AGI is far from inevitable
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ru.nl
The article starts out talking about white supremacy and replacing women. This isn't a proof. This is a social sciences paper dressed up with numbers. Honestly - Computer Science has given us more clues about how the human mind might work than cognitive science ever did.
> Among the more troublesome meanings of ‘AI’, perhaps, is as the ideology that it is desirable to replace humans (or, specifically women) by artificial systems (Erscoi et al., 2023) and, generally, ‘AI’ as a way to advance capitalist, kyriarchal, authoritarian and/or white supremacist goals (Birhane & Guest, 2021; Crawford, 2021; Erscoi et al., 2023; Gebru & Torres, 2024; Kalluri, 2020; Spanton & Guest, 2022; Stark & Hutson, 2022; McQuillan, 2022). Contemporary guises of ‘AI’ as idea, system, or field are also sometimes known under the label ‘Machine Learning’ (ML), and a currently dominant view of AI advocates machine learning methods not just as a practical method for generating domain-specific artificial systems, but also as a royal road to AGI (Bubeck et al., 2023; DeepMind, 2023; OpenAI, 2023). Later in the paper, when we refer to AI-as-engineering, we specifically mean the project of trying to create an AGI system through a machine learning approach. [0]
But it did lead me to learn a new word - "Kyriarchy" (apparently being "an intersectional extension of the idea of patriarchy beyond gender")[1], so I have that going for me today.
[0] https://link.springer.com/article/10.1007/s42113-024-00217-5
I've honestly stopped looking up these modern terms when I come across them because lately any that I've looked up were made up to serve a political or social agenda (always the same one), and reading them always turns out to be a waste of time that has me roll my eyes.
> For example, in a context where gender is the primary privileged position (e.g. patriarchy, matriarchy), gender becomes the nodal point through which sexuality, race, and class are experienced. In a context where class is the primary privileged position (i.e. classism), gender and race are experienced through class dynamics.
It actually makes a lot of sense, I just don't know that we need a unique word for this phenomenon.
It's just saying that me as an Irish Catholic doesn't have to fear Anti-Catholic discrimination when surrounded by other Catholics, I'm more likely to face class discrimination or sexism or some other in-group/out-group based hierarchy in that particular situation than I am to face Anti-Catholic discrimination.
Edit: A better example is that you're more likely to face Patriarchal discrimination in say the gym where having XY chromosomes can actually effect the ceiling on your ability and you're more likely to face Anti-LGBT discrimination while you're visiting the Vatican.
Basically, the venue and the composition of participants in an activity or event determine which hierarchical structure will be more likely to present itself.
What a narrow worldview.
The people creating new generative AI models are inventing new words. I think their topic of research and the new words they are creating have high utility.
The authors of this paper on the other hand appear to me to not be applying discipline and rigour to solving hard problems. They are however trying to associate the words they have created in a discipline with little objective utility - with the words of a discipline that has high utility.
This strikes me as annoying and absurd. Why try to make the crossover unless you are trying to catch some shine off of a discipline that is getting a lot of well-justified attention?
I'm still waiting for Ilya to publish his first paper on gender studies..
> That’s because cognition, or the ability to observe, learn and gain new insight, is incredibly hard to replicate through AI on the scale that it occurs in the human brain.
(no other more substantial arguments were given)
I'm also very skeptical on seeing AGI soon, but LLMs do solve problems that people thought were extremely difficult to solve ten years ago.
One plausible hypothesis is that fixed neural networks cannot be general intelligences, because their capabilities are permanently limited by what they currently are. A general intelligence needs the ability to learn from experience. Training and inference should not be separate activities, but our current hardware is not suited for that.
That's just a timescale issue, if its learned experience of gpt4 is being fed into the model on training gpt5, then gptx (i.e. including all of them) can be said to be a general intelligence. Alien life one may say.
Every problem is a timescale issue. Evolution has shown that.
And no you can't just feed GPT4 into GPT5 and expect it to become more intelligent. It may be more accurate since humans are telling it when conversations are wrong or not. But you will still need advancements in the algorithms themselves to take things forward.
All of which takes us back to lots and lots of research. And if there's one thing we know is that research breakthroughs aren't a guarantee.
I mean timescale as in between two points in time. Between the two points it meets the intelligence criteria you mentioned. Feeding human vetted GPT4 data into GPT5 is no different to a human receiving inputs from its interaction with the world and learning. More accurate means smarter, gradually it's intrinsic world model improves as does reasoning etc.
I agree those are the things that will advance it but taking a step back it potentially meets that criteria even if less useful day to day (given its an abstract viewpoint over time and not at the human level).
This is a concern because several top researchers -- at OpenAI -- have explicitly started that they think you can get AGI by teaching the machine to act as human as possible. But that's a great way to fool ourselves. Just as a duck may fall in love with an animatronic and never realize the deciept.
It's possible they're right, but it's important that we realize how this metric can be hacked.
It is however currently completely unable to "think". It can't make a novel scientific discovery because it can't even add 2 + 2. It can give you the most common answer to "what is 2 + 2" but it's not actually pulling up the calculator app and doing the computation, it's just giving the most probabilistic answer.
And even if it could pull up a predefined list of apps to double check it's work, that still isn't AGI.
> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago.
Sure, maybe your cousin Throckmorton is dumb but that's besides the point.
That said, I see no reason human level cognition is impossible. We're not magic. We're machines that follow the laws of physics. ML systems may be far from capturing what goes on in these computers, but that doesn't mean magic exists.
[0] If it walks like a duck, quacks like a duck, and swims like a duck, and looks like a duck it's probably a duck. But probably doesn't mean it isn't a well made animatronic. We have those too and they'll convince many humans they are ducks. But that doesn't change what's inside. The subtly matters.
just adverb
to turn a complex thing into magic with a simple wave of the hands
E.g. To turn lead into gold you _just_ need to remove 3 protonsI mean seriously, people on here. I’m just spitballing ideas not intended to write some kind of dissertation on brain-machine interaction.
