[famouswaffles]

>What is the point of this work? 99% on 100-digit arithmetic means there's a 0% chance anyone will ever use a Transformer as an ALU or anything of the kind. We already know how to hard-code a (literally) infinitely more accurate addition machine.

Nobody's going to be replacing calculators with transformers sure but many are and will be using transformers to solve problems arithmetic is a necessary component of.

>So what is the point of this? Transformers are supposed to be the "sparks of AGI" and they can almost do arithmetic if we try very hard to shove it down their heads? Who cares?

You don't need to shove anything down for transformers to get arithmetic. Just changing how numbers are tokenized works. But that requires an entire retrain so why not explore other techniques?

And what does any of this have to do with AGI ? You know how terrible humans are at arithmetic right ?

[YeGoblynQueenne]

Yes, but humans invented arithmetic. And then we invented computers that are much better than us at arithmetic calculations. That's a pattern we can observe all over the place: we're pretty damn good at inventing rich models of complex environments and processes but we're not very good at calculating the results of such models when that requires a lot of computation.

E.g., take chess. Modelling a game of chess as a game tree and searching the game tree by adversarial search is a human invention. Humans are pretty crap at searching a game tree beyond a handful of ply, but we can program a computer to go dozens of ply deep across thousands of branches, and beat any human.

So the challenge for AI is not to get computers to calculate when we know how the calculation is to be performed. The challenge is to get computers to create their own models. And that's a grand, open challenge that is not even close to be solved, certainly not by LLMs. Yann LeCun and Yoshua Bengio have said similar things.

The linked work doesn't move the needle any closer to that and it just shows progress in calculating arithmetic using a transformer, which we already know how to do in a myriad different ways and much more accurately. Hence my criticism for it.

[famouswaffles]

>Yes, but humans invented arithmetic.

I think most would argue Mathematics is a discipline that is discovered more than invented. That said, this isn't really the point I think.

A few humans invented/discovered arithmetic. Most humans will be born, live and die inventing absolutely nothing, even those with the opportunity and resources to do so.

It doesn't make sense to me that a bar most humans can't reach is the bar for General Intelligence of the Artificial kind. You can't eat your cake and have it.

Don't get me wrong. It's a fine goal to have. Of course we want machines that can invent things and push the frontier of science! It is still however a logical fallacy that an inability to do such would disqualify machines of general intelligence when it does not do so for Humans.

>The challenge is to get computers to create their own models. And that's a grand, open challenge that is not even close to be solved, certainly not by LLMs.

LLMs have fairly complex models of the world made manifest by the data they're trained on.

https://transformer-circuits.pub/2024/scaling-monosemanticit...

Lecun may disagree but some others like Hinton, Ilya and Norvig don't.

[YeGoblynQueenne]

>> Most humans will be born, live and die inventing absolutely nothing, even those with the opportunity and resources to do so.

I don't think that's right at all. I like to visit museums. You really get hit in the face with the unending creativity of the human mind and the variety of all that human hands have crafted over thousands of years across hundreds of cultures. I would go as far as to say that the natural state of the human mind is to create new things all the time. And mathematics itself was not created (invented or discovered) by one person, but by many thousands.

In any case, it doesn't matter if one instance of the class of human minds hasn't invented anything, in the same way that it doesn't matter if one car can't do 80mph. It's indisputable that we have the capacity for some novelty, and generality, in our thinking. Maybe not every member of the species will achieve the same things, but the fact is that the species, as a species, has the ability to come up with never-before seen things: art, maths, tech, bad poetry, you name it.

>> Lecun may disagree but some others like Hinton, Ilya and Norvig don't.

I'm with LeCun and Bengio. There's a fair amount of confusion about what a "model" is in that sense: a theory of the world. There's no reason why LLMs should have that. Maybe a transformer architecture could develop a model of the world- but it would have to be trained on, well, the world, first. Sutskever's bet is that a model can be learned from text generated by entities that already have a world model, i.e. us, but LeCun is right in pointing out that a lot of what we know about the world is never transmitted by text or language.

I can see that in my work: I work with planning, right now, where the standard thing is to create a model in some mathematical logic notation, that is at once as powerful as human language and much more precise, and then let a planning agent make decisions according to that model. It's obvious that despite having rich and powerful notations available there is information about the world that we simply don't know how to encode. That information will not be found in text, either.

Sutskever again seems to think that, that kind of information, can somehow be guessed from the text, but that seems like a very tall order, and Transformers don't look like the right architecture. You need something that can learn hidden (latent) variables. Transformers can't do that.

[famouswaffles]

>In any case, it doesn't matter if one instance of the class of human minds hasn't invented anything, in the same way that it doesn't matter if one car can't do 80mph.

It does matter, depending on what claim you're making. We've not reached the upper bound of transformer ability. Until we clearly do, then it very much does matter.

>I'm with LeCun and Bengio. There's a fair amount of confusion about what a "model" is in that sense: a theory of the world. There's no reason why LLMs should have that.

See this is my problem with Lecun's arguments. He usually starts with the premise that it's not possible and works his way from there. If you disagree with the premise then there's very little left. "Well it shouldn't be possible" is not a convincing argument, especially when we really have very little clue on the nature of intelligence.

>Sutskever's bet is that a model can be learned from text generated by entities that already have a world model, i.e. us, but LeCun is right in pointing out that a lot of what we know about the world is never transmitted by text or language.

A lot of the world is transmitted by things humans don't have access to. Wouldn't birds that can naturally sense electromagnetic waves to intuit direction say humans have no model of the world ? Would they be right ? Nobody is trained on the world. Everything that exists is trained on small slices of it. A lot of the world is transmitted by text and language. And if push comes to shove then text and language is not the only thing you can train a transformer on.

>Sutskever again seems to think that, that kind of information, can somehow be guessed from the text, but that seems like a very tall order,

I don't think this is as tall an order as you believe

>and Transformers don't look like the right architecture. You need something that can learn hidden (latent) variables. Transformers can't do that.

But they do this all the time.

Transformer trained on only protein sequences learns biological structure and function - https://www.pnas.org/doi/full/10.1073/pnas.2016239118

Toy example on binary addition (transformer trained on inputs and outputs of addition sequences) learn an algorithm for it - https://www.alignmentforum.org/posts/N6WM6hs7RQMKDhYjB/a-mec...

Unless i'm misunderstanding what you mean by hidden variables, it's very clear a transformer is regularly learning not just the sequences themselves but what might produce them.

[YeGoblynQueenne]

Sorry, I missed this.

>> Unless i'm misunderstanding what you mean by hidden variables, it's very clear a transformer is regularly learning not just the sequences themselves but what might produce them.

That's what I mean, but I don't think that's happening regulary, or at all. I don't see where the transformer architecture allows for this. Of course we can claim that any model of a process from examples is implicitly modelling the underlying sub-processes, for example we can claim that a multivariate regression that predicts the age at death from demographic data is somehow learning to represent human behaviour, say, but that's one of those big claims that need big evidence.

On the two works you link to, I know the one on mechanistic interpretabiity. As the author says:

Epistemic status: I feel pretty confident that I have fully reverse engineered this network, and have enough different lines of evidence that I am confident in how it works.

But I don't feel that confident at all that the author's confidence should instill confidence in myself. A clear, direct proof is needed, although of course we can discuss what a proof even means and how much it is a social construct etc.

The other paper, I haven't read. I'm going to bet it's basically data leakage which is a pervasive problem with most deep learning work that suffices to invalidate many big claims about big results. I'll have to read the paper a bit more carefully.

But, again, what is in the transformer architecture that can predict hidden variables?