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

technologyreview.com

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

#121

Post by rytill »

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…

Perfect explanation, down to earth and could be ELI5. Humans are so specialized.

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

#122

Post by whytaka »

thelazydogsback wrote: post #20

I've been waiting for the Symbolic/NN pendulum to starting swinging back the other way and start settling in the center. NN/DL is great for the interface between the outer world and the inner world of the mind (pattern recognition and re-construction), and symbolic AI more straightforwardly represents more "language of the mind" tasks, and easily handles issues like explanation and other meta-behaviors that with DL i…

I've looked around but can't seem to find a good online primer on Symbolic AI. Any help?

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

#123

Post by kchamplewski »

mindgam3 wrote: post #85
kchamplewski wrote: post #80

Earlier quoted context omitted.

I think it's quite important to look at the distinction between the actual agent in play and the learning algorithm used. The learning algorithm AlphaGo uses is somewhat general, and can handle different games (e.g. you can put chess or Go through the algorithm and it functions well for either). The output of this algorithm, however, is a specialised agent. The agent is not general. If I create a chess agent and give…

The distinction you’re making between agent and algorithm is meaningless for the point I was trying to make, which is that the only connection between this DeepMind research (agent, algorithm, whatever) and AGI have in common is the word “general”. Their “general learning” tech doesn’t even generalize to barely modified variants of the original games it has claimed to master. I call bullshit.

> Their “general learning” tech doesn’t even generalize to barely modified variants of the original games it has claimed to master. I call bullshit.

But the point I was making is precisely that the "general learning" tech is in fact somewhat general. AlphaGo and certainly AlphaZero's learning tech generalises to Go, chess, and a few other games. That's relatively general in the domain of board games, in my humble opinion.

The reason this isn't close to AGI is because it's not the agent doing the learning, and so while a relatively general learning algorithm produces the agent, the agent itself is not general even in the field of board games.

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

#124

Post by inimino »

jayd16 wrote: post #74
inimino wrote: post #68

Earlier quoted context omitted.

It is trivially proved that deep learning can represent any computable function so the proof you're asking for is not going to be possible. However, it's also completely obvious to anyone familiar with deep learning as it exists today that it is not moving in the direction of AGI and none of the research is ever going to lead to AGI short of some kind of miracle. The burden of proof here would be on those who think t…

I'm no expert but it doesn't seem very far fetched to me. If we can use deep learning to create a digital assistant, computer vision, and navigation/traversal then we're not very far off from something surpassing a dog's level of intelligence even with what exists today. If that's possible then it seems plausible it could continue onward. Not saying its obviously possible but it doesn't seem obviously impossible and…

Those systems seem close to a dog's intelligence only because you haven't worked on them and don't know how they work. Nothing about this field is obvious. If you are not an expert, just look at the history. People thought at the dawn of this field over 60 years ago (yes, really) that machine intelligence was right around the corner. The optimists have been wrong for more than six decades and they are wrong now. If you are an expert you can look at speech recognition, computer vision, and all these things you mention, and compare them to the intelligence and awareness of a dog, and realize that they are not close, not in the ballpark, not in the same league, not even the same sport. The ANN/DL research is not moving in the direction of AGI and nobody in industry and almost nobody in academia is bothered by that because ML is getting results and careers are being made, while chasing AGI means spending your career on something that almost certainly will not show any results. If anything, the current success of deep learning means we are further away from AGI than we would have been (which is probably a good thing) because there are fewer people working on it.

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

#125

Post by mattmcknight »

mlthoughts2018 wrote: post #19
keithyjohnson wrote: post #7

Understanding a sentence is fundamentally different from recognizing an object. But people are trying to use deep learning to do both. I agree with most of the article but I think this^^ skips over the different types of networks used to solve perception and language problems. A CNN is very different from say, word2vec, which isn't a very deep network at all.

I’d go further and say that deep networks are excellent for sentence understanding, and various types of RNN or 1D convolutional layers are very good at this in specialized domains just as CNNs and ResNets are good in specialized vision applications. It absolutely makes sense to use deep learning for both of these tasks. In fact, one very effective thing to do is to use a Siamese network to learn joint representation…

or... "vision and language sure seem different, can you believe that networks of neurons in the brain do both?"