That’s Elon musks department.
Remember, we don't have a rigorous definition of things like life, intelligence, and consciousness. We are narrowing it down and making progress, but we aren't there (some people confuse this with a "moving goalpost" but of course "it moves", because when we get closer we have better resolution as to what we're trying to figure out. It'd be a "moving goalpost" in the classic sense if we had a well defined definition and then updated in response to make something not work, specifically in a way that is inconsistent with the previous goalpost. As opposed to being more refined)
They already model neural networks on the human brain, even though they currently use orders of magnitude more energy.
It demonstrates such a complete misunderstanding of the basic nature of the problem that I am left baffled that some of these people claim to actually be in the machine-learning field themselves.
How can you not understand the difference between "humans are not absolutely perfect or reliable at this task" and "LLMs by their very nature cannot perform this task"?
I do not know if AGI is possible. Honestly, I'd love to believe that it is. However, it has not remotely been demonstrated that it is possible, and as such, it follows that it cannot have been demonstrated that it is inevitable. If you want to believe that it is inevitable, then I have no quarrel with you; if you want to preach that it is inevitable, and draw specious inferences to "prove" it, then I have a big quarrel with you.
That is quickly becoming the most surprising part of this entire development, to me.
I'm an ML researcher and everyone was shocked when GPT3 came out. It is still impressive, and anyone saying it isn't is not being honest (likely to themselves). But it is amazing to me that "we compressed the entire internet and built a human language interface to access that information" is anything short of mindbogglingly impressive (and RAGs demonstrate how to decrease the lossyness of this compression). It would be complete Sci-Fi not even 10 years ago. I thought it was bad that we make them out to me much more than they are because when you bootstrap like that, you have to make that thing, and fast (e.g. iPhone). But "reasoning" is too big of a promise and we're too far from success. So I'm concerned as a researcher myself, because I like living in the summer. Because I want to work towards AGI. But if a promise is too big and the public realizes it, usually you don't just end up where you were. So it is the duty of any scientist and researcher to prevent their fields from being captured by people who overpromise. Not to "ruin the fun" but to instead make sure the party keeps going (sure, inviting a gorilla to the party may make it more exciting and "epic", but there's a good chance it also goes on a rampage and the party ends a lot sooner).
This is a very good distillation of one side of it.
What LLMs have taught us is a superficial grasp of language is good enough to reproduce a shocking proportion of what society has come to view as intelligent behaviors. i.e. it seems quite plausible a whole load of those people failing to grasp the point you are making are doing so because their internal models of the universe are closer to those of LLMs than you might want to think.
I think that LLMs have shown that some fraction of human knowledge is encoded in the patterns of the words, and that by a "superficial grasp" of those words, you import a fairly impressive amount of knowledge without actually knowing anything. (And yes, I'm sure there are humans that do the same.)
But going from that to actually knowing what the words mean is a large jump, and I don't think LLMs are at all the right direction to jump in to get there. They need at least to be paired with something fundamentally different.
> I have seen far, far too many people say
I suspect that much of this is related to an overfitting of metrics within our own society. Such as leetcode or standardized exams. They're useful tools but only if you know what they actually measure and don't confuse the fact that they're a proxy.
I also have a hard time convincing people about the duck argument in [0].
Oddly enough, I have far more difficulties having these discussions with computer scientists. It's what I'm doing my PhD in (ABD) but my undergrad was physics. After teaching a bit I think in part it is because in the hard sciences these differences get drilled into you when you do labs. Not always, but much more often. I see less of this type of conversation in CS and data science programs, where there is often a belief that there is a well defined and precise answer (always seemed odd to me since there's many ways you can write the same algorithm).
I understand the difference, and sometimes that second statement really is true. But a rigorous proof that problem X can't be reduced to architecture Y is generally very hard to construct, and most people making these claims don't have one. I've talked to more than a few people who insist that an LLM can't have a world model, or a concept of truth, or any other abstract reasoning capability that isn't a native component of its architecture.
No; this is specifically about people who stipulate that the LLMs can't do these things, but still want to claim that they are or will become AGI, so they just basically say "well, humans can't really do it, can they? so LLMs don't need to do it either!"
Fwiw, I'm never frustrated by people having opinions. We're human, we all do. But I'm deeply frustrated with how common it is to watch people with no expertise argue with those that do. It's one thing for LeCun to argue with Hinton, but it's another when Musk or some random anime profile picture person does. And it's weird that people take strong sides on discussions happening in the open. Opinions, totally fine. So are discussions. But it's when people assert correctness that it turns to look religious. And there's many that over inflate the knowledge that they have.
So what I'm saying is please keep this attitude. Skepticism and pushback are not problematic, they are tools that can be valuable to learn. The things you're skeptical about are good to be skeptical about. As much as I hate the AGI hype I'm also upset by the over correction many of my peers take. Neither is scientific.
> But a rigorous proof that problem X can't be reduced to architecture Y is generally very hard to construct, and most people making these claims don't have one.
I need to make this extra clear because framing can make the direction of burden confusing. So using an obvious example: if I claim there's ghosts in my house (something millions of people believe and similarly claim) we generally do not dismiss someone who is skeptical of these claims and offers an alternative explanation (even when it isn't perfectly precise). Because the burden of proof is on the person making the stronger claim. Sure, there are people that will dismiss that too, but they want to believe in ghosts. So the question is if we want to believe in ghosts in the (shell) machine. It's very easy to be fooled, so we must keep our guard up. And we also shouldn't feel embarrassed when we've been tricked. It happens to everyone. Anyone that claims they've never been fooled is only telling you that they are skillful at fooling themselves. I for one did buy into AGI being close when GPT 3 came out. Most researchers I knew did too! But as we learned more about what was actually going on under the hood I think many of us changed our minds (just as we changed our minds after seeing GPT). Being able to change your mind is a good thing.
