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As someone who has worked in the field of AI/ML for quite awhile now, the problem with current AGI predictions is ML hasn't done anything new since the 80s (or

As someone who has worked in the field of AI/ML for quite awhile now, the problem with current AGI predictions is ML hasn't done anything new since the 80s (or arguably earlier). At the end of the day all ML is using gradient descent to do some sort of non-linear projection of the data on to a latent space, then doing some relatively simple math in this latent space to perform some task. Personally I think the limits of this technique are far better than I would have thought 10 years ago. However we are only near AGI if this is in fact how intelligence works (or can work) and I don't believe we've seen any evidence of this. And there are some very big assumptions baked into this approach. Essentially all we've done is pushed the basic model proposed by linear regression to it's absolutely limits, but, as impressive as the results are, I'm not entirely convinced this will get is over the limit to AGI.

soulofmischief 3y ago
Ultimately you are just a system who maintains homeostasis by modeling your environment and reprojecting it to find patterns which can be used to make actions with predictable outcomes. Is this intelligence? Is there only one correct way to achieve this goal? What do you believe intelligence to be?
- useruser125524 3y ago
  Ultimately we're just a collection of atoms obeying the laws of physics, but reducing all the complexity that entails doesn't really accomplish anything
  - gwoolhurme 3y ago
    This is my frustration put really well. When people talk about how humans are also just a form of LLM (not that the above comment did exactly that). That might even be true, but simplifying things to that degree doesn't help actually discuss what is going on. The original comment is as far as I know correct... while I don't have a PhD some of my undergraduate work was in control theory and ML, and it works really well. The underlying methods we used were from NASA in the late 70s.. surely there is something more in the field no?
    - earthnail 3y ago
      You can look at very small creatures on earth and think "surely there is more to a human than to this ant". And sure, there is, but also, there isn't. Just as basic life evolves into more complex life, why shouldn't the underlying methods from the NASA in the late 70s evolve into ChatGPT and eventually AGI? Simplifying things to this degree can show how you get from A to B.
      - gwoolhurme 3y ago
        I’m not the only one who thinks there is something missing though. That simplification does work to show how to get from A to B, but I don’t think it is analogous when we don’t really understand how we go to human intelligence. None of us were there. All I am saying is that simplifying even mammalian intelligence down to just an LLM doesn’t explain much to me. It might be the case that old 70s math proofs from NASA evolve into a mechanical intelligence maybe? I just personally think that isn’t the case. Humans run on a lot less overall power (though biological necessities like bathrooms and food are real), we also can learn even from less text than a machine and infer from experience. That might not convince you, or you might have your own ideas about what intelligence is.
      - tomhoward 3y ago
        We can see how an ant is a more primitive form of the same kind of thing that a human is, and though they're not on the same evolutionary path, we can see how humans evolved from the most primitive forms of life, because we know what the endpoint looks like. We don't know what 70s AI and ChatGPT will evolve into. That's why everyone keeps debating and prognosticating about it, because nobody actually knows. But whatever people mean by AGI, we don’t know if it will or can evolve from the AI platforms that have come before. We do know that the thing that evolved into ChatGPT is entirely different from primitive cellular life, so there's no reason to believe that ChatGPT will keep evolving into some kind of humanistic, self-aware intelligence.
    - elcomet 3y ago
      What frustrates me is when people say "neural networks cannot show intelligence because they are just a succession of linear layers", and methods that exist since the 70s". I don't understand this argument, and how this has anything to do with intelligence.
- nextos 3y ago
  There's a good summary in the Introduction to Kevin Murphy's second tome [1]. He quotes Josh Tenenbaum, who said: "Intelligence is not just about pattern recognition and function approximation. It’s about modeling the world". I agree, and I think Goodman & Tenennaum [2] is a great place to see other things that may pop up in the road to AGI. LLMs are great, but they do too much at once. I think moving towards AGI requires some form of symbolic reasoning, possibly combined with LLMs, which may play the role of intuition and kitchen-sink memory. [1] https://probml.github.io/pml-book/book2.html https://probml.github.io/pml-book/book2.html [2] https://probmods.org https://probmods.org
  - willbudd 3y ago
    Modelling the world is function approximation: the world is the function and the model is the approximation.
  - NumberWangMan 3y ago
    Do we know for sure that LLMs can't possibly be doing symbolic reasoning of some sort, internally? I thought we couldn't really interpret them yet.
  - stevenhuang 3y ago
    There's mounting evidence LLMs do appear to form models of the real world https://thegradient.pub/othello/ https://thegradient.pub/othello/
- Keyframe 3y ago
  all claims and questions in your answer are still open research and full of inconclusive evidence. Ultimately all of this leads down the spiral to the question about ship of Theseus. I guess, we will find out!
raydiatian 3y ago
Attention is all you need is new the same way Bitcoin is new. Just because you optimally resynthesize old ideas doesn’t mean it’s not new.
matrixdata 3y ago
+1. What's new "since the 80s" are faster computation, larger datasets, and a handful of mathematical breakthroughs that enable once-intractable algorithms. It's obvious that human general intelligence operates in frontiers that are plainly outside the scope of computation.
- space_fountain 3y ago
  What makes that obvious to you? What seems uncomputable about human intelligence?
  - HKH2 3y ago
    Despite our best efforts, we are deeply irrational. Our thinking is based on instinct, not on core principles; it's a top-down approach driven by feelings.
    - russelldjimmy 3y ago
      Off topic - a pet peeve of mine is seeing humans termed as “irrational”. Please forgive my rant, as it is not personally targeted at you. We only seem “irrational” when we are talking about a narrow view of “rationality”, i.e., as defined by the cold hard logic of machines. We do not question why we have this definition of rationality. Our “irrationality” simply seems so because we have not bothered to understand the larger complexity of our evolutionary programming. It’s the same as not bothering to understand how a car works, and then claiming that the car works on magic. If one understands how it works, then it is no longer magic. In the case of humans, we may never fully understand how we work, but we can work towards a compassionate understanding of the same. /rant
      - FredPret 3y ago
