As an AI researcher myself I can definitely see why people used to have these sentiments, but I also have to point out how the big papers of the last ~12 months or so have changed the landscape. Especially multi-modal models like Gato. To my surprise, a lot of people (even inside the community) still tend to put human intelligence on some sort of pedestal, but I believe once a system (or host of systems) achieves a sufficient level of multi-modality, these same people will no longer be able to tell human and machine intelligence apart - and in the end that is the only thing that counts, since we lack a rigorous definition of "understanding" or consciousness as a whole. Transformers are certainly going to become even more general purpose in the future and I'm sure we haven't even seen close to what's possible with RL. Regarding the AGI timescale, I'm a bit more bullish. Not Elon-type "possible end-all singularity in 5 years" but more Carmack-like "learning disabled toddler running on 10k+ GPUs by the end of the decade." Whether RL+transformers will be able to go all the way there is impossible to tell right now, but if I was starting an AGI company today that's definitely the path I would pursue.

I'm not sure Gato actually demonstrates intelligence. It definitely does demonstrate the ability to perform a wide variety of tasks, but I don't think that is actually intelligence. Gato has yet to demonstrate that it understands things. I do think it is easy to fall prone to the gullibility gap (and also take too strong of an opposing view, which I may be victim to).

How are you defining "understands things"? Or more precisely, what are the observable consequences of understanding things that you are waiting to see?

"Understands things" means a lot. Mostly I'm referring the causality and generalization of inference though.

So, what would be the observable, empirical consequences of the kind of causal understanding and generalization of inference you're describing?

The common current example? A text prompt of "A horse riding an astronaut" without prompt engineering. Though I don't think the successful production of this image will demonstrate intelligence/causal understanding either (but it is a good counter example).

Causal understanding is going to be a bit difficult to prove tbh.

> The common current example? A text prompt of "A horse riding an astronaut" without prompt engineering. Though I don't think the successful production of this image will demonstrate intelligence/causal understanding either (but it is a good counter example).

I'm not sure why you think this falsifies intelligence. There are plenty of puzzles and illusions that trick humans. The mere presence of conceptual error is no disproof of intelligence, any more than the fact that most humans get the Monty Hall problem wrong is.

Your argument is that there are adversarial cases? Sure... But that's not what I'm even arguing here. There's more nuance to the problem here that you lack an understanding of. I do suggest diving deep into the research to understand this rather than arrogantly make comments like this. If you have questions, that's a different thing. But this is inappropriate and demonstrates a lack of intimate understanding of the field.

I didn't make an argument that there are adversarial cases, you did. You brought up an adversarial example, and said the existence of that example proves these algorithms are not generally intelligent. If that follows, it follows that the existence of adversarial examples for humans proves the same thing about us.

And in general, if you're going to be condescending, you should actually make the counter argument. You might make fewer reasoning errors that way.

Counter-argument: DALL-E is smart enough to understand that an astronaut riding a horse makes more sense than a horse riding an astronaut, and therefore assumes that you meant "a horse-riding astronaut" unless you go out of your way to specify that you definitely do, in fact, want to see a horse riding an astronaut.

Because intelligence is more than frequentism. Being able to predict that a dice lands on a given side with probability 1/6 is not a demonstration of intelligence. It feels a bit naive to even suggest this and I suspect that you aren't a ML researcher (and if I'm right, maybe don't have as much arrogance because you don't have as much domain knowledge).

ML layman, asshole expert here. You’re a jerk dude. Stop condescending to your peers.

You will have to do better than make rhetorical arguments.

Gato is definitely cool, but it's worth remembering it was based on supervised learning with data from a bunch of tasks, so data availability is a strong bottleneck with that approach. Still, I'd agree the past year or two have made me more optimistic about HLAI (if not scary AGI).

Yeah, I'm definitely not saying this is already the ultimate solution. But if you look at their scaling experiments and extrapolate the results, it roughly shows how a model with number of parameters on the same order of magnitude as the human brain could more or less achieve overall human level performance. Of course that doesn't mean the architecture has to extrapolate, but it's definitely a path worth walking unless someone proves it's a dead end. In that case I'd replace "end of decade" with 10 to 20 years in my above statement.

Except it is pretty naive to extrapolate like that. A high order polynomial looks flat if you zoom in enough. You can't extrapolate far beyond what you have empirical data for without potentially running into huge problems.