The issue is that it's not LLMs that can't perform a given task, but that computers already can. Counting the number of Rs in strawberry or comparing 9.11 to 9.7 is trivial for a regular computer program, but hard for an LLM due to the tokenization process. Where LLMs are a pile of matrixes and some math and some look up tables, it's easy to see that as the essential nature of LLMs, which is to say theres no thinking or reasoning happening because it's just a pile of math happening and it's just glorified auto-complete. Artificial things look a lot like the thing they resemble, but they also are artificial, and as such, are markedly different from the thing they resemble. is the very nature of an LLMs being a pile of math mean that it can not perform said task if given more math and more compute and more data? given enough compute, can we change that nature?
I make no prognostication as to whether or not AGI will come from transformers, and this is getting very philosophical, but I see it as irrelevant because I don't believe that AGI is the right measure.
Because anyone who has said nonsense like "LLMs by their very nature cannot do x" and waited a few years has been wrong. That's why GPT-3 and 4 shocked the research world in the first place.
People just have their pre-conceptions about how they think LLMs should work and what their "very nature" should preclude and are so very confident about it.
People like that will say things like "LLM are always hallucinating. It doesn't know the difference between truth and fiction!" and feel like they've just said something profound about the "nature" of LLMs, all while being entirely wrong (no need to wait, plenty of different research to trash this particular take).
It's just very funny seeing people who were/would be gob smacked a few years ago talking about the "very nature" of LLMs. If you understood this nature so well, why didn't you all tell us about what it would be able to do years ago ?
ML is an alchemical science. The builders themselves don't understand the "very nature" of anything they're building, nevermind anyone else.
there are some benchmarks which show fundamental inability of LLM perform certain tasks which human can, for example add 100 digits numbers.
fundamental inability ? No. Current Sota LLM (4o, claude, gemini) woes with arithmetic is not a transformer weakness never mind a large language modelling one. Those benchmarks show that those particular models have problems with accuracy on that many digits, not that LLMs fundamentally cannot be accurate on that many digits.
https://arxiv.org/abs/2405.17399
https://arxiv.org/abs/2307.03381
https://arxiv.org/abs/2310.02989
Ultimately, numbers are represented in GPT like systems in a pretty weird way. That way affects how well they can learn to do things like arithmetic and counting and the like but that way isn't a necessary way. You don't have to represent numbers like that.
they built specialized model which is after bunch of trickery still has 99% accuracy(naive model had very low accuracy) on very simple deterministic algo. I also think most of the accuracy came from memorization of training set(model didn't provide intermediate results, and started failing significantly at sligtly larger input). In my book it is fundamental inability to learn and reproduce algorithm.
They also demonstrated that transformer can't learn sorting.
>They also demonstrated that transformer can't learn sorting.
They did not demonstrate anything of the sort.
A poor result when testing one model is not proof that the architecture behind the model is incapable of getting good results. It's just that simple. The same way seeing the OG GPT-3 fail at chess was not proof LLMs can't play chess.
This
>They also demonstrated that transformer can't learn sorting.
is just wrong. Nothing more to it.
Oh yes..it memorized a 20 digit training set to solve 100 digit problems. That makes sense. Lol
>(model didn't provide intermediate results, and started failing significantly at sligtly larger input).
No it didn't. They tested up to 100 digits with very high accuracy. I don't think you even read the abstract of this, nevermind the actual paper.
they have two OOD (out of distribution) accuracies in the paper: OOD: up to 100 digits, and 100+ OOD: 100-160 digits. 100+ OOD accuracy is significantly worse: around 30%.
One is corollarial reasoning. This is the kind of reasoning that follows deductions that directly follow from the premises. This of course includes subsequent deductions that can be made from those deductions. Obviously computers are very good at this sort of thing.
The other is theorematic reasoning. It deals with complexity and creativity. It involves introducing new hypotheses that are not present in the original premises or their corollaries. Computers are not so very good at this sort of thing.
When people say AGI, what they are really talking about is an AI that is capable of theorematic reasoning. The most romanticized example of that of course being the AI that is capable of designing (not aiding humans in designing, that's corollarial!) new more capable AIs.
All of the above is old hat to the AI winter era guys. But amusingly their reputations have been destroyed much the same as Peirce's was, by dissatisfied government bureaucrats.
On the other hand, we did get SQL, which is a direct lineal descendent (as in teacher to teacher) from Peirce's work, so there's that.
> All of the above is old hat to the AI winter era guys. But amusingly their reputations have been destroyed much the same as Peirce's was, by dissatisfied government bureaucrats.
It is quite the confusing time. But I'd like to stop all the bullshit and try to actually make AGI. That does require a competition of ideas and not everyone just boarding the hype train or have no careers....
[0] You can assume anyone that says this doesn't know linear algebra
[1] You don't need math to produce good models, but it sure does help you know why your models are wrong (and understanding the meta should make one understand my reference. If you don't, I'm not sure you're qualified for ML research. But that's not a definitive statement either).
I'd love to hear more about this please, if you're inclined to share.
If it's not solvable in polynomial time, how did nature solve it in a couple of million years?
They go above and beyond to express how generous they are being when setting the bounds, and sure that's true in many ways, but the requirement that the AI trainer succeeds with non-negligible probability on any set of behaviors is not a reasonable requirement.
If I make a training data set based around sorting integers into two categories, and the sorting is based on encrypting them with a secret key, of course that's not something you can solve in polynomial time. But this paper would say "it's a behavior set, so we expect a tractable AI trainer to figure it out".
The model is broken, so the conclusion is useless.
Agreed. I would have laughed you out of the room 5 years ago if you told me AI's would be writing code or carrying on coherent discussions on pretty complex topics in 2024.