        Absolutely on point. I would argue that irrationality isn't even possible. If person A thinks or behaves "irrationally" according to person B, there is simply a difference in perception between the two. A large percentage of those perceptions are created inside one's own mind which may or may not be aligned with the rest of the universe.
deleted 3y ago
[deleted]
blast 3y ago
"We dont have better algorithms, we just have more data" - Peter Norvig
- GaggiX 3y ago
  What about AlphaZero? No prior data.
  - WinLychee 3y ago
    > The parameters θ of the deep neural network in AlphaZero are trained by self-play reinforcement learning, starting from randomly initialised parameters θ. Games are played by selecting moves for both players by MCTS, at ∼ πππt. At the end of the game, the terminal position sT is scored according to the rules of the game to compute the game outcome z: −1 for a loss, 0 for a draw, and +1 for a win. In this case sounds like better algorithm + a lot of self-generated data (but no prior knowledge other than rules).
  - etiam 3y ago
    All of the observable data, implicitly, plus an evaluation criterion. They can operate on that, including starting to mine it in a strategically effective order, because the whole game system except the other player is defined by a small set of rules.
- CrampusDestrus 3y ago
  "We don't have better brains than our ancestors, we just have more data"
  - CatWChainsaw 3y ago
    Our brains are different from our ancestors'. Proto-humans had less frontal cortex and more ability to process information in the immediate spacetime around them, more evenly distributed across the five senses.
IIAOPSW 3y ago
THANK YOU You've phrased the skeptic-not-contrarian case perfectly and succinctly.
bsaul 3y ago
« I don't believe we've seen any evidence of this. » I think chatgpt did show proof of what was considered at least until very recently « intelligence ». aka: understand enough about context and concepts to provide relevant answers to very complex and open questions.
- kumarvvr 3y ago
  Is it understanding or, generating closest possible set of tokens that are accepted as a response to an received input of tokens?
  - vidarh 3y ago
    How do we know those two are different?
    - kumarvvr 3y ago
      Understanding and assimilation can lead to generating relations between disjoint sets of tokens. For example, "squeeze a tube to cut water flow" and "put pressure on a deep wound to stop blood loss" can only be related, if not already in the training data, if there is understanding and intelligence. The ability to do that is intelligence. Otherwise, it's just a search and optimization problem.
      - vidarh 3y ago
        Firstly, transformer architectures are not "just a search and optimisation problem". They do generalise. Whether that generalisation is sufficient to be structurally equivalent to what we consider intelligence is an open question, but getting them to demonstrate that they generalise is easy (eg. ChatGPT can do math, albeit badly, with numbers large enough that it is infeasible for it to just have occurred in its training set) Secondly, this poses the problem of 1) finding examples like the one you gave that it can't understand (regarding your specific example, see [1]), 2) ruling out that there was something "too close" drawing the equivalence in the training data, 3) ruling out that the failure to draw the equivalence is something structural (it can't have real understanding) rather than qualitative (it has real understanding, but it just isn't smart enough to understand the specific given problem) So I'm back to my original question of how we would know if these are structurally different things in the first place. [1] vidarh: How does squeezing a tube to cut water flow give you a hint as to what to do about a deep wound? ChatGPT (GPT4): Squeezing a tube to cut off water flow demonstrates the basic principle of applying pressure to restrict or stop the flow of a fluid. Similarly, applying pressure to a deep wound can help control bleeding, which is a critical first aid measure when dealing with serious injuries. [followed by a long list of what to do if encountering a deep wound]
earthnail 3y ago
I disagree with you. > Essentially all we've done is pushed the basic model proposed by linear regression to it's absolutely limits No, we haven't pushed linear regression to its limits. If it was only linear regression, it wouldn't work. Neural networks need a non-linearity to model complex things. The beauty is that given an infinite series of nonlinearities, one can model any mathematical function. In practice we found it takes much less than "infinite", a handful will already get you a long way. A stack of this basic building block, as you describe it, is really all it takes - we know that mathematically already. The interesting question is: how complex are the functions we need to model? So if we create a neural network of a certain size, is that size large enough to model the problem space? > However we are only near AGI if this is in fact how intelligence works (or can work) and I don't believe we've seen any evidence of this. And there are some very big assumptions baked into this approach. I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works?
- kranke155 3y ago
  Hallucinations are an indication that something is missing, surely? Does chatGPT need a way to verify reality for itself to become truly intelligent ?
  - superzamp 3y ago
    Just the other day, my friend asked me if I knew what this bug she took a picture of was. I replied it looked like a house centipede, and it was indeed one. Two things: 1) My confidence was around 80%, without any sense of absolute certainty. 2) I had no idea why I knew that, and where did I originally learned about what a house centipede looks like.
    - ImHereToVote 3y ago
      Yeah but your brain is wet. Try to make a GPU wet, and see how general the problem solving becomes. Check mate.
    - TheSpiceIsLife 3y ago
GaggiX 3y ago
Isn't intelligence an emergent behavior? We use gradient descent because it's faster than others techniques like evolution. Also aren't systems that play strategic game at the highest level already intelligent in their tasks?
ChildOfChaos 3y ago
I think here, a problem is we don't actually know how our 'intelengece' works, it's not what we think it is, our brain takes a huge amount of shortcuts to arrive at the results it gets, it's not outright intelligent in the way most assume, hence we are victim to all sorts of cognative basis and tricks. Our ability to reason, from an evoultion perspective came about from needing to communicate with others, i.e to give reasons for things, it's this same system we use to be 'logical' or think we are being inteligent from, but it's effectively just a system to make excuses for things, hence why we might choose something in a store for reasons of falling for some form of marketing, but give a completely different logical sounding reason for doing so, hence conspiracy theorists, when confronted with evidence against that theory, don't change there theory, but change there excuse. I believe this could be more profound and have bigger implications than we have realised. This is no different to what a lot of AI models are doing right now when they are wrong and people are calling that out saying there is no intelligence behind that, as it's spouts some nonsense reason for being right, but the problem is, humans do this all the time and I think we do it way more than we realise, the trouble is we are so caught up in it, we cannot see it. What we have with the human brain is something that wasn't designed logically but via evolution which gives the illusion of intelligence or arrives at mostly the same result, but in completely different ways than expected, therefore I think the path to an AGI, might not be quite what some are saying or expects it to be, because the human brain certainly doesn't work like that.
