AI needs to see thousands or millions of images of a cat before they reliably can identify one. The fact that a child needs to only see one example of a cat to know what a cat is from then on seems to point to humans having something very different.
Humans train on continuous video. Even our most expensive models are, in terms of training set size, far behind what an infant processes in the first year of their life.
EDIT: and it takes human children a couple years to reliably identify a cat. My 2.5 y.o. daughter still confuses cats with small dogs, despite living under one roof with a cat.
I contend that you could show any child old enough to communicate in basic English a photograph (so not live continuous video) of some obscure animal they've never seen before (say an Okapi) and they'd be able to easily identify another Okapi when seeing one at a zoo.
My daughter is 5 y.o., which means because of kindergarten, I spend plenty of time about kids this age. A random kid this age would absolutely fail your test. They may remember the word after one exposure, but I doubt they'll remember any of the distinctive features.
Hell, many adults would fail it. I'm not sure if I could pass such test - in my experience, you remember the important details only after first experiencing a test and realizing what exactly it is that would be useful in distinguishing the two animals.
It's a massive amount of data. Not just video, but all senses, continuously. Also, I think an important aspect is that the training data is interactive. From day one, an infant sees his own hands moving and begins testing cause and effect. They aren't passively absorbing.
If the model is first pre-trained on unlabeled images, then it takes about 10 labeled images of cats and 10 labeled images of dogs to train a (possibly strong) classifier (example: DINOv2), I doubt humans will do better.
That's a good point; when comparing performance with humans, one has to remember that a human spends years of training on unlabeled images in form of continuous video stream, on top of audio streams and other senses, before they're able to communicate with you so you could test them.
My point is not that humans have a faster training speed but that humans must be doing something fundamentally different from LLMs. You could build Altman's $7 trillion dollar GPU cluster and use the majority of the world's energy to feed it and you'd still hit the same limitations if you're just running an LLM on it, even a very sophisticated LLM. This is Yann LeCun's position as well.
That, as I understand it, is not a valid chain of logic. Requiring fewer data points does not inherently indicate that the underlying mechanism (autogressive sequential generation, not the transformer which is just an architecture) is different.
Not to mention the secondary arguments like - no proof that human learns faster from fewer datapoints, that's just your assumption in the sibling comment. Humans inherit information. The equivalent - fine-tuning a foundation model - is very fast to learn novel objects.
Just because someone has a Turing award doesn't mean they know what they're talking about. They are just people, with strengths and weaknesses like everyone else. But often on the extreme end of strengths and weaknesses.
Well, the same thing goes for you - just because someone posts on HN doesn't mean they know what they're talking about. And if I have to decide whose assessment I trust regarding AI, I take the Turing award winner who worked for almost 40 years on AI over a random guy from the internet.
Sure. But I'm not standing here saying my argument is valid because SolidAsparagus made it.
> And if I have to decide whose assessment I trust regarding AI, I take the Turing award winner who worked for almost 40 years on AI over a random guy from the internet.
I'd encourage you to do your own thinking and make up your own mind.
