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Why Robot Brains Need Symbols

nautil.us

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Re: Why Robot Brains Need Symbols

#61

Post by mindgam3 »

FakeComments wrote: post #57
mindgam3 wrote: post #50

Earlier quoted context omitted.

"I don’t think we’re able to show our workings for higher level things like proofs." I can't speak for proofs, but this is false in the case of higher level things like chess. I'm not a grandmaster, but I was a chess master at age 10 and I am ranked ~2400 in bullet chess. To the average person my ability to play chess is "magic". But to me it's not magic at all. I can explain my thought process at any time. It's all…

There’s chess theory, which is rule based and what you start off describing. But then you admit you don’t work directly with chess theory when selecting moves, there’s a trained black box evaluator that selects candidate moves, which you then select from via chess theory. That’s how you’re finding chess moves in the blink of an eye: you run a fuzzy approximation, then refine the results using higher level reasoning.…

"there’s a trained black box evaluator that selects candidate moves, which you then select from via chess theory"

You're missing my point. There is no "trained black box evaluator." There is indeed a trained evaluator, but it is not a black box. It is fully understandable. If I gave you private chess lessons, I could teach you my heuristics. And eventually you would understand them enough to be able to teach them to others. This would not be possible if it were truly a black box.

Re: Why Robot Brains Need Symbols

#62

Post by p1esk »

red75prime wrote: post #49
p1esk wrote: post #45

Earlier quoted context omitted.

output a vector of confidence scores that they can recognize classes A, B, C, ... This is literally what neural networks do when classifying patterns.

They classify patterns, not their own ability to classify patterns.

Again, they literally produce confidence scores - probabilities that each prediction is correct.

For example, say there are 3 classes, and the network is shown two different examples of class 2. Say it outputs a class probability distribution {0.02, 0.45, 0.43} for the first example, and {0.02, 0.9, 0.08} for the second. Even though in both cases it correctly identifies class 2, it's a lot more confident in its prediction in the second case.

Re: Why Robot Brains Need Symbols

#63

Post by buboard »

Esai wrote: post #4

AI development has hit a plateau in terms of performance and funding saturation. All investment is now expected to deliver some semblance of market performance before the great Margin Call of 2019 hits, which means scaling out AI to absurd domains and deploying vast armies of surplus humans to either code or emulate profitable-enough behavior to pass the risk on to the next investor round. In the end, the Mechanical…

What is the evidence for a plateau? The corporate temperaments are irrelevant to the actual progress of the field. If anything , ai systems have not even started being deployed , and the field is even missing a proper theoretical framework. this should indicate that it's still very early stage

Re: Why Robot Brains Need Symbols

#64

Post by [deleted] »

mindgam3 wrote: post #61
FakeComments wrote: post #57

Earlier quoted context omitted.

There’s chess theory, which is rule based and what you start off describing. But then you admit you don’t work directly with chess theory when selecting moves, there’s a trained black box evaluator that selects candidate moves, which you then select from via chess theory. That’s how you’re finding chess moves in the blink of an eye: you run a fuzzy approximation, then refine the results using higher level reasoning.…

"there’s a trained black box evaluator that selects candidate moves, which you then select from via chess theory" You're missing my point. There is no "trained black box evaluator." There is indeed a trained evaluator, but it is not a black box. It is fully understandable. If I gave you private chess lessons, I could teach you my heuristics. And eventually you would understand them enough to be able to teach them to…

[deleted]

Re: Why Robot Brains Need Symbols

#65

Post by giardini »

polkapolka wrote: post #55
giardini wrote: post #47

Earlier quoted context omitted.

polkapolka says> "Machine programming simply does not scale....Just try to solve the spam problem using symbolic AI. It will keep you busy (and paid) for a long time, while yielding subpar results." 1. We can scale "machine programming" by making computers faster and more complex, 2. The spam problem can be solved with Bayesian methods (which I consider to be part of "machine programming"): a connectionist solution i…

Faster and more complex computers does not make you faster at manual programming. Computer vision had this before the DL boom: engineers painfully crafting feature extractors. It went nowhere. Bayesian models underperform to DL by a wide margin (though it is a step up from handwritten rules: if DEAR FRIEND then Spam score++.

polkapolka says >"Faster and more complex computers does not make you faster at manual programming. Computer vision had this before the DL boom: engineers painfully crafting feature extractors. It went nowhere."

Faster and more complex computers make manual programming faster and make software faster, including DL software. Without the faster computers of today we wouldn't be using or even discussing DL.

Re: Why Robot Brains Need Symbols

#66

Post by giardini »

317070 wrote: post #2

No, they don't. Now, feel free to _show_ me that they do, and I will gladly accept to have been wrong. But this argument that because of some very theoretical view on the problem the current engineering solutions should be abandoned, without actually providing good engineering alternatives, is weird. I have a bit of a "not even wrong" feeling on the symbolist side of the debate. Deep neural networks have serious flaw…

If you're willing to accept a (possible) inexplicable intelligence that cannot explain its decisions or reasoning processes then you'd be fine w/o symbolism. In fact there may be an example for you (perhaps in your pocket):

The next time you have a decision to make, phrase it as a Heads/Tails question and flip a coin. Over its time of use, the coin will be right until it is wrong, putting you in exactly the same situation as you would be with an inexplicable intelligence.