stevenhuang 3y ago
It's entirely possible the way our human brains work may very well end up reducible to a sequence of very very complicated matrix operations. If that's the case, there doesn't need to be anything new; fundamentally, linear regressions may be sufficient.
krisoft 3y ago
> ML hasn't done anything new since the 80s Lol, let me ask ChatGPT what it thinks about that. :) > I don't believe we've seen any evidence of this What kind of evidence would you like to have? Do you want a mathematical proof or what? There is evidence that we are making forward progress in solving problems which were previously in the domain of human cognition. There is evidence that yesterday's "impossible" problem become "possible" while the "hard" problems become "easy" or even "trivial". (Just look at this xkcd[1]. When it was published in 2014 telling whether or not a picture contained a bird was indeed a "5 year and a team of researchers" project. Today it is what, an afternoon? A tutorial you pick up to learn a new ML framework?) There is also evidence that our solutions are tending toward more generalised ones. Previously you would need a "sentiment detection" network, and a separate "subject disambiguation" network to parse the meaning out of a text. Today you can achieve the same or better with an LLM trained to follow instructions. Obviously these are not "hard evidence" that this path will lead to AGI. But it is certainly not unreasonable to think that it might. 1: https://xkcd.com/1425/ https://xkcd.com/1425/
- novaRom 3y ago
  > ML hasn't done anything new since the 80s Schmidhuber, is it you? But seriously, it's partially true most techniques we use today could be found initially envisaged in 90th ANN-related papers. I think Geoffrey Hinton summarized most of them well in his famous "Neural Networks for Machine Learning" lectures. Essentially, what is available today is compute resources unimaginable in 90th, so scaling up from a shallow 2-layered Multi Layer Perceptron to something like 96-layered deep architecture is possible only recently. We also found some of the tricks work better than others in practice when we scale up (like *ELU non-linearity, layer-norm, residual connections). What stays the same however is the general approach: training and validation sets, cross entropy loss, softmax, learnable parameters based on data-in/data-out training pairs, and differentiation chain rule. IMO this requires some innovative revision, especially generalization is still very weak in all architectures today.
kypro 3y ago
> Personally I think the limits of this technique are far better than I would have thought 10 years ago. Honestly I've always surprised at the scepticism AI researchers have had of about the limits of training large neural nets with gradient descent. Where I've had my doubts is in the architecture of existing models and still think this is their primary limiting factor (more so than compute and network size). I think the question that remains now is whether existing models are actually capable of producing a general intelligence that's well rounded and reliable enough to be competitive with human general intelligence. Personally, I think LLMs like GPT-4 are generally intelligent in most ways and should be considered AGI already (at least in a weak sense of the word), but they clearly have notable gaps in their intelligence such as their ability to be consistent, long-term memory, and ability to discern reality from delusion. I don't think scaling existing models could possibly address these limitations – they seem to be emergent properties of an imperfect architecture. So I suspect we're still a few breakthroughs away from a general intelligence as well rounded as human general intelligence. That said, I suspect existing models (perhaps with a few minor tweaks) are generally intelligent enough that larger models alone are likely still able to replace the majority of human intellectual labour. I guess what I'm touching on here is the need to more nuanced about what we mean by "AGI" at this point. I think it's quite likely (probable even) that in a few years we'll have an AI that's generally intelligent and capable enough that it can replace a large percentage of existing knowledge work – and also generally intelligent enough to be dangerous. But I suspect despite this it will still have really clear limitations in its abilities when contrasted with human general intelligence.
- xiphias2 3y ago
  I agree with you totally. For me AGI is achieved when AutoGPT is at a point when it's able to improve its own algorithm (specifically improve on the GPT architecture). Flash attention came out a bit less than a year ago (27 May 2022), which was a great scaling improvement for getting rid of O(n^2) memory requirement in the length of attention. I guess the next one we need will be one of the solutions that change the FLOPS for computing attention from n^2 to n*log(n) (there was already a paper that achieved it using resizable gauss filters where the size and the base convolution filter is learned separately). A few of these kind of ,,breakthroughs''/algorithmic improvements are the only ones needed to get to AGI (self improving machine) in my opinion.
nologic01 3y ago
Unfortunately this argument, while imho entirely valid and also frequently seen in discussion, is unable to stop the massive train that has been set in motion. There are various types of retorts: i) the brain is also doing gradient descent; ii) what the brain does does not matter (if you can fake intelligence you have intellegence) iii) the pace is now so fast that has not happened in decades will happen in the next five years etc. None of them is remotely convincing. I would really start getting worried if indeed some of the AI fanboys had a convincing argument. But we are where we are. Somehow this whole episode will be just another turn of the screw in adopting algorithms in societal information processing. This in itself is fascinating and dangerous enough.
- dS0rrow 3y ago
  If not convincing what argument for and against do you think is the strongest?
  - nologic01 3y ago
    I don't think there is any strong argument for AGI (i.e., that it is somehow coming any time soon). In my explanatory framework all developments make sense as purely statistical algorithmic advances. The surprising and interesting applications involving images, language are not really changing that fundamental reality. There is a case to be made that with sufficient ingenuity at some point people will expand the algorithmic toolkit into more flexible and powerful dimensions. It may integrate formal logic in some shape or form, or yet to be conceived mathematical constructs. But the type of mental leap that I think would be required just to breakout of the statistical fitting straight-jacket cannot be invented to satisfy the timing of some market craze. If you look at the broad outline of the development of mathematics there are many areas where we have hit a complexity wall and generations of talent have not advanced an iota. Even if we condition on some future breakthrough, the next level of mathematical / algorithmic dexterity we might reach will follow its own intrinsic logic, which will probably be very interesting but may or may not have anything to do with human intelligence.