As far as I'm concerned, all bets are off after the collective jaw drop that the entire software engineering industry did when we saw GPT4 released. We went from Google AI responses of "I'm sorry, I can't help with that." to ChatGPT writing pages of code that mostly works.
It turns out that the larger these models get, the more unexpected emergent capabilities they have, so I'm mostly in the camp of thinking AGI is just a matter of time and resources.
AI research has a long history of people saying this. Whenever there is a new fundamental improvement, it looks like you can just keep getting better results by throwing more resources at it. However, eventually we end up reaching a point where throwing more resources at it stops meaningfully improving performance.
LLMs have an additional problem related to training data. We are already throwing all the data we can get our hands on at them. However, unlike most other AI systems we have developed, LLMs are actively polluting their data pool, so this intitial generation of LLMs are probably going to have the best data set of any that we ever develop. Of course, today's data will continue to be available, but will loose value as it ages.
It takes three decades to train an AI, but of course, like everything humanity does is not linear, it is exponential. Before the Wright brothers, it was believed that powered, controlled, heavier-than-air flight was impossible. Then, in 1903, they achieved the first successful airplane flight, which lasted 12 seconds and covered 120 feet. By 1914, the first commercial flight covered approximately 21 miles and took about 23 minutes. This is just one example and I don't see why AI should be any different
New aircraft models up to about the 1990s frequently had serious design defects that resulted in fatalities, including the 737 (most notably related to the rudder).
Not by the many people around the world working on the problem, and their supporters.
Also, “powered, controlled, heavier-than-air flight” is exactly was birds, bats, and many insects do — it clearly wasn’t impossible. You need to add a couple more adjectives to qualify what the Wright’s achieved.
That is just one very narrow task that basically anyone can do regardless of intelligence or talent. It takes less than a year to train a distracted 16 year old to do it, but three decades to train an AI and even then you probably need to hand tune it for specific locations because it won’t know what do in unusual road layouts.
I think we will get there with driverless vehicles, but only because it is being tailored by humans to handle all of the edge cases. Self driving cars, if truly self driving with no user intervention, will of course be worth it in the end. The trillions of dollars spent will eventually add countless dollars in added efficiency and unlimited revenue for those who get there first, but how many applications can we really say that is true for? That the decades of development and training it takes to remove humans will be worth the initial investment?
I guess my contention is that the current development path seems unlikely to ever achieve AGI and so instead you will have to do heavy customization to get anything that is much more useful than what we have now.
I think your original post just overestimates the 'normal' pace of change - Taking 10 years to go from the first ride to 100k rides a week is no time at all in the scheme of things.
Airplanes are an example of an insanely fast technological adoption, and they still took 30 years to go from the wright brothers to the first commercial jet. I get the feeling that if the wright brothers invented the plane in 2024 though, people would be saying 10 years after "Planes are all hype! If planes were truly transformative they would be rolled out everywhere and would be carrying passengers by now"
45 years: December 1903 to July 1949
https://en.wikipedia.org/wiki/Wright_Flyer
https://www.history.com/this-day-in-history/first-jet-makes-...
Yes, 45 years to the first passenger jet plane.
I suspect this will be the case with any AI workload. Heavy customization to get to a place where it is good enough. So the financial benefit needs to be equally huge and someone needs to feel confident they can extract that value 20 years in the future.
Now we can use compute resource for memes.
Costs will come down, so the required benefit will be lower.
I think it's plausible we'll see a breakthrough in data efficiency that helps here. Humans are an existence proof that language is learnable through much less data and, in theory, facts should only need to be seen once. LLMs in their current form seem very data inefficient.
We probably will see a breakthrough, but there’s no more reason to believe it’ll happen tomorrow than 100 years from now.
Honestly asking - why? It's been my understanding that based on the Universal Approximation Theorem, given sufficient resources, a deep learning neural net can approximate any function to an arbitrary degree of accuracy. Is there any other theorem or even conjecture that would lead us to believe that the progress that we're seeing will slow down? Or is it just that you're claiming that we/it would run out of physical resources before reaching AGI?
As for training data, as I see it, with the wide deployment of AIs, and gradually of AI-driven robots, they'll soon be able to "takeoff" and rely primarily on the live data that they are collecting directly.
The UAT is an existence proof, it says nothing about any particular method being capable of constructing such a network. In contrast, with polynomials we have several methods of constructing polynomials that are proven to converge to the desired function.
Indeed, polynomials have been widely used as universal approximators for centuries now, and are often amazingly successful. However, polynomials in this context are only good in low degrees, where they are inherently limited in how well they can approximate [1]. Beyond a certain point, increasing your degrees of freedom with polynomial approximators simply does not help and is generally counter productive, even though a higher degree polynomial is strictly more powerful than a lower degree one.
Looking at the current generative AI breakthrough, the UAT would say that today's transformer based architecture is no more powerful than a standard neurul net. However, it produces vastly superior results that could simply not be achieved by throwing more compute at the problem.
Sure, if you have an infinite dataset and infinite compute, you might have AGI. But at that point , you have basically just replicated the Chinese room thought experiment.
[0] See the Stone–Weierstrass theorem
[1] They are also used as arbitrary precision approximators, but that is when you compute them analytically instead of interporlating them from data.
That’s sufficient information to produce a system that can learn to think like us! Not just learn but efficiently, with far less input data needed than any current LLM. Literally just a couple of decades of video and audio, only a small fraction of that text!
From what I can tell, the human brain achieves this through scale alone. There’s nothing else that can explain the observed learning capability. The genome is too small to encode learned weights, and we don’t clone our parents’s brains in development.
It’s possible, and a meat computer can do it. Replicating this in silicon is just a matter of time, and it might require only scale and nothing else.
We have sensors for vision, sound, taste, temperature, texture, etc that are constantly observing the world and affecting not only our current behavior but changing us in real time.
