[virgildotcodes]

> You missed the core of my point: humans operate, including in the real world, on much less training data.

I very specifically addressed this in my response to you. How much training data is contained in 16 waking hours of navigating the world fusing all sensory data, never mind data being simultaneously generated within the mind while this is all going on, from birth til death? From birth til pushing that shopping cart?

Far, far more than in all the training datasets being used for AI.

I also addressed this again in my reply to the sibling comment.

People tend to discount how much data humans have passing through their minds 24/7.

A human isn’t born in a vacuum as a fully formed adult and dropped into the shopping cart navigation problem.

A human has had far, far more training data fed into it that contains all the pieces necessary to translate to pushing a shopping cart when first seeing it, than a machine learning model which has been fed 1 million videos of a robot pushing a shopping cart.

[staticman2]

I know I saw Geoffrey Hinton say humans operate with much less training data in a talk.

It doesn't strike me as a claim that should be controversial.

As far as I know nobody can train A.I. to push a shopping cart based on a human child's training set. It's mostly not relevant to the task.

[virgildotcodes]

Yeah I'm not sure what the exact context of the statement is.

I am absolutely certain that we have not already discovered let alone implemented the best possible learning algorithms. Humans have had more time to evolve, there's a great chance that we do learn more efficiently, and have developed specialized brains that are primed to learning things like how to navigate the physical world on planet Earth as bipeds.

That said, to say that we operate with less training data is just ignoring the reality of all the data we're training on at all times.

If we were to model in lossless fidelity what humans are capable of seeing, hearing, smelling, tasting, feeling, thinking consciously and subconsciously etc. essentially all the data flowing through our minds that we are constantly training on every moment of every day, even while we sleep/are unconscious, what sort of bitrate do you think would be required?

Modern LLMs train on datasets in the what, tens of terabytes in size? Let's call it 100 TB.

I would imagine that to losslessly reproduce the full suite of human sensory data (whatever that means for things like taste, touch, smell) would require a bitrate that hits that 100 TB total relatively quickly?

[necovek]

Let's stick to comparing language skills to language skills: at least in my experience with my two kids, they learn word formation patterns before they turn 2 — easy to notice because you see them make mistakes on exceptions.

LLMs needed how much training data to be able to do so?

FWIW, I still see them make up wrong words not following any grammatical pattern, esp in Serbian with less training data.

Serbian is pretty complex though: https://www.languagegrowth.com/en/blog/serbian-grammar-basic... — this made it even more surprising to see the kids pick them up so early when their vocabulary is probably not 2000 words yet.

[staticman2]

Hinton says things like

"...we're optimized for having not many experiences. You only live for about a billion seconds—that's assuming you don't learn anything after you're 30, which is pretty much true. So you live for about a billion seconds and you've got a 100 trillion connections. So [you've] got crazily more parameters than you have experiences. So our brains [are] optimized for making the best use of not very many experiences."

[necovek]

A billion seconds is around 34 years, so I'd say we live for two billion seconds.

But that's a good way to look at it: in 2B seconds, how many experiences can we get?

[KalMann]

I think this is disingenuous comparison. When we read a book we can estimate the amount of data we're taking in based on the character count (each character being represented by some fixed amount of bits).

What you're suggesting on the other hand is something akin to counting the number of pixels on each page we look at. That's absurd overestimate of the amount of data a person reading is actually taking in.

[necovek]

I believe there is a point: we simulataneously ingest words, but also glyph shapes and learn acceptable variations between them (eg. serif vs non-serif, large x-height vs small, curlier or more elegant, playful letters...) — all of these contribute to our multi-faceted learning, but ultimately, we do seem to need less of the data to learn (how long it takes for us to learn to recognize letters vs OCR based on ML).