- caddemon 3y ago
  The brain is definitely not doing gradient descent. That is the biggest issue with using artificial neural networks as a neuroscience modeling tool. The fact other learning algorithms have not come close to gradient descent performance in most cases has therefore largely kept neuro theory as a niche in modern academic research -- despite its entanglement with the early development of artificial neural networks. Not taking a stance on whether "intelligence" can emerge out of gradient descent but it's certainly not biological.
otabdeveloper4 3y ago
You miss the point. Just make humans dumber and more predictable. Problem solved. (This isn't that hard since humans will adapt to the stupidity level of the "AI" they use daily on their own without special social engineering efforts.)
- pallas_athena 3y ago
  https://www.smbc-comics.com/comic/ai-3 https://www.smbc-comics.com/comic/ai-3
ogogmad 3y ago
> Essentially all we've done is pushed the basic model proposed by linear regression to it's absolutely limits [emphasis mine] Where's the data to show that we've hit the limits? It's obviously not linear regression. You mean gradient descent?
_acco 3y ago
Yes, that’s right. AGI ≠ a lot of AI. They are fundamentally different things. The first computer was designed in 1837, long before a computer was ever built. We know how fusion reactions work, now we’re tweaking the engineering to harness it in a reactor. We don’t know how human intelligence works. We don’t have designs or even a philosophy for AGI. Yet, the prevailing view is that our greatest invention will just suddenly “emerge.” No other field I’m aware of so strongly purports it will reach its ultimate breakthrough without having a clue of the fundamentals of that breakthrough. It’s like nuclear scientists saying “if we just do a lot of fission, we think fusion will just happen.”
- RugnirViking 3y ago
  your hypothesis is that there is a qualitatively different property to agi than whatever we have now. Most people here are just saying "if chatgpt is smarter that would be agi". Going by the differences between gpt3.5 and gpt4 is really interesting. It is better able to reason in basically any problem I throw at it. Personally I think that a hypothetical system that is able to generate a sufficiently good response for ANY text input is AGI. There aren't really any "gotcha" cases with this technology that I'm aware of where it just can't ever respond appropriately. Most clear failings of existing systems involve ever more contrived logic puzzles, which each successive generation is able to solve, and eventually at some point the required logic puzzle will be so dense few humans can solve it. This isn't a case a case of "studying for the test" of popular internet examples either. I encourage you to try and invent your own gotchas for earlier versions then try them on newer models. Change the wording and order of logic puzzles, or encase them within scenarios to ensure its not responding to the format of the prompt There are absolubtely cases of people overhypting it, or it overfitting to training data (see the debacle about it passing whatever bar exam, university test etc.). But despite the hype there is an underlying level of intelligence that is building and I use it to solve problems pretty much every day. I think of it atm as like a 4 year old that has inexplicably read every book ever written
  - _acco 3y ago
    That’s right. In many ways, AGI and AI are opposites. We don’t actually “train” humans. We present them information. And a human can choose to ingest that information or - just as importantly - choose not to. A 3yo learns English not through being force fed, but through their own curiosity and motivation to learn. GPT and the like are proving to be amazing tools. The compute model of probabilistic algorithms (“plain” AI) is going to transform industries. But the more sophisticated these tools get, the further from AGI they will become. When we create AGI, it will begin with a blank slate, not the whole of human knowledge. It will study some topics deeply and be uninterested in others. It might draw its own conclusions instead of just taking the conclusions presented to it. Or it may decide not to study at all, preferring to instead become a hermit. David Deutsch is the most compelling on this topic: https://open.spotify.com/episode/5nGOkhcJ7VszXm2WVgxKyC?si=8zq1X2xhQCy4CcYQMs9tzQ https://open.spotify.com/episode/5nGOkhcJ7VszXm2WVgxKyC?si=8...
Art9681 3y ago
It's all moot until we can agree on a definition of intelligence. I don't necessarily think it's relevant to the discussion whether an AI is truly intelligent or not. What matters is whether the technology is good enough to fool an "intelligent" human. It's good enough to do that today. I'd say we would have to train a "dumber" model that can respond with a simple "im not sure" instead of the typical responses we get today would make it even more believable, not less. It's obvious it's an AI because it's too smart. It's too intelligent. Dumb it down, remove the AI branding, and it would fool the majority of the world.
- marcusverus 3y ago
  > It's all moot until we can agree on a definition of intelligence. We did agree on a definition intelligence! For 50 years, the Turing test was the unquestioned threshold beyond which machines would be considered "intelligent". The simple fact of the matter is that we've reached that point, but folks remain unimpressed, and so have set about moving the goalposts. I am of the opinion that, when the dust settles, the point in time which will be selected as "the moment we achieved Artificial Intelligence", or even rudimentary AGI, will not be in the future, but in the past. This will be true because, once we take a step back, we'll remember that our definition of intelligence should not be so strict that it it excludes a significant percentage of the human population. Consider this--is there any reasonable definition of intelligence which excludes Chat GPT 4.0, but which includes all human beings who fall in the bottom ~5% of IQ?
  - Kbelicius 3y ago
    > We did agree on a definition intelligence! For 50 years, the Turing test was the unquestioned threshold beyond which machines would be considered "intelligent". First of all it is now more then 70 years. Secondly the question of the paper isn't whether a computer is intelligent but whether a computer can win the imitation game, so naturally it doesn't contain any definition of intelligence within it.
    - marcusverus 3y ago
bick_nyers 3y ago
Do you not believe that intelligence is just nonlinear optimization all the way down?
nr2x 3y ago
Depends on the definition. In my mind, a system that can write code, poetry, and suggest a recipe based on a list of ingredients has a form of “general” intelligence. So by that measure we are there. If we’re talking about something like agency or free will, that’s trickier. The problem in this space is people use terms that nobody agrees on.

