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Interesting. I have heard the opposite of this.

Supervised learning may be how it looks from the outside, but consider that out of the >6,570,0000 waking seconds of a child's life up to age 5, there maybe only a few dozen instances of supervised adult instruction per day. Besides those, what do neurons do the remaining 99.99% of the time?

Part of the problem might be that comparing supervised and unsupervised learning 'effectiveness' is a bit apples-and-oranges. Their effect together is highly collaborative. Children have to develop abstractions on their own before you can supervise them on those abstractions. It is probably fair to say that a key part of human general intelligence is creating high-level representations of low-level stimuli. It might also be fair to say that this is what the brain is doing 100% of the time.

So if I may hand wave a little: while supervised learning can make a child better maximize objectives on those high-level representations (objectives they may be aware of through unsupervised observation), for the most part it does not fundamentally change the structure of those things in the child's brain. This makes unsupervised learning almost all of the cake to me.

My post has the caveat that children undergo a lot of other objective-based learning besides explicit instruction from adults, and all of this maps only fuzzily to supervised vs unsupervised learning in AI, which is the issue from the submitted post.

> there maybe only a few dozen instances of supervised adult instruction per day

There might be only a few dozen instances but I think each instance has a lasting effect which makes up for this.

If you scold a child for something stupid it did then it will remember this for a long-ish time. Same for teaching him things or correcting stuff.

I guess you show the child some correct behaviour at a few instances and this is then used internally as a guideline for selflearning.

That is a great point. Talking about supervised vs unsupervised vs reinforcement learning is most straightforward with tasks like language learning, audio processing, image processing, and playing discrete games. It is possible to see broad similarities between deep learning approaches and human cognition for several of these tasks. But when you start getting into tasks like the formation of narrative identity, things get very complicated.

Maybe one major difference between playing a game and forming a personality is that these early important interactions don't just adjust wirings in the cerebral cortex, the part of the brain most responsible for general intelligence. It goes straight to our emotional memory bank in the limbic system, which is all about learning an incredibly important objective function: to survive. But very high level features formed by unsupervised learning can do this, not just reptilian predator detection routines. Being scolded or corrected can have a powerful effect on future motivation. Suffice to say, artificial intelligences don't currently worry about this.

> Supervised learning may be how it looks from the outside, but consider that out of the >6,570,0000 waking seconds of a child's life up to age 5, there maybe only a few dozen instances of supervised adult instruction per day. Besides those, what do neurons do the remaining 99.99% of the time?

This seems like a really facile analysis. For example, if I read a child a storybook, I'm deliberately providing several signals every second. That's a "single instance" but I've effectively provided a lot of training information. At least enough to keep a child's mind busy for 3600 seconds.

I understand what you're saying and I agree with you. Human learning is based almost entirely around feedback and trial and error. But by comparison, machine learning requires a lot of implicitly labelled data, and that's what I think Yann LeCunn was talking about.

For example, to train a classifier to identify birds you need a large number of pictures of birds, maybe millions of varied examples. And then you'd need an equivalent number of images of things that aren't birds, or things that look similar to birds. Humans are able to make that same classification with a very small number of examples, maybe even n = 2. If someone saw a bird for the first time and then another one shortly after, they would be able to put the two together immediately and make a lot of inferences on top of that. Machine learning algorithms aren't even close to that yet.

Supervised learning sucks because it requires vast quantities of labelled and prepared data, which is expensive. Top ML researchers must feel like they're sitting in a Formula One racer but can't afford any gas for it. AlphaGo shows that a computer can do almost anything, but only with a significant investment of resources for each specific task. Unsupervised algorithms tend to be less effective than supervised methods right now, but once that changes it will open up a new world.

As a parent, I have quite an opposite experience to your claims.

> Parents spend lots of time in do and don't and giving specific instructions on everything from how to use toilet to how to construct a correct sentence.

What you're missing is that the child is initially a blank page. He has no knowledge of language either, so giving instructions to somebody that doesn't understand your language is challenging, to say the least. And acquiring language is something they do just by listening and observing others, in a very cool game of trial and error. At some point a child starts mimicking what the parent does, repeating words or gestures and then notices the triggered response.

They learn best by observing what you do and not by what you say. They also learn by discomfort. I taught my boy to use the chamber pot, not by language, but by letting him without diapers and letting him pee on himself, until he got the hint that he should use the chamber pot :-)

And of course, you might classify this as "supervised learning", but these are just shortcuts. Because of our ability to communicate in speech and writing, we learn from the acquired knowledge of our ancestors. Isolate a couple of toddlers from the world and you'll eventually see that they'll invent their own language and they'll learn by themselves to not shit were they eat or sleep.

If child gets external reward every waking moment until she is 12 years old, it's just 4.2 million feedback signals. It's not enough to learn complex behavior.

Reinforcement learning works for fine motor control and other tasks where the feedback loop is tight and immediate. Reinforcement and conditioning can also modulate high level cognition and behavior, but it's not the secret sauce of learning.

This sign language spontaneously developed mostly without supervising:

https://en.wikipedia.org/wiki/Nicaraguan_Sign_Language

Err. A human is capable of unsupervised learning. Try locking two kids in a room for over decade. Maybe she didn't develop speech and social skills because there was no one to speak to or socialize with.

It's been done: https://en.wikipedia.org/wiki/Language_deprivation_experimen...