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AGI is far from inevitable

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Re: AGI is far from inevitable

#201

Post by cmsj »

ryandvm wrote: post #112
Gehinnn wrote: post #6

Basically the linked article argues like this: > 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.

> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago 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. W…

General Intelligence is constantly learning. Transformer based models can't do that.

All bets are still firmly on.

Re: AGI is far from inevitable

#202

Post by smokedetector1 »

oska wrote: post #121
anon84873628 wrote: post #110

Earlier quoted context omitted.

Yes, and?

It comes with all the blindness and limitations of materialist thinking

For some reason materialism is so popular among tech people that it's almost considered foolish/primitive/superstitious to think any other way. Why is that? Is it the fact that programming is a god-like experience that gives one the illusion the mind is as comprehensible as the complex program I wrote in Java? Or is it a shared personality trait of people that get into tech, that they are disconnected from an experience of their own aliveness and soul?

Re: AGI is far from inevitable

#203

Post by cmsj »

tirant wrote: post #191
jiggawatts wrote: post #189

Earlier quoted context omitted.

You can feed an LLM of typical size (~1 TB) all the video and audio you can find, but it won't turn into a human. I suspect but can't prove that even if you wired up a bunch of other sensors, gave it a robot body, and let it "explore" the world on its own, that would still be woefully insufficient. A gazelle just days(!) old can control its body and four legs sufficiently well to outrun a cheetah . This is a complex…

How many hours or actually years of evolution have been needed until reaching walking capability? If first life is believed to have happened 4 billion years ago, and first walking animals started 450 million years ago during the siluariab period, that’s around 3,5 billion years.

That's not really a great comparison though because evolution wasn't trying to learn how to walk.

Re: AGI is far from inevitable

#204

Post by cmsj »

jiggawatts wrote: post #169
ryandvm wrote: post #112

Earlier quoted context omitted.

> but LLMs do solve problems that people thought were extremely difficult to solve ten years ago 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. W…

One thing that keeps me up at night is that the human genome is only 3 giga base pairs, of which only a fraction encodes the design of our brains — and quite inefficiently at that, through layers of indirections. 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…

Meat computers physically change when they learn. They don't separate data from compute hardware, the two are the same thing, so they scale really well. Silicon doesn't currently have that capability.

Re: AGI is far from inevitable

#205

Post by somedude895 »

falcor84 wrote: post #197
castigatio wrote: post #124

Whatever you think about AGI, this is a dumb paper. So many words and references to say - what. If you can't articulate your point in a few sentences you probably don't have a point. There are all kinds of assumptions being made in the study about how AI systems work, about what people "mean" then they talk about AGI etc. The article starts out talking about white supremacy and replacing women. This isn't a proof. Th…

I thought you were exaggerating, but wow, they really 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 &…

> Kyriarchy

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.

Re: AGI is far from inevitable

#206

Post by simonh »

threeseed wrote: post #68
simonh wrote: post #61

Earlier quoted context omitted.

We don’t have to assume it’s like a modern computer, it may well not be in important ways, but modern computers aren’t the only possible computers. If it’s a physical information processing phenomenon, there’s no theoretical obstacle to replicating it.

> there’s no theoretical obstacle to replicating it Quantum theory states that there are no passive interactions. So there are real obstacles to replicating complex objects.

That's only a problem if the relevant functional activity is a quantum effect. We have no problem mass producing complex macroscopic functional objects, and in the ways that are relevant human brains are all examples of the same basic system. Quantum theory doesn't seem to have been an obstacle to mass producing those.

Re: AGI is far from inevitable

#207

Post by jebarker »

dartos wrote: post #183
jebarker wrote: post #162

Earlier quoted context omitted.

> LLMs have an additional problem related to training data. We are already throwing all the data we can get our hands on at them. 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.

You can’t predict breakthroughs. We probably will see a breakthrough, but there’s no more reason to believe it’ll happen tomorrow than 100 years from now.

I didn't make a prediction, let alone a timeline - I just said it's plausible. I suppose I did say "we'll see" so I'm implicitly predicting within a few decades.

Re: AGI is far from inevitable

#208

Post by [deleted] »

falcor84 wrote: post #198
gizmo686 wrote: post #114

Earlier quoted context omitted.

> 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 poi…

> However, eventually we end up reaching a point where throwing more resources at it stops meaningfully improving performance. 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 beli…

[deleted]

Re: AGI is far from inevitable

#209

Post by gizmo686 »

falcor84 wrote: post #198
gizmo686 wrote: post #114

Earlier quoted context omitted.

> 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 poi…

> However, eventually we end up reaching a point where throwing more resources at it stops meaningfully improving performance. 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 beli…

The universal approximation therorem is not as powerful as it sounds. Polynomials essentially satisfy it as well [0], the only hiccup being that the Universal Approximation Theroum is explicitly about neural networks.

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.

Re: AGI is far from inevitable

#210

Post by remon »

If we lower the bar on papers getting published even further it'd be on the floor. Even with a simple physics argument you can immediately disprove the claim. Human brains have a known amount of compute (as in, we are at most an order of magnitude or two off) within a specific volume. We are already capable of that amount of compute, we just don't have a way to use that compete for AGI yet (and THAT may be hard or impossible but it's certainly not energy resource constrained). We also know the abilities this paper claims are not possible to achieve can be achieved by pretty much every human being within years of being born.

How they managed to massage white supremacy and sexism into this is...well...wow.

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