soulofmischief 3y ago

Ultimately you are just a system who maintains homeostasis by modeling your environment and reprojecting it to find patterns which can be used to make actions with predictable outcomes. Is this intelligence? Is there only one correct way to achieve this goal? What do you believe intelligence to be?

useruser125524 3y ago
Ultimately we're just a collection of atoms obeying the laws of physics, but reducing all the complexity that entails doesn't really accomplish anything
- gwoolhurme 3y ago
  This is my frustration put really well. When people talk about how humans are also just a form of LLM (not that the above comment did exactly that). That might even be true, but simplifying things to that degree doesn't help actually discuss what is going on. The original comment is as far as I know correct... while I don't have a PhD some of my undergraduate work was in control theory and ML, and it works really well. The underlying methods we used were from NASA in the late 70s.. surely there is something more in the field no?
  - earthnail 3y ago
    You can look at very small creatures on earth and think "surely there is more to a human than to this ant". And sure, there is, but also, there isn't. Just as basic life evolves into more complex life, why shouldn't the underlying methods from the NASA in the late 70s evolve into ChatGPT and eventually AGI? Simplifying things to this degree can show how you get from A to B.
    - gwoolhurme 3y ago
      I’m not the only one who thinks there is something missing though. That simplification does work to show how to get from A to B, but I don’t think it is analogous when we don’t really understand how we go to human intelligence. None of us were there. All I am saying is that simplifying even mammalian intelligence down to just an LLM doesn’t explain much to me. It might be the case that old 70s math proofs from NASA evolve into a mechanical intelligence maybe? I just personally think that isn’t the case. Humans run on a lot less overall power (though biological necessities like bathrooms and food are real), we also can learn even from less text than a machine and infer from experience. That might not convince you, or you might have your own ideas about what intelligence is.
    - tomhoward 3y ago
      We can see how an ant is a more primitive form of the same kind of thing that a human is, and though they're not on the same evolutionary path, we can see how humans evolved from the most primitive forms of life, because we know what the endpoint looks like. We don't know what 70s AI and ChatGPT will evolve into. That's why everyone keeps debating and prognosticating about it, because nobody actually knows. But whatever people mean by AGI, we don’t know if it will or can evolve from the AI platforms that have come before. We do know that the thing that evolved into ChatGPT is entirely different from primitive cellular life, so there's no reason to believe that ChatGPT will keep evolving into some kind of humanistic, self-aware intelligence.
  - elcomet 3y ago
    What frustrates me is when people say "neural networks cannot show intelligence because they are just a succession of linear layers", and methods that exist since the 70s". I don't understand this argument, and how this has anything to do with intelligence.
nextos 3y ago
There's a good summary in the Introduction to Kevin Murphy's second tome [1]. He quotes Josh Tenenbaum, who said: "Intelligence is not just about pattern recognition and function approximation. It’s about modeling the world". I agree, and I think Goodman & Tenennaum [2] is a great place to see other things that may pop up in the road to AGI. LLMs are great, but they do too much at once. I think moving towards AGI requires some form of symbolic reasoning, possibly combined with LLMs, which may play the role of intuition and kitchen-sink memory. [1] https://probml.github.io/pml-book/book2.html https://probml.github.io/pml-book/book2.html [2] https://probmods.org https://probmods.org
- willbudd 3y ago
  Modelling the world is function approximation: the world is the function and the model is the approximation.
- NumberWangMan 3y ago
  Do we know for sure that LLMs can't possibly be doing symbolic reasoning of some sort, internally? I thought we couldn't really interpret them yet.
- stevenhuang 3y ago
  There's mounting evidence LLMs do appear to form models of the real world https://thegradient.pub/othello/ https://thegradient.pub/othello/
Keyframe 3y ago
all claims and questions in your answer are still open research and full of inconclusive evidence. Ultimately all of this leads down the spiral to the question about ship of Theseus. I guess, we will find out!

raydiatian 3y ago

Attention is all you need is new the same way Bitcoin is new. Just because you optimally resynthesize old ideas doesn’t mean it’s not new.

matrixdata 3y ago

+1. What's new "since the 80s" are faster computation, larger datasets, and a handful of mathematical breakthroughs that enable once-intractable algorithms. It's obvious that human general intelligence operates in frontiers that are plainly outside the scope of computation.

space_fountain 3y ago
What makes that obvious to you? What seems uncomputable about human intelligence?
- HKH2 3y ago
  Despite our best efforts, we are deeply irrational. Our thinking is based on instinct, not on core principles; it's a top-down approach driven by feelings.
  - russelldjimmy 3y ago
    Off topic - a pet peeve of mine is seeing humans termed as “irrational”. Please forgive my rant, as it is not personally targeted at you. We only seem “irrational” when we are talking about a narrow view of “rationality”, i.e., as defined by the cold hard logic of machines. We do not question why we have this definition of rationality. Our “irrationality” simply seems so because we have not bothered to understand the larger complexity of our evolutionary programming. It’s the same as not bothering to understand how a car works, and then claiming that the car works on magic. If one understands how it works, then it is no longer magic. In the case of humans, we may never fully understand how we work, but we can work towards a compassionate understanding of the same. /rant
    - FredPret 3y ago
      Absolutely on point. I would argue that irrationality isn't even possible. If person A thinks or behaves "irrationally" according to person B, there is simply a difference in perception between the two. A large percentage of those perceptions are created inside one's own mind which may or may not be aligned with the rest of the universe.