How do you know what the function looks like? Unless someone shows that this scaling behaviour breaks down, it is definitely worth pursuing, since the possible benefits are obvious. Everyone who just says it will break down eventually is almost certainly right, but this is also a completely trivial and worthless prediction by itself. The important distinction is that noone knows where the breakdown happens. And from what we can tell today, it's not even close.

I think your viewpoint is the more naive one and I think you have the burden of proof to argue why local trends are representative of global trends. This is because the non-local argument accepts a wider range of possibilities and the local argument is the the more specific one. Think of it this way, I'm saying that every function looks linear given the right perspective and you're arguing that a particular function _is_ linear. I'm arguing that we don't know that. The burden of proof would be on you to prove that this function were linear beyond what we have data for. (Path towards HLI doesn't have to be linear, exponential, nor logarithmic but you do have to make a pretty strong argument to convince others that the local trend is generalizable).

You are arguing completely beside the original point. Gato was just meant as one surprising example out of many to show how much more the architectures we already have today are capable of. Whether you believe how the scaling curve for this very particular model will work in detail is totally irrelevant to me or anyone else and I'm not trying to convince you of anything. I was just pointing out that there is a clear direction for future research and if you think people like Carmack are on the wrong path - so be it. You don't have to pursue it as well. But I don't care and he certainly doesn't either.

I understood your argument, in the context of the discussion we've been having, that Gato was a good example of us being on a good path towards building intelligent machines. I do not think Gato demonstrates that.

You're missing the point.

The point isn't that we can or cannot extrapolate. The point is that it is worth investigating to see if the trend line holds up.

I think it is pretty disingenuous to read my comments as not wanting to investigate. I definitely do. I did say I'm an ML researcher and I do want to see HLI in my lifetime. But I also think we should invest in other directions as well.

If you review the history of AGI research going back to the 1960s, there have been multiple breakthroughs which convinced some researchers that success was close. Yet those all turned out to be mirages. You're probably being overly optimistic, and the Gato approach will eventually run into a wall.

Gato is just one tiny glimpse out of many, and the combined research effort both from academia and industry and the level of success has never even come close throughout history to the things we are seeing right now.

> I think people (especially tech enthusiasts) are getting caught in the gullibility gap.

I think it's that coupled with inserting their own often fanciful projections to fill the void and gaps between the lack of understanding how the mechanics works. Here is a good example of influencers laboring under this narrative [0] and a glimpse as to why it's so effective and pervasive.

I'm conflicted, because I think this is precisely what keeps many Corps and VCs throwing money into this tech thinking it's just right around the corner, as, I'm also studying AI and ML, but I also realize how implausible AGI is in my lifetime given where we are and how narrow parameters these algos function in to yield some degree of accuracy and more specifically how many errors there are to do so as it's just part of the process--learning how gradient descent works was pretty eye opening.

The Marketing has made many people not just fall into a gullibility gap as they make it seem like some sort of black magic, when it's really just statistics fed with immense amounts of specific data, but influncers make it seem like it's capable of ending the World (Elon) when in reality people should be more worried about big tech's capability at aggregating data points from info people willfully post online to exclude you or cluster you into a certain group because of your use of free apps and selling it the highest bidder which are tied to your real ID. Or the algos with weights (biases) that are used and bundled into outsourced/automated HR solutions.

0: https://youtu.be/jdIyNMkusLE?t=1268

This is a classic case of scope creep caused by underspecified scope (evidenced by how little HLI improves the non-definition of AGI). If humans accurately exhibited causal reasoning in any but the most mind-numbingly obvious conditions we would be living in a very different world!

> Being in the tail end of my PhD

That doesn't really qualify you to make these anti-predictions any more than tens of thousands of other people working in this field. There's also this phenomenon where experts close to a problem are worse at predicting the future because they are so intimately familiar with the problems that they can't see the forest for the trees.

To me there is one major signal that AGI is much closer than 50 years away and that is the breakthrough with Transformers. Now we have a general purpose architecture for predicting the future that works really well, which is a key component in the brain (cortical columns). Clearly we're still at the early stages, but 50 years is a loooong time. 50 years ago we didn't even have the Altair 8800.

Yes we are going back to symbolism, , ontologies and causality. Which is a good and healthy sign. It was abandonned the same way neural networks were at some point. Now that it becomes practical at scale, we can combine them all.

I don't think folks in academia are anywhere near the cutting edge on AGI. It's not a field that is advanced by things like particle accelerators as you might find in physics, that some unis are famous for.

Look at the work result of folks like engineers at Google - state of the art in AI and driving most of the trends in AGI. Academic opinions are shot down quite quickly there, PhD hiring is mostly frowned on, even in teams working on AGI related tech.