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

#126

Post by Mathnerd314 »

inimino wrote: post #66
Mathnerd314 wrote: post #57

> General AI also ought to be able to work just as comfortably reasoning about politics as reasoning about medicine. It’s the analogue of what people have; any reasonably bright person can do many, many different things. The average human has extreme difficulty reasoning about politics, while usually being reasonable on medicine (anti-vax being one of many exceptions). And it seems strange to expect a skilled pianist…

> some magic AI that will do everything perfectly Total straw man. He actually uses an intern as an example in the very next sentence after what you quoted, as you would expect them to be able to read and get up to speed on a new area regardless of what it was. Meanwhile SOTA in NLP is a system that can be built to answer a single kind of question but can't explain why it did so or do anything useful if given an expl…

There are deep models like BERT that do pre-training and then need minimal training to do multiple tasks such as question answering, entailment, sentiment analysis, etc. I don't know about "explaining" an answer but there are debuggers that find errors in data sets: https://arxiv.org/pdf/1603.07292.pdf.

But as I said, I don't see why an artist would suddenly get up to speed as a construction worker. He seems to overestimate the capacity of interns as well.

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

#127

Post by mindgam3 »

kchamplewski wrote: post #123
mindgam3 wrote: post #85

Earlier quoted context omitted.

The distinction you’re making between agent and algorithm is meaningless for the point I was trying to make, which is that the only connection between this DeepMind research (agent, algorithm, whatever) and AGI have in common is the word “general”. Their “general learning” tech doesn’t even generalize to barely modified variants of the original games it has claimed to master. I call bullshit.

> Their “general learning” tech doesn’t even generalize to barely modified variants of the original games it has claimed to master. I call bullshit. But the point I was making is precisely that the "general learning" tech is in fact somewhat general. AlphaGo and certainly AlphaZero's learning tech generalises to Go, chess, and a few other games. That's relatively general in the domain of board games, in my humble opi…

You appear to be completely missing the point of my root comment, which is that AlphaGo’s tech isn’t nearly as general as it’s made out to be, even if you stick to Go.

> AlphaGo can play very well on a 19x19 board but actually has to be retrained to play on a rectangular board.

It doesn’t even generalize to the same game with a different board shape. Whereas a human Go master could easily do so.

DeepMind is essentially hacking the common usage of the word “general” in order so that they can make claims about “general” intelligence. And it’s working!

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

#128

Post by image888 »

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.…

I think AI systems should each have its own talent. I believe that a robot that can detect a fire and a fire only can be assumed to be talented in that. It is complete. Its just that AI scientists are not satisfied with what they have already achieved

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

#129

Post by inimino »

Mathnerd314 wrote: post #126
inimino wrote: post #66

Earlier quoted context omitted.

> some magic AI that will do everything perfectly Total straw man. He actually uses an intern as an example in the very next sentence after what you quoted, as you would expect them to be able to read and get up to speed on a new area regardless of what it was. Meanwhile SOTA in NLP is a system that can be built to answer a single kind of question but can't explain why it did so or do anything useful if given an expl…

There are deep models like BERT that do pre-training and then need minimal training to do multiple tasks such as question answering, entailment, sentiment analysis, etc. I don't know about "explaining" an answer but there are debuggers that find errors in data sets: https://arxiv.org/pdf/1603.07292.pdf . But as I said, I don't see why an artist would suddenly get up to speed as a construction worker. He seems to over…

Deeply familiar with BERT. It lacks the very ability he is describing, to adapt itself, get up to speed, and collect relevant information in a new field, because that's simply not how it works. It can't possibly explain itself because it lacks any mechanism of introspection that could possibly give it that ability. It's an expensive way of gaining a very accurate language model that can be tweaked and get good results on a lot of tasks, but it doesn't understand what it's doing. It can't argue for its position or explain why it thinks whatever it thinks. It's not operating on that level of reasoning, at all.

An artist understands the goals of construction work, and can pick up the skills necessary along the way, because we can understand a goal and have a wide variety of cognitive tools to let us know how we are doing. If you've worked closely with BERT you already know that interns have nothing to worry about, not just from the current crop of tools that includes BERT, but from the entire line of deep learning research, short of a sudden and dramatic shift in direction.

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

#130

Post by [deleted] »

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.…

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