deleted 3y ago

[deleted]

blast 3y ago

"We dont have better algorithms, we just have more data" - Peter Norvig

GaggiX 3y ago
What about AlphaZero? No prior data.
- WinLychee 3y ago
  > The parameters θ of the deep neural network in AlphaZero are trained by self-play reinforcement learning, starting from randomly initialised parameters θ. Games are played by selecting moves for both players by MCTS, at ∼ πππt. At the end of the game, the terminal position sT is scored according to the rules of the game to compute the game outcome z: −1 for a loss, 0 for a draw, and +1 for a win. In this case sounds like better algorithm + a lot of self-generated data (but no prior knowledge other than rules).
- etiam 3y ago
  All of the observable data, implicitly, plus an evaluation criterion. They can operate on that, including starting to mine it in a strategically effective order, because the whole game system except the other player is defined by a small set of rules.
CrampusDestrus 3y ago
"We don't have better brains than our ancestors, we just have more data"
- CatWChainsaw 3y ago
  Our brains are different from our ancestors'. Proto-humans had less frontal cortex and more ability to process information in the immediate spacetime around them, more evenly distributed across the five senses.

IIAOPSW 3y ago

THANK YOU You've phrased the skeptic-not-contrarian case perfectly and succinctly.

bsaul 3y ago

« I don't believe we've seen any evidence of this. » I think chatgpt did show proof of what was considered at least until very recently « intelligence ». aka: understand enough about context and concepts to provide relevant answers to very complex and open questions.

kumarvvr 3y ago
Is it understanding or, generating closest possible set of tokens that are accepted as a response to an received input of tokens?
- vidarh 3y ago
  How do we know those two are different?
  - kumarvvr 3y ago
    Understanding and assimilation can lead to generating relations between disjoint sets of tokens. For example, "squeeze a tube to cut water flow" and "put pressure on a deep wound to stop blood loss" can only be related, if not already in the training data, if there is understanding and intelligence. The ability to do that is intelligence. Otherwise, it's just a search and optimization problem.
    - vidarh 3y ago
      Firstly, transformer architectures are not "just a search and optimisation problem". They do generalise. Whether that generalisation is sufficient to be structurally equivalent to what we consider intelligence is an open question, but getting them to demonstrate that they generalise is easy (eg. ChatGPT can do math, albeit badly, with numbers large enough that it is infeasible for it to just have occurred in its training set) Secondly, this poses the problem of 1) finding examples like the one you gave that it can't understand (regarding your specific example, see [1]), 2) ruling out that there was something "too close" drawing the equivalence in the training data, 3) ruling out that the failure to draw the equivalence is something structural (it can't have real understanding) rather than qualitative (it has real understanding, but it just isn't smart enough to understand the specific given problem) So I'm back to my original question of how we would know if these are structurally different things in the first place. [1] vidarh: How does squeezing a tube to cut water flow give you a hint as to what to do about a deep wound? ChatGPT (GPT4): Squeezing a tube to cut off water flow demonstrates the basic principle of applying pressure to restrict or stop the flow of a fluid. Similarly, applying pressure to a deep wound can help control bleeding, which is a critical first aid measure when dealing with serious injuries. [followed by a long list of what to do if encountering a deep wound]

earthnail 3y ago

I disagree with you. > Essentially all we've done is pushed the basic model proposed by linear regression to it's absolutely limits No, we haven't pushed linear regression to its limits. If it was only linear regression, it wouldn't work. Neural networks need a non-linearity to model complex things. The beauty is that given an infinite series of nonlinearities, one can model any mathematical function. In practice we found it takes much less than "infinite", a handful will already get you a long way. A stack of this basic building block, as you describe it, is really all it takes - we know that mathematically already. The interesting question is: how complex are the functions we need to model? So if we create a neural network of a certain size, is that size large enough to model the problem space? > However we are only near AGI if this is in fact how intelligence works (or can work) and I don't believe we've seen any evidence of this. And there are some very big assumptions baked into this approach. I think ChatGPT is good evidence of this. What evidence do we have that this isn't how intelligence works?

kranke155 3y ago
Hallucinations are an indication that something is missing, surely? Does chatGPT need a way to verify reality for itself to become truly intelligent ?
- superzamp 3y ago
  Just the other day, my friend asked me if I knew what this bug she took a picture of was. I replied it looked like a house centipede, and it was indeed one. Two things: 1) My confidence was around 80%, without any sense of absolute certainty. 2) I had no idea why I knew that, and where did I originally learned about what a house centipede looks like.
  - ImHereToVote 3y ago
    Yeah but your brain is wet. Try to make a GPU wet, and see how general the problem solving becomes. Check mate.
  - TheSpiceIsLife 3y ago

GaggiX 3y ago

Isn't intelligence an emergent behavior? We use gradient descent because it's faster than others techniques like evolution. Also aren't systems that play strategic game at the highest level already intelligent in their tasks?

ChildOfChaos 3y ago

I think here, a problem is we don't actually know how our 'intelengece' works, it's not what we think it is, our brain takes a huge amount of shortcuts to arrive at the results it gets, it's not outright intelligent in the way most assume, hence we are victim to all sorts of cognative basis and tricks. Our ability to reason, from an evoultion perspective came about from needing to communicate with others, i.e to give reasons for things, it's this same system we use to be 'logical' or think we are being inteligent from, but it's effectively just a system to make excuses for things, hence why we might choose something in a store for reasons of falling for some form of marketing, but give a completely different logical sounding reason for doing so, hence conspiracy theorists, when confronted with evidence against that theory, don't change there theory, but change there excuse. I believe this could be more profound and have bigger implications than we have realised. This is no different to what a lot of AI models are doing right now when they are wrong and people are calling that out saying there is no intelligence behind that, as it's spouts some nonsense reason for being right, but the problem is, humans do this all the time and I think we do it way more than we realise, the trouble is we are so caught up in it, we cannot see it. What we have with the human brain is something that wasn't designed logically but via evolution which gives the illusion of intelligence or arrives at mostly the same result, but in completely different ways than expected, therefore I think the path to an AGI, might not be quite what some are saying or expects it to be, because the human brain certainly doesn't work like that.