So you see, intelligence is not that far away! But explicable predictive intelligence requires reasoned prediction and can answer "Why that prediction?" when asked and can learn if a prediction fails. The coin cannot but will be adequate, at least until it fails sufficiently to induce you to abandon it.

Re: Why Robot Brains Need Symbols

#67

Post by red75prime »

p1esk wrote: post #62
red75prime wrote: post #49

Earlier quoted context omitted.

They classify patterns, not their own ability to classify patterns.

Again, they literally produce confidence scores - probabilities that each prediction is correct. For example, say there are 3 classes, and the network is shown two different examples of class 2. Say it outputs a class probability distribution {0.02, 0.45, 0.43} for the first example, and {0.02, 0.9, 0.08} for the second. Even though in both cases it correctly identifies class 2, it's a lot more confident in its predi…

I just say that there's no introspection in existing networks. There's no part, which can take the weights of a convolutional part of a network and images of a certain class, and then output confidence score of the convolutional part classification ability for that class.

I don't say that it is possible or useful. I said that I don't know of any deep learning systems, which "[...] can freely introspect their thought process as part of their thought process."

Re: Why Robot Brains Need Symbols

#68

Post by d--b »

YeGoblynQueenne wrote: post #48
d--b wrote: post #43

My case against hardcoding some kind of symbolic logic within the architecture of an AI model is that there won't be a way to challenge the symbols as the brain does. When I think "the house is red", I know what it means very well, but I'm also able to doubt or modulate my understanding of the symbols. These conversations would be hard to put in symbols: - This house is red - No! It's crimson! - But crimson is red! O…

>> My case against hardcoding some kind of symbolic logic within the architecture of an AI model is that there won't be a way to challenge the symbols as the brain does. Who said anything about hardcoding anything? Marcus is advocating for the use of gradient descent to learn symbols- he even cites the DeepMind paper on θILP, a differentiable Inductive Logic Programming system that learns symbolic rules with deep lea…

From the article

> Their solution? A hybrid model that vastly outperformed what a purely deep net would have done, incorporating both back-propagation and (continuous versions) of the primitives of symbol manipulation, including both explicit variables and operations over variables

This is what I called "hardcoding some kind of symbolic logic". Is it not?

Re: Why Robot Brains Need Symbols

#69

Post by ben_w »

opless wrote: post #14
ben_w wrote: post #11

Earlier quoted context omitted.

> Please someone who knows much more than me about deep learning tell me how a deep learning ai can explain how it came to an answer. > It can't. By definition. What definition are you using? From the my understanding, if you can explain your reasoning, then a deep learning system can in principle learn how to explain its reasoning. If it couldn’t, you couldn’t either.

We're not deep learning systems. So reasoning by analogy there isn't correct. (Aside: There have been experiments that show that many explanation for [everyday] actions are, in fact made up after the decision to execute an action. Which gives some insight to the whole nature of free will - but that's another discussion entirely. However, indeed we're able to show our workings for higher level workings, like mathemati…

> We're not deep learning systems. So reasoning by analogy there isn't correct.

That’s why I’m asking which definition you’re using. It’s broad enough that some models are biologically plausible even though there is criticism that many others are not: https://arxiv.org/abs/1502.04156

> Deep learning systems, are examples of supervised learning. Once trained, they cannot adapt to new input. You have to retrain to accommodate new features that you want to capture.

Supervised, unsupervised, and reinforcement; and there is work on both incremental learning (https://arxiv.org/abs/1712.02719) and avoiding catastrophic forgetting (https://arxiv.org/abs/1812.02464)

> (also - hi Ben! I recognise your blog!)

Oh no, I’m becoming famous. (Infamous?) :)

Re: Why Robot Brains Need Symbols

#70

Post by opless »

ben_w wrote: post #69
opless wrote: post #14

Earlier quoted context omitted.

We're not deep learning systems. So reasoning by analogy there isn't correct. (Aside: There have been experiments that show that many explanation for [everyday] actions are, in fact made up after the decision to execute an action. Which gives some insight to the whole nature of free will - but that's another discussion entirely. However, indeed we're able to show our workings for higher level workings, like mathemati…

> We're not deep learning systems. So reasoning by analogy there isn't correct. That’s why I’m asking which definition you’re using. It’s broad enough that some models are biologically plausible even though there is criticism that many others are not: https://arxiv.org/abs/1502.04156 > Deep learning systems, are examples of supervised learning. Once trained, they cannot adapt to new input. You have to retrain to acco…

ooh interesting links -thanks!

I'll read them later.

Famous-ish :)

We probably have similar views on brexit.

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