Ilya Sutskever's SSI Inc raises $1B

>An LLM will learn what it CAN (and needs to to reduce the loss), but not what it CAN'T. How difficult is that to understand?!

Right and the point is that you don't know what it CAN'T learn. You clearly don't quite understand this because you say stuff like this:

>Chess is a good example, since it's easy to understand. The generative process for world class chess (whether human, or for an engine) involves way more DEPTH (cf layers) of computation than the transformer has available to model it

and it's just baffling because:

1. Humans don't play chess anything like chess engines. They literally can't because the brain has iterative computation limits well below that of a computer. Most Grandmasters are only evaluating 5 to 6 moves deep on average.

2. We have a chess transformer playing world class chess (grandmaster level) - https://arxiv.org/abs/2402.04494.

You keep trying to make the point that because a Transformer architecturally has a depth limit for some trained model, a, it cannot reach human level.

But this is nonsensical for various reasons.

- Nobody is stopping you from just increasing N such that every GI problem we care about is covered.

- You have shown literally no evidence that the N even state of the art models posses today is insufficient to match human iterative compute ability.

GPT-4o instant shots arbitrary arithmetic more accurately than any human brain and that's supposedly something it's bad at. You can clearly see it can reach world class chess play.

If you have some iterative computation benchmark that shows transformers zero shotting worse than an unaided human then feel free to show me.

You are supposed to show me an example no human will fail. I didn't ignore anything. I'm just baffled that you genuinely believe this:

>An averagely smart human does not have these failure modes where they answer a question with something that looks like an answer "cross A to B, then B to A. done. there you go!" but has zero logic to it.

Humans are poor at logic in general. We make decisions, give rationales with logical contradictions and nonsense all the time. I just genuinely can't believe you think we don't. It happens so often we have names for these cognitive shortcomings. Get any teacher you know and ask them this. No need to take my word for it. And i don't care about getting the last word.

I did not claim the state of the art was better at all forms of reasoning than all humans. I claimed the architecture isn't going to stop it from being so in the future but I guess constructing a strawman is always easier right ?

There are benchmarks that rightfully show the SOTA behind average human performance in other aspects of reasoning so why are you fumbling so much to demonstrate this with unaided iterative computation ? It's your biggest argument so I just thought you'd have something more substantial than "It's limited bro!"

You cannot even demonstrate this today nevermind some hypothetical scaled up model.

I think Sam will be just fine.

> so why are you fumbling so much to demonstrate this with unaided iterative computation

Well, you see, I've been a professional developer for the last 45 years, and often, gasp, think for long periods of time before coding, or even writing things down. "Look ma, no hands!".

I know this will come across as an excuse, but the thing is I assumed you were also vaguely famililar with things like software development, or other cases where human's think before acting, so I evidentially did a poor job of convincing you of this.

I also assumed (my bad!) that you would at least know some people who were semi-intelligent and wouldn't be hopelessly confused about farmers and chickens, but now I realize that was a mistake.

Really, it's all on me.

I know that "just add more rules", "make it bigger" didn't work for CYC, but maybe as you suggest "increase N" is all that's needed in the case of LLMs, because they are special. Really - that's genius! I should have thought of it myself.

I'm sure Sam is OK, but he'd still appreciate you letting him know he can forget about Q* and Strawberries and all that nonsense, and just "increase N"! So much simpler and cheaper rather than hiring thousands of developers to try to figure this out!

Maybe drop Yan LeCun a note too - tell him that the Turing Award committee are asshats, and that he is too, and that LLMs will get us all the way to AGI.

> Google "evolutionary spandrels" and you will learn there can be body features (large brains of whales) that are simply a byproduct of other evolutionary pressures rather than direct adaptation.

If you're a 10-150 ton whale, a 2-10 kg brain isn't a significant cost.

But if you're a 50kg primate, a brain of more than 1kg IS.

For humanoids over the past 10 million years, there are very active evolutionary pressures to minimize brain size. Still, the brain grew to maybe 2-4 times the size over this period.

This growth came at a huge cost, and the benefits must have justified those costs.

> On evolutionary timeframes what biology allows can evolve and the hard limits are due to chemistry and physics.

It's not about there being hard limits. Brain size or muscle size or density is about tradeoffs. Most large apes are 2-4 times stronger than humans, even when accounting for size, but human physiology has other advantages that make up for that.

For instance, our lower density muscles allow us to float/swim in water with relative ease.

Also, lighter bodies (relative to size) make us (in our natural form) extremaly capable long distance runners. Some humans can chase a horse on foot until it dies from exhaustion.

I'm sure a lot of other species could have developed human level intelligence if the evolutionary pressures had been there for them. It just happens to be that it was humans that first entered an ecological niche where evolving this level of intelligence was worth the costs.