stevenhuang 3y ago

It's entirely possible the way our human brains work may very well end up reducible to a sequence of very very complicated matrix operations. If that's the case, there doesn't need to be anything new; fundamentally, linear regressions may be sufficient.

krisoft 3y ago

> ML hasn't done anything new since the 80s Lol, let me ask ChatGPT what it thinks about that. :) > I don't believe we've seen any evidence of this What kind of evidence would you like to have? Do you want a mathematical proof or what? There is evidence that we are making forward progress in solving problems which were previously in the domain of human cognition. There is evidence that yesterday's "impossible" problem become "possible" while the "hard" problems become "easy" or even "trivial". (Just look at this xkcd[1]. When it was published in 2014 telling whether or not a picture contained a bird was indeed a "5 year and a team of researchers" project. Today it is what, an afternoon? A tutorial you pick up to learn a new ML framework?) There is also evidence that our solutions are tending toward more generalised ones. Previously you would need a "sentiment detection" network, and a separate "subject disambiguation" network to parse the meaning out of a text. Today you can achieve the same or better with an LLM trained to follow instructions. Obviously these are not "hard evidence" that this path will lead to AGI. But it is certainly not unreasonable to think that it might. 1: https://xkcd.com/1425/ https://xkcd.com/1425/

novaRom 3y ago
> ML hasn't done anything new since the 80s Schmidhuber, is it you? But seriously, it's partially true most techniques we use today could be found initially envisaged in 90th ANN-related papers. I think Geoffrey Hinton summarized most of them well in his famous "Neural Networks for Machine Learning" lectures. Essentially, what is available today is compute resources unimaginable in 90th, so scaling up from a shallow 2-layered Multi Layer Perceptron to something like 96-layered deep architecture is possible only recently. We also found some of the tricks work better than others in practice when we scale up (like *ELU non-linearity, layer-norm, residual connections). What stays the same however is the general approach: training and validation sets, cross entropy loss, softmax, learnable parameters based on data-in/data-out training pairs, and differentiation chain rule. IMO this requires some innovative revision, especially generalization is still very weak in all architectures today.

kypro 3y ago

> Personally I think the limits of this technique are far better than I would have thought 10 years ago. Honestly I've always surprised at the scepticism AI researchers have had of about the limits of training large neural nets with gradient descent. Where I've had my doubts is in the architecture of existing models and still think this is their primary limiting factor (more so than compute and network size). I think the question that remains now is whether existing models are actually capable of producing a general intelligence that's well rounded and reliable enough to be competitive with human general intelligence. Personally, I think LLMs like GPT-4 are generally intelligent in most ways and should be considered AGI already (at least in a weak sense of the word), but they clearly have notable gaps in their intelligence such as their ability to be consistent, long-term memory, and ability to discern reality from delusion. I don't think scaling existing models could possibly address these limitations – they seem to be emergent properties of an imperfect architecture. So I suspect we're still a few breakthroughs away from a general intelligence as well rounded as human general intelligence. That said, I suspect existing models (perhaps with a few minor tweaks) are generally intelligent enough that larger models alone are likely still able to replace the majority of human intellectual labour. I guess what I'm touching on here is the need to more nuanced about what we mean by "AGI" at this point. I think it's quite likely (probable even) that in a few years we'll have an AI that's generally intelligent and capable enough that it can replace a large percentage of existing knowledge work – and also generally intelligent enough to be dangerous. But I suspect despite this it will still have really clear limitations in its abilities when contrasted with human general intelligence.

xiphias2 3y ago
I agree with you totally. For me AGI is achieved when AutoGPT is at a point when it's able to improve its own algorithm (specifically improve on the GPT architecture). Flash attention came out a bit less than a year ago (27 May 2022), which was a great scaling improvement for getting rid of O(n^2) memory requirement in the length of attention. I guess the next one we need will be one of the solutions that change the FLOPS for computing attention from n^2 to n*log(n) (there was already a paper that achieved it using resizable gauss filters where the size and the base convolution filter is learned separately). A few of these kind of ,,breakthroughs''/algorithmic improvements are the only ones needed to get to AGI (self improving machine) in my opinion.

nologic01 3y ago

Unfortunately this argument, while imho entirely valid and also frequently seen in discussion, is unable to stop the massive train that has been set in motion. There are various types of retorts: i) the brain is also doing gradient descent; ii) what the brain does does not matter (if you can fake intelligence you have intellegence) iii) the pace is now so fast that has not happened in decades will happen in the next five years etc. None of them is remotely convincing. I would really start getting worried if indeed some of the AI fanboys had a convincing argument. But we are where we are. Somehow this whole episode will be just another turn of the screw in adopting algorithms in societal information processing. This in itself is fascinating and dangerous enough.

dS0rrow 3y ago
If not convincing what argument for and against do you think is the strongest?
- nologic01 3y ago
  I don't think there is any strong argument for AGI (i.e., that it is somehow coming any time soon). In my explanatory framework all developments make sense as purely statistical algorithmic advances. The surprising and interesting applications involving images, language are not really changing that fundamental reality. There is a case to be made that with sufficient ingenuity at some point people will expand the algorithmic toolkit into more flexible and powerful dimensions. It may integrate formal logic in some shape or form, or yet to be conceived mathematical constructs. But the type of mental leap that I think would be required just to breakout of the statistical fitting straight-jacket cannot be invented to satisfy the timing of some market craze. If you look at the broad outline of the development of mathematics there are many areas where we have hit a complexity wall and generations of talent have not advanced an iota. Even if we condition on some future breakthrough, the next level of mathematical / algorithmic dexterity we might reach will follow its own intrinsic logic, which will probably be very interesting but may or may not have anything to do with human intelligence.
caddemon 3y ago
The brain is definitely not doing gradient descent. That is the biggest issue with using artificial neural networks as a neuroscience modeling tool. The fact other learning algorithms have not come close to gradient descent performance in most cases has therefore largely kept neuro theory as a niche in modern academic research -- despite its entanglement with the early development of artificial neural networks. Not taking a stance on whether "intelligence" can emerge out of gradient descent but it's certainly not biological.

