[dibstern]

While I appreciate the sentiment, I thi k the fact that we can learn from fewer examples demonstrates that the learning process isn’t as efficient as ours, therefore it isn’t yet optimal. It seems like a goal should be for learning to be as efficient or more efficient for computers than for humans.

[mockingbirdy]

We got 86 billion neurons in our brains that all crave to get used - so you can even imagine them as single agents trying to get along.

It's like 86 billion guys that try to please that thing they simultaneously produce (our consciousness). What I want to say: The algorithm can be dumb as f*. I call it the f-star-algorithm. But the computational power in our brains is extremely high.

[dibstern]

I don’t think throwing more computational power at the problem is the right answer to all ML problems.

[mockingbirdy]

Those neurons are arranged and incentivized cleverly. The structure is also very important and is necessary for the resulting intelligence.

So it's not only computational power, but also the unique structure nature found through trial and error.

[monetus]

I wonder how much of that cleverness will be gleaned and appropriated by those who design 3 dimensional chips.