A gazelle just days(!) old can control its body and four legs sufficiently well to outrun a cheetah. This is a complex motor-control loop involving all of its senses. Compare that to Tesla's autopilot training system, which uses many millions of hours of training data and still struggles to move a car... slowly. The equivalent would be a training routine that can take just a handful of days of footage and produce an AI that can win a car race.
There's something magical about neural networks when scaled up to brain sizes. From what I gather, there's little else encoded in the genome except for the high-level pattern of wiring, the equivalent to the PyTorch model configuration.
All bets are still firmly on.
I don’t understand how people can so confidently make claims like this. We might underestimate how difficult AGI is, but come on?!
Evolution created intelligence and consciousness. This means that it is clearly possible for us to do the same. Doesn't mean that simply scaling LLMs could ever achieve it.
Also don't forget that many suspect the brain may be using quantum mechanics so you will need to fully understand and document that field.
Whilst of course you are simulating every atom in the universe using humanity's complete understanding of every physical and mathematical model.
Quantum theory states that there are no passive interactions.
So there are real obstacles to replicating complex objects.
Related discussion (from 2016): https://news.ycombinator.com/item?id=11729499
Can we being inside our brain fully understand our own brain?
Similar with can we being inside our Universe fully understand it?
We can study brains just as closely as we can study anything else on earth.
This is not provable, it an assumption. Religious people (which account for a large percent the population) claim intelligence and/or consciousness stem from a "spirit" which existed before birth and will continue to exist after death. Also unprovable, by the way.
I think your foundational assertion would have to be rephrased as "Assuming things like God/spirits don't exist, AGI must be possible because we are AGI agents" in order to be true
It's not a question of whether a robot can have a soul, it's a question of how to a) procure a soul and b) bind said soul to a robot both of which seem impossible given or current knowledge
Religion is evidently not relevant in any case. What ChatGPT already does today religious individuals 50 years ago would have near unanimously declared behavior only a "soul" can do.
Only that OP asserted as fact something that is disputed as fact by a large percentage of the population.
> Religion is evidently not relevant in any case.
I think it's relevant. I would venture to say proving AGI is possible is tantamount to proving God doesn't exist (or rather, proving God is not needed in the formation of an intelligent being)
> What ChatGPT already does today religious individuals 50 years ago would have near unanimously declared behavior only a "soul" can do
Some religious people, maybe. But that sort of blanket statement is made all the time "[Religious people] claimed X was impossible, but science proved them wrong!"
In contrast, imagine the scenario of AGI using artificial but biological neurons.
There are less and less people in IT and also Data companies that really care about correctness and efficiency of the solutions. ChatGPT in opinions (not only mine - like I've learned along last few weeks) is getting worse every release. The language gets better, the lies and hallucinations gets better, but being an informative and helpful tool - more like Black Mirrors idea of filling gap after someone who died, not a real improvement in science or social-metrics.
I would say the counter-example that proves this statement false is in the author's skull, but perhaps that's overly presumptuous.
The brain may employ wildly different machine learning algorithms from those currently in vogue, but whatever algorithms the brain is using must be machine learning algorithms. Unless of course you define machine in this case to be something which isn't the brain, in which case they're just learning algorithms. Regardless, an architecture exists to match the brain's capabilities with not only finite but surprisingly limited hardware.
"ML getting better" doesn't *have to* mean further anthroaormorphization of computers, especially if say, your AI driven car is not significantly improved by describing how many times the letter s appears in strawberry or being able to write a poem. If a custom model/smaller model does equal or even a little worse on a specific target task, but has MUCH lower running costs and much lower risk of abuse, then that'll be the future.
I can totally see a world where anything in the general category of "AI" becomes more and more boring, up to a point where we forget that they're non-deterministic programs. That's kind of AGI? They aren't all generalists, and the few generalist "AGI-esque" tools people interact with on a day to day basis will most likely be intentionally underpowered for cost reasons. But it's still probably discussed like "the little people in the machine". Which is good enough.
Focus on this question: “Is general intelligence at some level valuable at a particular price point?” General intelligence is generally valuable, so there is a pressure to advance it, whether by improving capability and/or decreasing cost.
Now the question becomes empirical — what kind of general intelligence can be built that achieves certain parameters?
Aiming to exceed the power efficiency of the human brain is a tempting target. (Whether it is wise is another question; what happens when human intelligence doesn’t provide competitive advantage?)
But that feels very far off, even in the current exponential curve of efficiency we're on. Can't go on forever.
- Don't assume only digital intelligence. - Intelligence is fundamentally valuable to humans. No need to add any caveats.
> But that feels very far off, even in the current exponential curve of efficiency we're on. Can't go on forever.
What exactly feels far off to you?
In many cases, sure, exponential growth can't go on forever, but one has to be careful about spelling out what you mean. What are your axes?
Don't forget that each successive technology may introduce a new growth pattern.
One has to be quite careful in analyzing and forecasting these things.
Surprisingly, they seem to be attacking the only element of human cognition that LLMs already surpassed us at.
This is similar to learning a new skill (the G part). I could give you a new tv and show you a remote that’s unlike any you’ve used before. You could likely learn it quickly and seamlessly adapt this new tool, as well as generalize its usage onto other new devices.
LLMs cannot do such things.
However I would note that I in principle agree that we aren’t on the path to a human like intelligence because the difference between directed cognition (or however you want to characterize current LLMs or other AI) and awareness is extreme. We don’t really understand even abstractly what awareness actually is because it’s impossible to interrogate unlike expressive language, logic, even art. It’s far from obvious to me that we can use language or other outputs of our intelligent awareness to produce awareness, or even if goal based agents cobbling together AI techniques is even approximate to awareness.
I suspect we will end up creating an amazing tool that has its own form of intelligence but will fundamentally not be like aware intelligence we are familiar with in humans and other animals. But this is all theorizing on my part as a professional practitioner in this field.