otabdeveloper4 3y ago

You miss the point. Just make humans dumber and more predictable. Problem solved. (This isn't that hard since humans will adapt to the stupidity level of the "AI" they use daily on their own without special social engineering efforts.)

pallas_athena 3y ago
https://www.smbc-comics.com/comic/ai-3 https://www.smbc-comics.com/comic/ai-3

ogogmad 3y ago

> Essentially all we've done is pushed the basic model proposed by linear regression to it's absolutely limits [emphasis mine] Where's the data to show that we've hit the limits? It's obviously not linear regression. You mean gradient descent?

_acco 3y ago

Yes, that’s right. AGI ≠ a lot of AI. They are fundamentally different things. The first computer was designed in 1837, long before a computer was ever built. We know how fusion reactions work, now we’re tweaking the engineering to harness it in a reactor. We don’t know how human intelligence works. We don’t have designs or even a philosophy for AGI. Yet, the prevailing view is that our greatest invention will just suddenly “emerge.” No other field I’m aware of so strongly purports it will reach its ultimate breakthrough without having a clue of the fundamentals of that breakthrough. It’s like nuclear scientists saying “if we just do a lot of fission, we think fusion will just happen.”

RugnirViking 3y ago
your hypothesis is that there is a qualitatively different property to agi than whatever we have now. Most people here are just saying "if chatgpt is smarter that would be agi". Going by the differences between gpt3.5 and gpt4 is really interesting. It is better able to reason in basically any problem I throw at it. Personally I think that a hypothetical system that is able to generate a sufficiently good response for ANY text input is AGI. There aren't really any "gotcha" cases with this technology that I'm aware of where it just can't ever respond appropriately. Most clear failings of existing systems involve ever more contrived logic puzzles, which each successive generation is able to solve, and eventually at some point the required logic puzzle will be so dense few humans can solve it. This isn't a case a case of "studying for the test" of popular internet examples either. I encourage you to try and invent your own gotchas for earlier versions then try them on newer models. Change the wording and order of logic puzzles, or encase them within scenarios to ensure its not responding to the format of the prompt There are absolubtely cases of people overhypting it, or it overfitting to training data (see the debacle about it passing whatever bar exam, university test etc.). But despite the hype there is an underlying level of intelligence that is building and I use it to solve problems pretty much every day. I think of it atm as like a 4 year old that has inexplicably read every book ever written
- _acco 3y ago
  That’s right. In many ways, AGI and AI are opposites. We don’t actually “train” humans. We present them information. And a human can choose to ingest that information or - just as importantly - choose not to. A 3yo learns English not through being force fed, but through their own curiosity and motivation to learn. GPT and the like are proving to be amazing tools. The compute model of probabilistic algorithms (“plain” AI) is going to transform industries. But the more sophisticated these tools get, the further from AGI they will become. When we create AGI, it will begin with a blank slate, not the whole of human knowledge. It will study some topics deeply and be uninterested in others. It might draw its own conclusions instead of just taking the conclusions presented to it. Or it may decide not to study at all, preferring to instead become a hermit. David Deutsch is the most compelling on this topic: https://open.spotify.com/episode/5nGOkhcJ7VszXm2WVgxKyC?si=8zq1X2xhQCy4CcYQMs9tzQ https://open.spotify.com/episode/5nGOkhcJ7VszXm2WVgxKyC?si=8...

Art9681 3y ago

It's all moot until we can agree on a definition of intelligence. I don't necessarily think it's relevant to the discussion whether an AI is truly intelligent or not. What matters is whether the technology is good enough to fool an "intelligent" human. It's good enough to do that today. I'd say we would have to train a "dumber" model that can respond with a simple "im not sure" instead of the typical responses we get today would make it even more believable, not less. It's obvious it's an AI because it's too smart. It's too intelligent. Dumb it down, remove the AI branding, and it would fool the majority of the world.

marcusverus 3y ago
> It's all moot until we can agree on a definition of intelligence. We did agree on a definition intelligence! For 50 years, the Turing test was the unquestioned threshold beyond which machines would be considered "intelligent". The simple fact of the matter is that we've reached that point, but folks remain unimpressed, and so have set about moving the goalposts. I am of the opinion that, when the dust settles, the point in time which will be selected as "the moment we achieved Artificial Intelligence", or even rudimentary AGI, will not be in the future, but in the past. This will be true because, once we take a step back, we'll remember that our definition of intelligence should not be so strict that it it excludes a significant percentage of the human population. Consider this--is there any reasonable definition of intelligence which excludes Chat GPT 4.0, but which includes all human beings who fall in the bottom ~5% of IQ?
- Kbelicius 3y ago
  > We did agree on a definition intelligence! For 50 years, the Turing test was the unquestioned threshold beyond which machines would be considered "intelligent". First of all it is now more then 70 years. Secondly the question of the paper isn't whether a computer is intelligent but whether a computer can win the imitation game, so naturally it doesn't contain any definition of intelligence within it.
  - marcusverus 3y ago

bick_nyers 3y ago

Do you not believe that intelligence is just nonlinear optimization all the way down?

nr2x 3y ago

Depends on the definition. In my mind, a system that can write code, poetry, and suggest a recipe based on a list of ingredients has a form of “general” intelligence. So by that measure we are there. If we’re talking about something like agency or free will, that’s trickier. The problem in this space is people use terms that nobody agrees on.