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We can’t trust AI systems built on deep learning alone

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Re: We can’t trust AI systems built on deep learning alone

#111

Post by chillacy »

jkp56 wrote: post #81
XuMiao wrote: post #32

One key question is whether symbolic AI is the right model of the world. It underperforms vector based AI on many specific tasks. But human experts heavily reply on it to communicate with each other. If symbolic AI is not the right model, P vs NP problem might be just irrelevant. Human philosophy is full of craps. We will lose a lot of beliefs. Elon will be right, we will abandon human languages, and connect through…

Of course symbolic AI is the way to go. We communicate with text messages that consist of words that we internally convert to word2vec style vectors to detect similar words. One more thing we do in our heads: we build a graph of those word2vec symbols. When I read in a book "a cat is sleeping on a tree" I instantly build a small graph where nodes Cat and Tree are connected with an edge labeled Sleeps. I may visualize…

We perceive ourselves building a knowledge graph but at the physical level how does that happen? Does this graph materialize physically as neuron connections? Or is it just an abstraction the brain makes, a part of the subjective experience of thinking?

It's certainly true that we think in symbols but they exist somewhere in the mushy goo of neurons, could symbolic thinking emerge from large ANNs in the same way?

Re: We can’t trust AI systems built on deep learning alone

#112

Post by m0zg »

newen wrote: post #109
m0zg wrote: post #82

Earlier quoted context omitted.

I'm pretty sure this isn't how chess masters actually play chess. That'd be too slow and error prone. They pattern match very heavily in the beginning and towards the end. And all those patterns would be wrong on a larger board with more chess pieces. At least for chess.

No, huge majority of those patterns are going to transfer over to the 10x10 board.

How? Remember, there'd have to be more pieces, and the new pieces could have different moves. The game would also be dramatically more difficult, combinatorially.

Re: We can’t trust AI systems built on deep learning alone

#114

Post by Isinlor »

mindgam3 wrote: post #58

> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board. This right here is the soft underbelly of the entire “machine learning as step towards AGI” hype machine, fueled in no small part by DeepMind and its flashy but misleading demos. Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.…

Try learning to ride bike with inverted steering, try to navigate world with your vision flipped over or use your non-dominant hand to do things that you normally do. Well, try to write on Azerty keyboard if you are Qwerty native (really, fk Azerty :P).

Humans are also not a general intelligence.

In certain sense Deep Reinforcement Learning is actually more general than human intelligence. For example, when playing games you can remove certain visual clues. It makes it almost impossible to play for humans, while Deep RL scores will not even budge. It means that Deep RL is more general, because it does not relay on certain priors, but it also makes it more stupid in narrow domain of human expertise. Try this game to see yourself: https://high-level-4.herokuapp.com/experiment

Here is bike with reverse steering: https://www.youtube.com/watch?v=MFzDaBzBlL0 Here is flipped vision experiment: https://www.youtube.com/watch?v=MHMvEMy7B9k

Human brains are amazing, but they also require certain amount of time to retrain when inputs/outputs are fundamentally changed.

PS. I didn't hear about anyone testing different board sizes with AlphaZero-esque computer players. But I saw Leela Zero beating very strong humans, when rules of the game were modified so that that the human player could play 2 additional moves: https://www.youtube.com/watch?v=UFOyzU506pY

Re: We can’t trust AI systems built on deep learning alone

#115

Post by jkp56 »

chillacy wrote: post #111
jkp56 wrote: post #81

Earlier quoted context omitted.

Of course symbolic AI is the way to go. We communicate with text messages that consist of words that we internally convert to word2vec style vectors to detect similar words. One more thing we do in our heads: we build a graph of those word2vec symbols. When I read in a book "a cat is sleeping on a tree" I instantly build a small graph where nodes Cat and Tree are connected with an edge labeled Sleeps. I may visualize…

We perceive ourselves building a knowledge graph but at the physical level how does that happen? Does this graph materialize physically as neuron connections? Or is it just an abstraction the brain makes, a part of the subjective experience of thinking? It's certainly true that we think in symbols but they exist somewhere in the mushy goo of neurons, could symbolic thinking emerge from large ANNs in the same way?

This graph is a high level abstraction, of course. How exactly neurons store information is interesting, but hardly relevant here. My guess is that one symbol is stored in a very sparse subset of neurons and each neuron acts a bit like a node in a DHT. All together these neurons implement a fast DHT where a word2vec graph node acts as a key. On top of that this "wet DHT" can quickly find keys nearby, i.e. in can instantly return all neighbors of word2vec("apple").

I think ANNs implement only the word2vec function that translates images or sounds into symbols and vice versa.

Re: We can’t trust AI systems built on deep learning alone

#116

Post by YeGoblynQueenne »

csande17 wrote: post #69
mindgam3 wrote: post #63

Earlier quoted context omitted.