It might be expected that PhD experience provides surprising little insight here unfortunately. Academic smugness is not something that should be offered as part of an opinion- just like many computer science related concepts, progress moves so quickly in these topics that by the time academics reach and start to iterate on concepts, companies have already moved very far beyond.

This isn't intended to be condescending to academics, just real life experience I've had working closely with both crowds and seeing this firsthand. It's a very palpable thing in industry, an Academic gives an opinion on AGI and engineers roll their eyes - since Academic perspectives are currently so useless or out of date for any current, meaningful progress.

I know it maybe too much to ask but as a noob to this field I’d love to hear more about why AGI is not feasible even in 50 years.

I've seen a few posts here asking why it isn't possible. Research doesn't work like that, you can't just imagine something is possible and demand reasons why it isn't. How is it possible?

It feels a bit like a lot of these fringe science things (electromagnetic fields causing harm etc) where people's argument is that nobody's shown it isn't true. Well if it's true, propose a mechanism and study it. You're not really doing research if you just make up a conclusion and use the fact that it can't be definitively refuted as evidence for it

Yeah this is a good point. I've been taking the bearish position of HLI here but that doesn't mean I don't think HLI is impossible. I very much do think it is possible (I'm not sure there are many researchers who think it is impossible) but just how close we are. But we don't even have good definitions, so really this is just laymen being privy to expert arguments. I also think there's this naive belief that complex topics can be distilled to laymen terms while maintaining accuracy. That's definitely not true here, as even our "in-group" doesn't have a consensus. But that's cutting edge research for you.

In fairness to the parent, research doesn't work that why, but they're not conducting research.

"What are the remaining unknowns that would need to be solved to do X and why are they difficult?" is valid question to ask experts when trying to further your own understanding.

Sure. It is rather complex to be honest but I'll do my best to give a high level abstract. I should first off explain that there's no clear definition of intelligence. You might see some of my other comments use the term HLI, which is Human Level Intelligence. It is a term gaining in popularity due to this difficult to define definition and allows for more a porn like definition -- I know it when I see it.

Philosophers, psychologists, neuroscientists, and many others have been contemplating the definition of intelligence for centuries and we're still not quite there. But we do have some good ideas. One of the important aspects is the ability to draw causal relationships. This is something you'll find state of the art (SOTA) text to image (T2I) generators fail at. The problem is that these models learn through a frequency based relationship with the data. While we can often get the desired outcome through significant prompt engineering it still demonstrates a lack of the algorithm really understanding language. The famous example is "horse riding an astronaut." Without prompt engineering the T2I models will always produce an astronaut riding a horse. This is likely due to the frequency of that relationship (people are always on horses and not the other way around).

Of course, there are others in the field that argue that the algorithms can learn this through enough data, model scale, and the right architecture. But this also isn't how humans learn and so we do know that if this does create intelligence it might look very different ("If a lion could talk I wouldn't be able to understand it"). Obviously I'm in the other camp, but let's recognize that this camp does exist (we have no facts, so there's opinions). I think Chomsky makes a good point: our large language models have not taught us anything meaningful about language. Gary Marcus might be another person to look into as he writes a lot on his blogs and there's a podcast I like called Machine Learning Street Talk (they interview both Marcus and Chomsky but also interview people that have the opposite opinion).

So another thing, is that machines haven't shown really good generalization, abstraction, or really inference. You can tell a human a pretty fictitious scenario and we're really good at producing those images. Think of all the fake scenarios you go through while in the shower or waiting to fall asleep. This is an important aspect of human intelligence as it helps train us. But also, we have a lot of instincts and a lot of knowledge embedded in our genetics (as does every other animal). So we're learning a lot more than what we can view within our limited lifetimes (of course our intelligence also has problems). Maybe a good example of abstraction is "what is a chair." There's a whole discussion about embodiment (having a body) and how language like this may not be able to be captured in the abstract manner without a body since a chair is essentially anything that you can sit on.

I feel like I've ranted and may have written a mess of a lot of different things haha. But I hope this helps. I'll try to answer more and I'm sure others will lay out their opinions. Hopefully we can see a good breadth of opinions and discussion here. Honestly, no one knows the answers.

> You can tell a human a pretty fictitious scenario and we're really good at producing those images.

Not all us do that.

A chair is both what I see as well as what I perceive. And it is neither. But, I never build a visual model of it when my eyes aren’t seeing it.

