[AndrewKemendo]

Most comments here are to the tune of "Well DL is just a bunch of correlations and statistics, it's not really understanding anything"

Ok, well I can also say "humans are just a bunch of chemical reactions and electrical signals."

The beauty of DL is in it's simplicity and really we're at the very starting point of seeing it work with extremely sparse networks (compared to biological intelligence). The fact that it works so well with such limited data in narrow domains should be energizing.

[candiodari]

I really do want to ask the author that question, given that he focuses so much on the "weird" idea that everything turns out to be just numbers moving around over time.

"What do you suppose the input to the human brain looks like ?"

Since I have kids I have come to realize that the same thing you see in neural nets you see in human beings. Understanding exists, but it is mostly not how human beings respond to the world around them. Mostly we are a minimally generalized dictionary, we know a long long very damn long list of "tricks". If A happens, B will follow. There's very little along the lines of "objects fall along a parabolic trajectory".

This leads to generalization errors, and the surprising thing is you see those in humans ! Kids having learned to open one type of door do not know how to deal with an (even very slightly) jammed doorknob, they don't recognize differently shaped doorknobs as doorknobs, etc. First few days they don't even realize that if pushing won't work, pulling might. So the understanding of opening doors really does start out on the level of "move the free end of the small cylindrical object in the middle of the door that's parallel to the floor down, and then push", and if any of those conditions fails, well, door's going to stay shut.

And this is exactly the very hard problem you encounter with neural nets : finding the right balance between specificity and generalization. But one saving grace is that if you specialize in enough special cases, you can get around without having a general understanding, and that's exactly what's happening with kids.

[AndrewKemendo]

Yes, my advice to any aspiring AI researcher is to have a kid and approach them similar to how Piaget did

[OtterCoder]

Enervating? I find it the opposite. It's exciting and energizing to think of what we can do with this.

[AndrewKemendo]

Gah thanks for the catch, gotta love autocorrect.

[carapace]

Babbage is said to have owned a dancing automaton he called "The Silver Lady" that was delightfully lifelike in its movements. I wouldn't say that such a device "understood" dance, no matter how perfectly it moved.

[kthejoker2]

Given today's technology and sufficient time, you could devise an AI that could watch dance videos, "understand" dance, and create its own Silver Lady.

[AndrewKemendo]

You're arguing a strawman. I never claimed that understanding was based on a phenomenological evaluation of an output. Rather, reductionism is not an argument against complexity.

[carapace]

I don't understand. (No joke intended. I'd like to reply to you but I don't understand what you're saying.)

[AndrewKemendo]

You write about a robot that, from a phenomenology perspective, appeared to embody the understanding of dance required because it was moving in a certain graceful way of dancing. Then you imply that this system was certainly unaware of the complexity of dance, irrespective of how graceful it could be. That is, it didn't "understand" dance in a more abstract way.

I would not argue otherwise, nor was this line of reasoning in question.

So you aren't arguing against my point, that reducing the argument of understanding to: "Well X is just [a, b, c]" is a bad argument. Instead you argue against the unstated claim that "X systems that look like they embody Y actually have an understanding of Y," in the sense that a human would "understand" Y. That is a strawman and not what the something I am claiming.

[carapace]

Okay, I get that I was arguing past you, not to your point.

> reducing the argument of understanding to: "Well X is just [a, b, c]" is a bad argument

I think when you say, "humans are just a bunch of chemical reactions and electrical signals" you're stating a hypothesis, not a fact. But we know DL et. al. is just mathematical machinery.

It may turn out that consciousness is somehow the result of mechanics (I do not believe it, but that's beside the point) or it may turn out that what we are, the "thing" that understands, is somehow beyond mechanical systems. I feel like I should clarify that when I say "understanding" I mean more than that there is some mechanism that can perform a complex mapping from inputs to outputs (example: chess playing AI doesn't "understand" chess in the sense I mean.) There is some "self" that understands, and this is directly tied to conscious subjective awareness.

In one of your other comments on the same article you say:

> As to the question of consciousness, it is yet to be well defined, with no possibility to test (because of eg Qualia) so by definition you'd never verify or not. At most you'd recognize what you perceive as consciousness based on how you perceive other entities which you believe have it.

Consciousness cannot be defined, as you say, because it has no qualities. And it cannot be scientifically studied for the same reason. However, there is a method to "detect" it in other systems, to wit: merging. Two or more conscious systems can voluntarily merge, creating a new conscious entity partaking of but greater than its members. This isn't widely discussed or even known in AI and consciousness debates, so I wanted to mention it.

[AndrewKemendo]

However, there is a method to "detect" it in other systems, to wit: merging. Two or more conscious systems can voluntarily merge, creating a new conscious entity partaking of but greater than its members. This isn't widely discussed or even known in AI and consciousness debates, so I wanted to mention it.

Sounds like woo-woo. Have any research on this?