[wvoch235]

I would wager that it will eventually be able to do math and logic, and may already be able to with the right prompting. It can follow instructions quite well, and multi step problems can be solved by breaking the problem into small digestible steps, and having it write out each step, just the same as humans do. If it can't do this yet, it's likely just that it doesn't have enough parameters yet. But I don't see why this couldn't be achieved with fine tuning.

But ultimately I think it's just too computationally heavy to do math, remember obscure facts, or track rapidly changing data, with an LLM, and it's far more efficient to pass it off to a piece of specialized software, just the same as humans do.

[thomastjeffery]

An infinite amount of "fine tuning" would solve any problem, yes...

...but what we just described isn't a language processor anymore: it's just a database. There is no need to use a neural network if you already have a known response to every possible input!

The entire point of ChatGPT in the first place is to generate a response that doesn't exist in the training data. If all we needed was to look up the answer we wrote down earlier, we already have SQL.

What we are talking about here is being able to provide a logically correct response without just pulling it out of a lookup table. Either you can do that, or you can't, and ChatGPT can't.

[wvoch235]

I get what you're saying, and it's a database if you're talking about facts, but breaking down mathematics and being able to perform logic isn't a database in the true sense, it's still able to generate novel responses based on a set of rules.

My point here is even if it only "gets" the semantics, it has the ability to perform logic. It's just not very efficient. And, I'd say this isn't that far off from what is happening in our brains.

Do we really "get" logic, or do we rely on heuristics? Why do we know that 12 * 2 is equal to 24? Either because we remember our multiplication tables (look up from a table) or because we break it into smaller steps until we're left with pieces of the problem that we inherently know (including 12 * 2 means 12 + 12 which means take 12 and increment the number +1 for 12x times, or 10 + 10 = 20 and 2 + 2 = 4 so 12 + 12 = 24).

I don't see why that couldn't reasonably scaled up to advanced calculus.

I think the point that you're hitting on is that LLMs aren't "full brains" that have all of the components that human brains have, and that's true. But LLMs appear to essentially have the ability to simulate (or replicate depending on how far you want to go) executive function. As others have hit on, from that, you can either command other specialized components to be able to do specific tasks (like recall facts, perform logic, etc) or you can have it break down the problem into steps. Then take the output from that processing and form a coherent response.

The structure here is flexible enough that I'm struggling to find the upper limit, and if all research in the development of LLMs stopped tomorrow, and everyone focused on building with LLMs, I still don't think that limit will be found.

[anthonypasq]

if by "do" math and logic, you mean spit out what seems most reasonable then yes.

ChatGPT doesn't "know" anything, its just a prediction engine. if you ask it what 2+2 is, it will tell you 4, but it didnt actually do math. it only said 4 because everyone else said it.

there is no generalizable reasoning going on there.

[wvoch235]

See https://news.ycombinator.com/item?id=34340079, I would venture to say that this isn't any different to the reason why you know that 2+2 is 4.