[Jensson]

> It’s the same as with people. The more variations people see of something, the more likely they intuit the underlying pattern.

> The fewer examples, the more likely they just pattern match.

A kid who uses a calculator and just fills in the answer to every question will see a lot more examples than a kid that learned by starting from simple concepts and understanding each step. But the kid who focused on learning concepts and saw way fewer problems will obviously have a better understanding here.

So no, you are clearly wrong here, humans doesn't learn that way at all. These models learn that way, you are right on that, but humans don't.

[Nevermark]

I have no idea where your calculator came from.

In neither case did I introduce one.

And since the calculator itself has already a general understanding, it would seem completely counter productive to start training a computer or child by first giving them a machine that has already solved the problem.

Also, for what it’s worth, I am speaking from many years experience not just training models but creating the algorithms that train them.

[Jensson]

Replace "uses calculator" to "looks through solved problems", same thing. Not sure what you don't understand. Humans don't build understanding by seeing a lot of solved examples.

To make a human understand we need to explain how things work to them. You don't just show examples. A human who is just shown a lot of examples wont understand much at all, even if he tries to replicate them.

> Also, for what it’s worth, I am speaking from many years experience not just training models but creating the algorithms that train them.

What does this has to do with how humans learn?

[Nevermark]

Humans learn vast amounts of information from examples.

They learn their first words, how to walk, what a cat looks like from many perspectives, how to parse a visual scene, how to parse the spoken word, interpret facial expressions and body language, how different objects move, how different creatures behave, different materials feel, what things cause pain, what things taste like and how they make them feel, how to get what they want, how to climb, how not to fall, all by trial & example. On and on.

And yes, as we get older we get better and better at learning 2nd hand from others verbally, and when people have the time to show us something, or with tools other people already invented.

Like how a post-trained model picks up on something when we explain it via a prompt.

But that is not the kind of training being done by models at this stage. And yet they are learning concepts (pre-prompt) that, as you point out, you & I had to have explained to us.

[Jensson]

> Like how a model picks up on when we explain something to it after it has been trained.

Models don't learn by you telling them something, the model doesn't update itself. A human updates their model when you explain how something works to them, that is the main way we teach humans. Models don't update themselves when we explain how something works to them, that isn't how we train these models, so the model isn't learning its just evaluating. It would be great if we could train models that way, but we can't.

> Humans learn vast amounts of information from examples.

Yes, but to understand things in school those examples comes with an explanation of what happens. That explanation is critical.

For example, a human can learn to perform legal chess moves in minutes. You tell them the rules each piece has to follow and then they will make legal moves in almost every case. You don't do it by showing them millions of chess boards and moves, all you have to do is explain the rules and the human then knows how to play chess. We can't teach AI models that way, this makes human learning and machine learning fundamentally different still.

And you can see how teaching rules creates a more robust understanding than just showing millions of examples.

[Nevermark]

> you explain how something works to them, that is the main way we teach humans

I am curious who taught you to recognize sounds, before you understood language, or how to interpret visual phenomena, before you were capable of following someone’s directions.

Or recognize words independent of accent, speed, pitch, or cadence. Or even what a word was.

Humans start out learning to interpret vast amounts of sensory information, and predictions of results of there physical motor movements, from a constant stream of examples.

Over time they learn the ability to absorb information indirectly from others too.

This is no different from models, except that it turns out, they can learn more things, at a higher degree of abstraction, just from example than us.

And work on their indirect learning (I.e. long term retention of information we give them via prompts), is just beginning.

But even as adults, our primary learning mode is experience is from the example situations we encounter non-stop as we navigate life.

Even when people explain things, we generalize a great deal of nuance and related implications beyond what is said.

“Show, don’t tell”, isn’t common advice for no reason. We were born example generalizers.

Then we learn to incorporate indirect information.

[Jensson]

You are right, but I think it is really important to have this difference in learning in mind, because not being able to learn rules during training is the main weakness in these models currently. Understanding that weakness and how that makes their reasoning different from humans is key both to using these models and for any work on improving them.

For example, you shouldn't expect it to be able to make valid chess moves reliably, that requires reading and understanding rules which it can't do during training. It can get some understanding during evaluation, but we really want to be able to encode that understanding into the model itself rather than have to keep it in eval time.