[abetusk]

Intelligence is the amalgamation of many smaller problems working together and building on top of each other.

* Facial recognition/detection

* Facial synthesis (deepfakes)

* Speech synthesis, including mimickry

* Speech recognition

* Natural language processing

* Gait/walking algorithms

* Motion planning

* etc.

Complexity arises from simple units working together in parallel. We're working on the smaller, specialized problems that will, in the next generation, be put together to build more complex and complete systems.

I'm no fan of the 'black box' nature of neural networks but it's clear they're getting results. As they become more accessible to the lay person, we'll see a profusion of use cases that are both anticipated and surprising.

I'm always flabbergasted by the doom prediction. The path we're on seems apparent.

[cjhanks]

I agree with the notion that artificial intelligence is a graph of smaller problems, as is human perception.

The problem is a question of informational density. Biological systems are computationally very dense. Far more dense than the 4nm transistor fabrication available today, and with a far larger volume of size.

Consequentially, the computational capability of most AI systems is far lower than its biological equivalent. And as you find in most information finite discretization problems - the lower density information system will alias against the higher information system.

So, that means you will have a hierarchy/pipeline of computational stages - each aliasing reality. Eventually, you will find that your parameterization of each perceptual stage has a strange property. The size of each subsequent layer is important... but the relative computational space of each subsequent stage is even more important. Because mismatched stages results in nothing but numerical interference and noise.

And I think that is where we are today. The IQ of a krill shrimp.

[natmaka]

Isn't "the amalgamation of many smaller problems working together and building on top of each other" a fair description of the theorical Unix system?

Aren't your criteria for "intelligence" human-centric, implying that there is no other form of "intelligence"?

Aren't your criteria of the "black box" type, given that AFAIK no human can really completely explain how he recognizes faces/does NLP/walks/...?

[abetusk]

> Isn't "the amalgamation of many smaller problems working together and building on top of each other" a fair description of the theorical Unix system?

Yes. Note the success of Unix and the ability to scale, do work and provide an environment to be productive in.

> Aren't your criteria for "intelligence" human-centric, implying that there is no other form of "intelligence"?

Your use of 'human-centric' is odd. I would have thought the traditional 'human-centric' theory of the mind is something monolithic and indivisible. Suggesting that it's many small processes communicating with each other is basically taken straight out of nature, from ants, schools of fish, birds flocking, etc.

Whether there are other forms of intelligence or no, it's clear that incremental progress in individual processes that can then be composed together is a productive way to traverse the energy landscape. This is why (imo) we see so many symbiotic relationships from cells on up to higher level animals.

> Aren't your criteria of the "black box" type, given that AFAIK no human can really completely explain how he recognizes faces/does NLP/walks/...?

I'm not quite sure what your point is here. If you're critiquing me about neural networks being black boxes and not giving us real insight into the underlying system, that's fair and the reason why I said I didn't like the black box aspect of neural networks. I will say that if there is a black box model that can be easily manipulated, this will probably lead to deeper models much quicker.

If you're saying that human cognition is not describable by any human and, I guess, implying that it's indescribable, I would point out that one doesn't follow from the other. Not having a good model right now doesn't imply we won't understand it at some future date and, in my opinion, this is precisely what's happening. Having no human be able to describe the underlying computation (of face recognition, nlp, walking etc.) doesn't mean it's indescribable, it means it's not describable by anyone right now.

At one point we didn't know how birds flew. We still might not know, to your satisfaction, but we have a basic understanding of how to make things fly, both in practical and theoretical terms. Planes fly and we understand how even though they don't flap their wings. I have no doubt we'll figure out how to do complex human-level computation even if we don't have a deep model of the specifics of human thought.

[natmaka]

>> Aren't your criteria for "intelligence" human-centric, implying that there is no other form of "intelligence"?

> Your use of 'human-centric' is odd. I would have thought the traditional 'human-centric' theory of the mind is something monolithic and indivisible.

Sorry, my answer wasn't clear. It was not tied to the "small processes communicating..." approach but to your very list of "problems" (facial recognition/detection, facial synthesis (deepfakes), speech synthesis, speech recognition..) which seems to me expressed in a way tied to human activities, while at least part if the underlying "intelligence" underlying many of them may also exists in other forms of life (other mammals, birds, fishes...).

> Suggesting that it's many small processes communicating with each other is basically taken straight out of nature, from ants, schools of fish, birds flocking, etc.

Exactly. My point is that analyzing the ways "the smaller, specialized problems" are tackled by non-human living beings seems pertinent as self-analysis (as humans analyzing human intelligence) is difficult, and as various species may apply various solutions, some more easy to grok. Focusing on "problems" too specific to the human being may be a sort of "framing" detrimental to the quest. Moreover the famous Dijkstra quote ("The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.") may be pertinent.

> incremental progress in individual processes that can then be composed together is a productive way to traverse the energy landscape. This is why (imo) we see so many symbiotic relationships from cells on up to higher level animals.

I agree. My point is about _how_ we consider the system(s) (our "point of view"): framing it to human characteristics, globally or locally (dualism)... It seems to me that the very organization of a system may be neglected when we consider it a stack of "small processes communicating...". Pirsig's "Metaphysics of Quality" may be pertinent.

>> Aren't your criteria of the "black box" type, given that AFAIK no human can really completely explain how he recognizes faces/does NLP/walks/...?

> I'm not quite sure what your point is here. If you're critiquing me about neural networks being black boxes and not giving us real insight into the underlying system, that's fair and the reason why I said I didn't like the black box aspect of neural networks.

This was my point and I agree with you.

> if there is a black box model that can be easily manipulated, this will probably lead to deeper models much quicker.

I'm less optimistic, as it is only 'probable', and AFAIK won't give us more real insight into the underlying system.

> Having no human be able to describe the underlying computation (of face recognition, nlp, walking etc.) doesn't mean it's indescribable, it means it's not describable by anyone right now. > I have no doubt we'll figure out how to do complex human-level computation even if we don't have a deep model of the specifics of human thought.

I agree, we will enhance ways to "approximate" (tricks leading us to a solution to each "local" problem) up to the point of being able to solve real-world problems. However it may reach some hard limit (as far as I understand this is the point of the article), and using a powerful tool/method insufficiently understood may be dangerous.