Almost all of the human strategies and concepts would still apply: center control, square control, development, initiative, king safety, the opposition, etc. The only exceptions would be fringe concepts like opening theory (already moot in Chess960) and endgame edge cases. If you’re still not convinced, I’ll prove that skills transfer by playing bullet against anyone who can make a 10x10 variant playable online. [Edi…

> make a 10x10 variant playable online I'm not saying I'm definitely going to do this, but is there a rulebook somewhere for 10x10 chess? (What are the initial piece positions, and how would castling work?)

There are variants with fewer pieces like Hexapawn:

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

Re: We can’t trust AI systems built on deep learning alone

#117

Post by YeGoblynQueenne »

jayd16 wrote: post #60
mindgam3 wrote: post #58

> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board. This right here is the soft underbelly of the entire “machine learning as step towards AGI” hype machine, fueled in no small part by DeepMind and its flashy but misleading demos. Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.…

>Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining. Interesting. Has this actually been shown? I would assume a lot of the strategies a human is familiar with would fall apart as well. I'm no chess or go player but I would have to learn new strategies in a tic-tac-toe game scaled to 10x10. I would certainly not be as proficient although I…

Why stick to chess? Magic: the Gathering is a game that is played with cards with printed rules text that describes how a card should be played. The set of cards is constantly updated with a few hundred new cards introduced at least twice a year (although games are often played with only a subset of all cards).

Despite the constant change of the card pool, and also the wording of the rules text on the cards, and the rules themselves, human players are perfectly capable of "picking up a card they've never seen before and playing it" correctly.

https://en.wikipedia.org/wiki/Magic:_The_Gathering

Re: We can’t trust AI systems built on deep learning alone

#118

Post by mark_l_watson »

turk73 wrote: post #36
mark_l_watson wrote: post #5

I started reading Rebooting AI last night. I think that Marcus and Davis (so far in the book) take a reasonable approach by wanting to design robust AI. Robust AI requires general real world intelligence that is not provided by deep learning. I have earned over 90% of my income over the last five or six years as a deep learning practitioner. I am a fan of DL based on great results for perception tasks as well as soli…

Making 90% of your income off of this tech over the last n years is different than that tech being successful. I work at a very large company that is trying to use ML and AI in all kinds of places. The trend I am seeing is that most of that effort is falling flat, really flat, in fact. They have success in places where regular algorithms would also succeed, but just having regular developers design matching systems a…

Thanks for your response. Sure, the level of hype is rather high for DL.

That said, I also lived through and worked through the level of hype around expert systems. I think the high level of hype around expert systems in the 1980s was much more extreme and unwarranted that the DL hype levels. I base this on selling expert system tools for both Xerox Lisp Machines and for the Macintosh when it was released in 1984. Some of my customers did cool and useful things, but nothing earth shaking.

At least DL provides very strong engineering results for some types of problems.

Re: We can’t trust AI systems built on deep learning alone

#119

Post by YeGoblynQueenne »

cjauvin wrote: post #6
cs702 wrote: post #2

The opposing argument, by Rich Sutton, Distinguished Research Scientist at DeepMind: http://www.incompleteideas.net/IncIdeas/BitterLesson.html

I would say this is also an opposing argument: https://twitter.com/ylecun/status/1066568396177842176 i.e. gradient-based learning is the final word on the matter.

Yes, gradient optimisation always gets the final word. But what does that word, well, say?

                                     _
                                    / \
            ,----------------------------. 
            | Hey! I found your minimum! |
            '------------    ------------'                                    /
                 .----.  \  /   /         \                                  /
                /      \  \/   /           \                                /
               /        \  @  /             \                              /
              /          `'--'               \                            /
             /                                \                          /
            /                                  \                        /
           /                                    \                      /
          /                                      \   .-.              /
         /                                        \_/   `\           /
   \.__.'                                                 \         /
                                                           \       /
                                                            `;._.-'

Re: We can’t trust AI systems built on deep learning alone

#120

Post by jmnicolas »

Isinlor wrote: post #114
mindgam3 wrote: post #58

> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board. This right here is the soft underbelly of the entire “machine learning as step towards AGI” hype machine, fueled in no small part by DeepMind and its flashy but misleading demos. Once a human learns chess, you can give it a 10x10 board and she will perform at nearly the same skill level with zero retraining.…

Try learning to ride bike with inverted steering, try to navigate world with your vision flipped over or use your non-dominant hand to do things that you normally do. Well, try to write on Azerty keyboard if you are Qwerty native (really, f k Azerty :P). Humans are also not a general intelligence. In certain sense Deep Reinforcement Learning is actually more general than human intelligence. For example, when playing…

Fk QWERTY. (written from an AZERTY keyboard :p )
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