accurate predictions about its future so as to better use resources for its goals.
See, lemme zero in on this for emphasis. Yes, it is indeed hard.
But the higher 'intelligence' entities, given accurate information (ensuring the information you collect is true is another aspect of intelligence!), should ALWAYS be better at making such predictions than lower intelligence ones.
High IQ humans were at least discussing Artificial Intelligence and putting forth timelines for its appearance. And I suspect realized what was happening when AlphaGo beat Sedol. If I were maybe 10 points smarter, I would have plowed money into NVDIA then and there, or at least as soon as people realized AI could run on GPUs.
Average IQ humans might now get that AI has arrived and can figure out uses for it, but would NEVER have seen it coming 5 years out, even if you showed them a complete factual article explaining the AlphaGo Sedol situation. How do I know? I TRIED VERY HARD to explain the implications back when it happened. I also tried to explain the implications when DallE first arrived on the scene. Now these folks I tried explaining to use image generators without a thought!
Low IQ humans, presumably, STILL don't really get what AI is or what it does.
This is why making falsifiable predictions and tracking their outcomes is kind of critical for smart folks to stay calibrated.
It's not really an IQ thing. There was a lot of research and investment that went into AI from a lot of very smart people after AlphaGo, but essentially all of it didn't contribute to the current progress in LLM's at all.
Even if you did make money from e.g Nvidia or Google it would have been for the completely wrong reasons; I don't think that could really be called a successful prediction.
As far as I know not a single person at the time of AlphaGo was calling that sufficient data + compute would lead to the level of generalization we see in LLM's, compared to the narrow single-game approaches of AlphaGo and AlphaZero that were in vogue at the time.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
I don't think there's strong evidence against these but I don't think there's strong evidence for these either. Certainly LLMs are not more efficient than the human brain.
Could be. But this isn't an argument for short timelines, which is implicitly what we're discussing here.
Only if, with self-improvement, it actually improves things that aren't suitable for RL environments with massive amounts of data. So far we are very much in the "lumpy capabilities" regime.
At some tasks they undoubtedly are.
The thought experiment that makes it palatable to me is this:
John Von Neumann might be the smartest human who has ever lived. At least that we have good records of. So call him peak human cognitive capacity.
That man, by coordinating with other extremely smart but not quite as smart humans, fully revolutionized multiple fields, and he died relatively early so we don't even know what he might have output over the rest of his life.
We should, in principle, be able to build a simulated Von Neumann that is ~as smart as he was.
Then we should be able to copy that cognitive model.
We should be able to run a bunch of these copies in parallel and have them work together.
With enough hardware... we should be able to speed up these copies arbitrarily.
We could ask these copies (if they don't ask it themselves) how to improve their own speed and efficiency.
With Von Neumann and Co. we were able to move from pure theory to actual nuclear weapons in <10 years. with 10,000 Von Neumanns running at, say, double speed, what could they do in 5 years?
(Yes, I'm handwaving technical details).
In that respect, I consider Von Neumann's existence as evidence of superintelligence being possible. Unless there's something completely ineffable about human cognition that we, as humans, can't ever capture it.
Which tasks specifically? Human brains consume something like 20% of the power of your laptop, and don't meaningfully draw more power working than when at rest, so the actual efficiency at thinking seems to be even higher than that.
ETA: hang on. Aren't we sort of making some assumptions here, too, about how intelligence works? It seems very likely to me that the value of a Von Neumann drops off steeply after the first one (you can only found decision theory once).
And I think this is likely also to be true with AI (which, based on the research I have seen, is less creative than humans and more homogenous in its output, with even different models experiencing convergence on that homogeneity).
More options
Context Copy link
This is basically assuming the conclusion though. Even granting this for the sake of argument, it doesn't mean that we'll be able to build such a simulation in the next 10 years rather than in ten thousand.
My counter is that you're implicitly making a special pleading for how human brains work that is unlikely to be true.
I assume creating a Von Neumann-level intelligence is possible because a Von Neumann level intelligence existed. It has been created, so it could be done again. And repeated.
I'm not saying we clone Von Neumann, scan his brain and build an electronic copy of it. I'm saying even if we can only build a computer program that is approximately as smart as the smartest human ever... the mere fact that we can then copy that program and run it in parallel should result in technological improvement on par with the Manhattan project.
There is NO limiting principle I'm aware of that makes it impossible to build an electronic brain that meets those criteria. Even if we stumble into it rather than intentionally build it, eventually our millions of monkeys slamming away at keyboards can stumble into a viable method.
Evolution was able to stumble into building Von Neumann, after all.
So what I'd ask you, as a full counter to my arguments, what upper limit or barrier is going to appear BEFORE we get to the point we've built something smarter than our whole species?
You are still assuming the conclusion. We have not built a computer program that is as capable as even a sub-median human in all domains, as far as I can tell, unless there is a program that can tie a shoelace and correctly tell me if I should drive to the car wash.
I don't mean this as a gotcha. LLMs are prone to certain cognitive biases that humans are not, and vice versa, and they are highly useful in many fields. But it's clear that the capabilities frontier is not uniform, far from it.
I don't know. All I know is that the current paradigm relies on massive amounts of artificially generated example problems with answers and I don't believe that all of human knowledge is amenable to such treatment. So far I have not seen any reason to believe that actually general, rather than spiky, superintelligence is imminent. And the imminence is, again, really the key question that's motivating all this.
I guess its easy for me to believe that if a largely randomized optimization process (natural selection) was able to eventually get to Von Neumann intelligence, then humans working with a bit more inherent purpose towards the goal of building a Von Neumann level intelligence can probably get there, even if they make some mis-steps and wander around in the dark for a bit.
Especially if we can build some optimization processes that result in sub-Von Neumann intelligences that are nonetheless useful.
Like, the mountain peak we're seeking is visible, poking out above the fog, even if we can't see and specifically plan a route that will get us there, we have flashlights and climbing gear and GPS systems in place to make navigation through the terrain towards the peak much easier. We're not utterly lost with no clue on what we're doing, in that respect.
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link
More options
Context Copy link