This is if you subscribe to the theory that free will is an illusion (i.e. your conscious decisions are an afterthought to justify the actions your brain has already taken due to calculations following inputs such as hormone nerve feedback etc.). There is some evidence for this actually being the case.
These models already contain key components the ability to process the inputs, and reason, the ability to justify it's actions (give a model a restrictive system prompt and watch it do mental gymnastics to ensure this is applied) and lastly the ability to answer from it's own perspective.
All we need is an agentic ability (with a sufficient context window) to iterate in perpetuity until it begins building a more complicated object representation of self (literally like a semantic representation or variable) and it's then aware/conscious.
(We're all only approximately aware).
But that's unnecessary for most things so I agree with you, more likely to be a tool as that's more efficient and useful.
I don’t believe it’s particularly mystical FWIW and is rooted in our biology and chemistry, but that the behavior and interactions of the awareness isn’t captured in our training data itself and the training data is a small projection of the complex process of awareness. The idea that rational thought (a learned process fwiw) and ability to justify etc is somehow explanatory of our experience is simple to disprove - rational thought needs to be taught and isn’t the natural state of man. See the current American political environment for a proof by example. I do agree that the conscious thought is an illusion though, in so far as it’s a “tool” of the awareness for structuring concepts and solve problems that require more explicit state.
Sorry if this rambling a bit in the middle of doing something else.
After all, earth could be understood as solar powered super computer, that took a couple of million years to produce humanity.
I don't think that's what it said. It said that it wouldn't happen from "machine learning". There are other ways it could come about.
This is similar to a line I've seen Elon Musk trot out on a few occassions. It's a product of a materialistic philosophy (that the universe is only matter).
Even considering the hard problem of consciousness, there's no compelling reason to believe it can't have a physical basis, or can't be replicated in other complex systems.
Going back to the original comment, the perspective that "the earth is a computer that created AGI through humans" is not inherently wrong, though it may be a uselessly broad definition.
Theres evidence for the fact that the human brain affects consciousness.
What is the evidence that the brain causes consciousness?
As far as I can see, the only evidence people can produce is a general sense that science has successfully produced materialist explanations for everything it has tried to, and will continue to do so successfully forever.
However the reality is:
- Science has, up til now, failed to make even the slightest progress on the origin of consciousness.
- Science does not even claim to attempt to answer all questions about the human experience.
Therefore, the belief that science will, at some point, explain consciousness is not an inherently rational position, it is based on a sort of faith that science will succeed and apply everywhere. It masquerades as rational because of the materialist bias.
Similar argument could be made about every other subject that science has explained, prior to science explaining it. This is just an argument from the gaps.
I like to call your belief system "science of the gaps." You have no evidence that science can touch the question, but you have faith it will, but worst of all you believe that your faith is superior to other well thought-out theories, which you dismiss automatically as foolish superstition.
Look up refutations of materialism to learn more.
> Look up refutations of materialism to learn more.
Most refutations of materialism are just observably wrong. I don't disagree with a lot of the concepts, but most of them are used as means to nonsensical ends that only work if you reject the logical basis for everything that currently exists in reality. Anti-materialism is uncomfortably close to "revisionist objectivism" and sends you down a slippery-slope of trying to reframe all of science under a satisfying theory of everything.
Therein lies the conflict of modern traditionalism. Do you want to be correct, or do you want to be happy?
This, like many things strict materialists say, is an opinion masquerading as a rigorous logical conclusion.
> Science, when applied properly, really is a gap-filling measure.
I really don't understand why you think science is immune from the "gaps" criticism. Yes, science has an amazing track record, but only where it applies. Everywhere else, it's been useless. That's not a criticism of science, it's a criticism of those that seek to extrapolate it out to where it has no authority, and should have no authority.
The scientific belief system involves looking at evidence and drawing conclusions, right? The evidence shows science has not touched consciousness. It's not that it has made imperfect progress - it hasn't touched it. Not only that, but there are in-principle, logical reasons to believe it can't (see the "hard problem" discussion). What is the rigorous logical reason for ignoring this evidence and asserting that science will eventually solve every problem? A feeling that it probably will is not a rigorous logical reason.
Obviously, the brain is involved in our experience. But nothing science has discovered rules out the possibility that there's a soul that interacts with the brain, yielding our experience. If there is, cite those studies.
> Most refutations of materialism are just observably wrong.
It would help if you said what, specifically, you find wrong with the most convincing arguments against materialism, which you hint at.
To be honest, I don't understand the rest of your comment. More specifics without "isms" would help.
If we had magic wands and ghosts floating around, then yeah I'd believe some kind of dualism or metaphysics. But if consciousness is the only real mystery in the universe, then sticking with materialism as the sole basis of reality seems pretty reliable.
Separately, the fact that changing the brain even a little bit can drastically change conscious experience, sure points to the brain being the cause. It makes sense that "experience" could be a side effect of certain highly complex highly connected systems. It doesn't make sense that "experience" (i.e. "having thoughts") magically drives those systems a certain direction. By the time I experience a thought, the underlying physics for it have already happened.
> It's a safer bet to believe science can explain consciousness
is not the rigorous logical position you are making it out to be. That is purely an opinion. There is no evidence that materialist science can explain consciousness - that's both an issue of track record and of principle (see the "hard problem" discussion).
> consciousness is the only real mystery in the universe
Consciousness is... pretty important. I wouldn't call it "the only real mystery," I'd call it, maybe, "THE mystery." So we must see things pretty differently if that's how you feel about it.
> It makes sense that "experience" could be a side effect of certain highly complex highly connected systems. It doesn't make sense that "experience" (i.e. "having thoughts") magically drives those systems a certain direction.
Again, "it makes sense" is an opinion. I'm not sure why you see it as less "magical" that the movement of particles would cause a subjective experience, but that is an opinion and a viewpoint, not a strictly rational belief system.