Clearly, but it is common enough that we joke about how it is impossible to not think about a pink elephant simply because someone said it. People have better visualization than others but the vast majority of people can conjure up the concept of this non-existent elephant. But clearly intelligence is beyond visualization as we wouldn't say a person born blind is unintelligent simply because they cannot see. Still, the pink elephant is a fairly good analogy. But analogies aren't perfect and that's okay. Just take it in good faith.

> It is a term gaining in popularity due to this difficult to define definition and allows for more a porn like definition

> Philosophers, psychologists, neuroscientists, and many others have been contemplating the definition of intelligence for centuries and we're still not -quite there

Given the decade-old, seminal, widely cited papers by Legg (co-founder of Deepmind) & Hutter (long-term professor of ANU with multiple awards, currently researcher at Deepmind) titled "Universal Intelligence: A Definition of Machine Intelligence"[1] and "A Collection of Definitions of Intelligence"[2] and the whole rigorous mathematical theory of universal bayesian reinforcement learning aka "AIXI"[3] developed during the last decades which you fail to mention, this soft language of "not quite there" looks evasive, bordering on disingenious even.

Citing Chomsky, with his theorems which were interesting at their own time yet quickly faded into irrelevance once the modern assumptions of learning theory were developed, and Marcus whose simplistic prompts are a laughing stock among degreed and amateur practicing researchers alike brings little of value to the already biased discussion.

> Of course, there are others in the field that argue that the algorithms can learn this through enough data

Such as Rich Sutton, one of the fathers of Reinforcement Learning as an academic discipline, whose recent essay "Bitter Lesson"[4] took the field by word of mouth, if nothing else. It takes a certain amount of honesty and resolution to acknowledge that one's competitive pursuit of knowledge, status and fame via participating in academic "publish or perish" culture does not bring humanity forward along a chosen axis, compared to more mundane engineering-centric research, many people commend him for that.

> So another thing, is that machines haven't shown really good generalization, abstraction, or really inference.

To play a devil's advocate: "Scaling is all you need" and "Attention is all you need" are devilishly simple hypotheses which seem to work again and again when we reluctantly dole out more compute to push them further. With a combination of BIG-bench[5] and an assortment of RL environments as the latest measuring stick, why should we, as a society, avoid doling out enough compute to take it by storm in favor of a certain hypothesis implicit to your writing, which could as well be named "Diverse academic AI funding is all you need"? Especially when variations of this hypothesis have failed us for decades starting with the later part of the XX century, at least if we consider AI research.

> but let's recognize that this camp does exist (we have no facts, so there's opinions).

With all due respect, the continuing lavish (by global standards) funding of your career and careers of many other unlucky researchers should not be a priority when such massive boon to all of humanity as a practical near-HLI/AGI is at stake. "Think of the children", "think of the ill", think of everybody who is not yourself - I say as a taxpayer, hoping for AGI checkpoint to be produced by academics and not the megacorporations, and owned by the people, and not the select few.

Which - the proprietary nature of FAANG-developed AI - might be a real problem we are going to face this decade which is going to overshadow any career anxiety you may have experienced so far.

1. https://arxiv.org/abs/0712.3329

2. https://arxiv.org/abs/0706.3639

3. https://arxiv.org/abs/cs/0004001

4. http://www.incompleteideas.net/IncIdeas/BitterLesson.html

5. https://github.com/google/BIG-bench

They didn't say it's not possible within 50 years, they said they weren't expecting it to happen in that timeframe, and that's a very different sort of claim.

Scale isn’t the problem. Very simple organisms display general intelligence. General intelligence is rampant in the insect world. It’s something along the lines of a repl vs compiled language paradigm shift.

> gullibility gap

Search turns up very little except a book by a Christian author. Could you define what you mean by gullibility gap?

It's coined in context.

Really I'd say it is coined by Gary Marcus. At least that's who I first heard discuss it.

I don't see it. Did they mean "uncanny valley"?

Kinda. The gullibility gap more refers to the fact that it is really easy to believe the machines are smarter than they are because they can perform tasks that nothing but humans can do. With the exception of maybe parrots there's no other creatures that can talk so we associate this with intelligence. But I think it is rather naive to believe that the ability to string together reasonable sounding text is actually intelligence. There has yet to be a demonstration of these algorithms actually understanding things.

What sort of demonstration do you have in mind?

> Hell, causality research still isn't that popular but is quite important for intelligence.

what are the esteemed epistemological frameworks in the AGI space? from the outside looking in, i see CNNs and lots of correlation-based approaches: it looks like the field settled on consequentialism. is that not the case?