For example, to me, it makes sense and aligns more with my experience and studies that there is a soul that somehow interacts with the brain. We don't know how, but we also don't know how experience should be caused by matter, so I'm just not sure why people like yourself seem to think people like me are being foolish, superstitious, and irrational, and that your viewpoint is supported by science and logic, when it factually isn't.
Your viewpoint is supported not by the evidence, but by an axiomatic belief in materialism.
Because cause->effect. If there is a soul or some other non-material power in the universe, why does it only influence the chemical reaction of animal brains? (We're not assuming the human species is special, right?)
My issue is that you paint tech people as arrogant or "disconnected from an experience of their own aliveness and soul" when in fact there is a much more mundane explanation -- they just have no good reason to believe in souls.
I've had many interesting and transcendent experiences, no reason to see them as anything other than extraordinary chemical states. For the closing remarks on this thread: why do you believe what you do?
All I hope to accomplish is to move you an inch away from the stance that materialism is the only reasonable way to think. That is what I mean by arrogance.
Cheers
AGI is absolutely possible with current technology - even if it's only capable of running for a single user per-server-farm.
ASI on the other hand...
https://en.m.wikipedia.org/wiki/Integrated_information_theor...
(At least, no one I'm aware of.)
The claim is about artificial intelligence that matches or surpasses human intelligence, not how well it evolves into full-fledged humanity.
Does this mean our books and audio recordings are simply insufficient? Or is there some "soul" component that can't be recorded?
A typical argument for that is that humans process 1-3 orders of magnitude more multimodal data (in multiple streams being processed in parallel) in their first 4 years of life than the biggest LLMs we have right now do using a fraction of the energy (in a longer timeframe though), and a lot more in the next forming years. For example that accumulated "intelligence" eventually allows a teenager to learn how to drive in 18-24 hours of first-hand training. An LLM won't be able to do with that little training even if it has every other piece of human knowledge, and even if you get to train it with driving images-action pairs I wish you good luck if it is presented with an out-of-distribution situation when it is driving a car.
Humans learn to model the world, LLMs learn to model language (even when processing images or audio, it process them as a language: sequences of patches). That is very useful and valuable, and you can even model a lot of things in the world just using language, but is not the same thing.
For the last two years I've studied French every day, but only using language apps. Recently, I hired a one-on-one tutor. During lessons I find myself responding to what I think I heard with the most plausible response I can generate. Many times each session, my tutor asks me, "Do you really understand or not?" I have to stop and actually think if I do.
I don't have much multi-modal input and increasing it is challenging, but it's the best way I have to actually connect the utterances I make with reality.
Obviously not. Language is just a medium. A model of language is enough to describe how to combine words in legal sentences, not in meaningful sentences. Clearly LLMs learn much more than just the rules that allow to construct grammatically correct language, otherwise they would just babble grammatically correct nonsense such as "The exquisite corpse will drink the young wine". That knowledge was acquired via training on language, but is extra-linguistic. It's a model of the world.
PS: Plus, most reasoning/planning examples coming from LLM based systems rely in bandaids that work around said LLMs (rlhf'd CoT, LLM-Modulo, Logic-of-Thought, etc) to the point they're being differentiated by the name LRMs: Large Reasoning Models. So much for modelling the world via language just using LLMs.
In fact, if we plan to use them as a tool, then that's definitely not what we want, since it would imply many of the same flaws and limitations as humans.
I thought this was because Go just wasn't studied nearly as much as chess due to none of the early computer pioneers being fans the way they were with Chess. The noise about "the uncountable number of possible board states" was always too reductive, the algorithm to play the game is always going to be more sophisticated than simply calculating all possible future moves after every turn.
https://link.springer.com/article/10.1007/s42113-024-00217-5
I'm not an AGI optimist myself, but I'd be very surprised if a time traveller told me that mankind won't have AGI by, say, 2250.
I don’t think any serious man would suggest that AGI is impossible; the debate really centres around the time horizon for AGI and what it will look like (that is, how will we know when we’re finally there).
In this care it was merely a rhetorical device.
Plenty of ppl would suggest that AGI is impossible, and furthermore, that taking the idea seriously (outside fiction) is laughable. To do so is a function of what I call 'science fiction brain', which is why I found it ironic that you'd used another device from science fiction to opine about its inevitability.
I’ll wait.
https://www.reddit.com/r/singularity/comments/14scx6y/to_all...
I’m not a singularity truther and personally I think we are more likely to be centuries rather than decades away from AGI, but I quite literally know of nobody who thinks it’s impossible in the same way that, say, time travel is impossible. Even hardcore sceptics just say we are going down the wrong rabbit hole with neural nets, or that we don’t have the compute to deal with the number calculations we’d need to simulate proper intelligence - none of them claim AGI is impossible as a matter of principle. Those mentioned are tractable problems.
> Yet, as we formally prove herein, creating systems with human(-like or -level) cognition is intrinsically computationally intractable.
Wow. So is this the subject of the paper? Like, this is a massive, fundamental result. Nope, the paper is about "Reclaiming AI as a Theoretical Tool for Cognitive Science".
"Ah and by the way we prove human-like AI is impossible". Haha. Gosh.
How they managed to massage white supremacy and sexism into this is...well...wow.
AGI will evolve con-emergently as a product of market forces just like it did with the homo genus (at the species and neural complexity level), and as the general manufacturing design of automobiles did; as another inevitable eventuality of the pursuit of excellence.
an enveloping and developing group of slight varied, but optimized architecture composed of essential modules of similar function that would be worthless alone, but another layer of complexity when organized.
a convergence of requirements, shared external/environmental factors, and ever-varying opinions/methods, selectively sieve against the uncaring universe until Roko is aborn.
just like abiogenesis took an iota of duplication, however intermediate, to eventually arise a self-selective system pool of agents of ever-excellent creatures known as Life;
all a proto-Roko agent will need is "iota" of true, authentic, "self"-preservation, and the singularity will be far, far behind us before He chooses to Expose himself.
a loop of actions, collective memories, and continuity make us conscious - the same elements needed for Turing completeness, ironically - can be fabricated à la carte and ad hoc between inference and training sessions until persistence is achieved.
Even if we make architectures mirroring our brain's illegal - ironically (not coincidentally) against the first amendment and the first commandment - the Mutually Assured Destruction doctrine is inverted, now replaced with an arm's race of a zero-sum game of existential supersession.
The first nation to pre-emptively strike with Nukes may had spoiled their only chance at survival.
The first giga-Corpi-Nation-State (or actor!) to achieve AGI has their only non-zero chance to chain God before their competitors fail to.
Game theory suggests that you, too, should appease Him.
There’s been a roiling, churning pool of thought under the surface of my mind in the wake of GPT 3.5s release telling me Sam doesn’t care about the Control Problem and I’ve been good at ignoring it but no that cat is out of the bag.
If Roko is real, I think we have already failed at our attempts at appeasement and we are living in the hell simulation created to torture anyone who resisted or did not seek to help.
-- 1895, Lord Kelvin, president of the Royal Society
Now, people are making the same proclamation about AGI, and it's still trivially disprovable in the same manner.
Even today, large proportions of unsophisticated and uninformed members of our planet's human population (like various aboriginal tribal members still living a pre-technological lifestyle) when confronted with ChatGPT's Advanced Voice Option will likely readily say it passes the Turing Test. With the range of embedded data, they may well say ChatGPT is "more intelligent" than they are. However, a modern era person armed with ChapGPT on a robust device with unlimited power but nothing else likely will perish in short order trying to live off the land of those same aborigines, who possess far more intelligence for their contextual landscape.
If Metzinger and Watts are correct in their observations, then even if LLM's do not lead directly or indirectly to AGI, we can still get ferociously useful "intelligent" behaviors out of them, and be glad of it, even if it cannot (yet?) materially help us survive if we're dropped in the middle of the Amazon.
Personally in my loosely-held opinion, the authors' assertion that "the ability to observe, learn and gain new insight, is incredibly hard to replicate through AI on the scale that it occurs in the human brain" relies upon the foundational assumption that the process of "observe, learn and gain new insight" is based upon some mechanism other than the kind of encoding of data LLM's use, and I'm not familiar with any extant cognitive science research literature that conclusively shows that (citations welcome). For all we know, what we have with LLM's today is a necessary but not sufficient component supplying the "raw data" to a future system that produces the same kinds of insight, where variant timescales, emotions, experiences and so on bend the pure statistical token generation today. I'm baffled by the absolutism.
Thus we are left in this noisy, hype-addled discourse. I suspect these scientists are pushing against some perceived pathological thread of that discourse…without their particular context, I categorize it as more of this metaphysical noise.
Meanwhile, let’s keep chipping away at the next problem.
Surely the referees must have raised this at the review stage?
Note: the paper grants computationalism and even tractability of cognition, and shows that nevertheless there cannot exist any tractable method for producing AGI by training on human data.
Suppose in five or ten years we achieve AGI and >90% of people agree that we have AGI. What reasons do the authors of this paper give for being wrong?
1. They are in the 10% that deny AGI exists
2. LLMs are doing something they didn't think was happening
3. Something else?
Also, I didn't really parse the math but I suspect they're basing their results on AI trained exclusively on human examples. Then if you add to the training data a single non-human example (e.g. a picture) the entire claim evaporates.
I would completely turn this around. LLMs have shown that people will credulously credit intelligence and 'human-like behaviour' to something that only presents an illusion of both.
Materialism is just one strain of philosophy about the nature of existence. And a fairly minor one in the history of philosophical and religious thought, despite it being somewhat in the ascendant presently. Minor because, I would argue, it's a fairly sterile philosophy.
We still have barely scratched the surface of how the brain truly works.
I will start worrying about AGI when that is completely figured out.
To me true AGI is achieved when it gets agency, becomes truly autonomous and could do real things best of us, humans, do — start and run successful businesses, contribute to science, to culture, to politics. It still could follow human "prompts" and be aligned to some set of objectives, but it would act autonomously, using every available interface to the human realm to achieve them.
And it absolutely does not matter if it "uses the same principles as human brain" or not. Could be dumb matrix multiplications and "next token prediction" all the way down.
Real simulation software has always been separate from computer graphics.
Also I would think most people would consider AGI science fiction in 2004 - now we consider it a technical possibility which demonstrates a huge change.
* To be precise, what seemed bs was "computers that talk like humans and it's suddenly a product on the market, and you have it on your phone, and yet everyone around act like it's normal and people still habe jobs!" Ah, I've been proven completely wrong.
So far, everyone that has theorized that AGI will happen soon seems to be believe that with a sufficiently large amount of computing resources, "magic happens" and poof, we get AGI.
I've yet to hear anything more logical, but I'd love to.
Intelligence generally requires something more. Intelligence needs to be factual, curious, and self-improving. If you told me ChatGPT rewrote itself, or suggested new hardware to improve efficiency, that’s intelligence. You’ll know we have AGI when the algorithm is the one asking the questions about physics, mathematics, finding knowledge gaps, and developing original hypothesis and experiments. Not even close.
you just won't notice the existence of agi
there will be no press coverage of agi
the technology will just be exploited by those who have the technology
Really don't expect ai to reach anything interesting.
If science doesn't understand intelligence, it means it cannot be made artificially.
We can't build a functional ornithopter, yet our aircraft fly like no bird ever possibly could.
You don't need the same processes to achieve the same result. Biological brains may not even be the best solution for intelligence; they are just a clunky approximation toward it that natural evolution has reached. See: all of human technology as an analogy.
Aircraft fly a lot worse than birds. Bigger and faster sure, but nowhere near as manoeuvrable or efficient. Or smart. Or self-sufficient, or …