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He's right about LeCun. Yann really does not like Deep Learning being criticized.

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.

You can say that it's a equation with a lot of terms, but that isn't really showing your working out.

You need a symbolic system to show that working out.

This isn't to say that deep learning is worthless, just not worth the amount of column inches it's getting at the moment.

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A trained neural network is a model, just like any other kind of model. If there is any overall property of the data it learned, you can coerce it from the network just like you can read them from any mathematical model. (What is, for some properties, very easily, and for others it's very hard.)

An AI can always explain how it came to an answer. But the explanation can be like "there are way too many factors" or "things are like this just because of those other things, if those changed, the explanation would too". But those are not an attribute of the neural networks, but of the problem they are solving. If you get an explanation like that for a problem that has simple explanations, it means you failed at training your network.

> 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 mathematical proofs. )

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.

Deep learning systems, are in essence a very long equation that hides all meaning over the entire network - good luck in finding out why it came to a decision. You might get lucky and see a definitive triggering path through the network, but that's an exception to the rule, as far as I understand. (I'm willing to be proven wrong here.)

Symbolic systems, can trivially be made to trace the code execution paths they made for any action they might take.

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

I don’t think we’re able to show our workings for higher level things like proofs.

For instance, I can’t tell you (accurately) how I composed the proof conceptually, ie what strategy it takes, or even determined the appropriate steps, ie how I determined to use a particular tactic.

I can tell you the premises lead to the conclusion via a chain of reasoning, but that’s the artifact of my thought process, not anything about how it works. The output of proving is that chain of steps.

So I think here is somewhere DL systems may have an advantage: they actually can freely introspect their thought process as part of their thought process.

> they actually can freely introspect their thought process as part of their thought process.

Do you mean that they can in principle? I don't know existing systems, which can inspect their own weights and output a vector of confidence scores that they can recognize classes A, B, C, ...

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.

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."

"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 based on symbolic manipulation at progressively higher levels, i.e. clustering pieces into "chunks", connecting these chunks into higher level patterns like weak pawn structure or forks, and ultimately deciding on the best course of action by weighing all of the different high level patterns. Every step is rule-based logic which I can readily explain to anyone, even a chess novice. The part that appears "magic" is the ability to do all of these calculations in the blink of an eye. But that too is simply due to having trained so many of these patterns extensively at a young age. Anyone who can speak a language is doing the same thing, manipulating complex symbolic objects in real time at progressively higher levels (i.e. letters, words, sentences, paragraphs).

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. But you don’t have access to the network doing the evaluation and can’t describe exactly how it operates, just that it was trained on chess theory.

It’s that fuzzy reasoning to speed up the process of actually finding solutions that I was calling out as the source of the unknowns in our processing — and at least from my exposure to board games (and their players), it’s often the source of things like innovative moves.

"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.

I don't think this refutes the argument. You've just explained that the process by which you play chess is by calculation. In essence, you're doing something you could program a machine to do (without any learning component).

The question is: how do you explain how you have an original idea, or learn something you previously had no conceptualisation of.

I think chess is a poor example at a professional level, because players have learnt mainly from analysis of other's techniques, rather than mostly making their own inferences. How a novice plays chess, inventing as they go, because they cannot draw on extensive experience, would be a better analogy, in my opinion.

"how do you explain how you have an original idea, or learn something you previously had no conceptualisation of"

This is like "where does your chess intuition come from" - and what I'm trying to express is that my chess intuition isn't actually a black box. There is a set of heuristics which I follow in order to come up with these "original ideas". Some of these I learned from watching other great players. Others I learned through trial and error, by playing tons of games, just like AlphaZero. The difference between me and AlphaZero is I can describe these heuristics conceptually in a way that anyone can understand, without resorting to "it's because this move tended to work in a large portion of the million games I played".

> 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?) :)

ooh interesting links -thanks!

I'll read them later.

Famous-ish :)

We probably have similar views on brexit.

Humans can almost always give an explanation judgement but, for intuitive judgements at least, the reasons we give are frequently wrong or incomplete. People seldom notice that we rate essayists with more symmetric faces as better at writing but we have this bias and tons more besides.

But I don't think that there's any reason to doubt that when using abstract reasoning, Kahneman's system 2, our explanations of our thought processes can't be accurate. It's system 1 that's opaque to accurate introspection.

Aren't we - as a group - better in this? I'm thinking about the scientific method, and the common differentiation there between the context of discovery and the context of justification.

So you are free to discover a mathematical theorem by intuition or during daydreaming (like the molecular structure of benzene) - anything goes.

But in the context of justification, you have to be much more disciplined - even if you trust your own intuitions and don't need to persuade yourself (of course generally you should not trust your intuition in scientific context that much;), you have to persuade others who are missing your insight.

edit: And of course - in the end - to accept something as a true A.I. system, it probably has to persuade us that its intuitions are right, using the type justifications we accept. (So even if it has better types of justification - if we won't get it, they has to dumb it down to our level of scientific understanding)

We are verifying the results of our opaque brain processes. We cannot yet verify that our explanations of those processes are correct.

Given some results regarding brain damage we might just be rationalizing afterwards separate from the problem solving. Severed corpus callosum I believe it was.

Or that effect might just be how a brain keeps functioning after losing some pieces - better off evolutionarily have data integrity issues than shut down if a checksum fails.

I believe that ML can technically give you an answer but it ammounts to a summation of its training set and how it parses the data. Which isn't too readable to even mathematicians and leaves it vulnerable to stuff like adding a small pixel cloud to cause it to see a toy turtle as an assault rifle. Our errors certainly exist for visual processing but they tend to be more distorion than that.

Most people can’t explain why they make most of their decisions, really. It’s really only a very small subset of our activity that we are capable of explaining logically.

What’s the chain of reasoning behind me falling in love and marrying my wife? What’s the chain of reasoning behind why I watch the great British bake off compulsively? What’s the chain of reasoning that leads an alcoholic to drink time and time again. What’s the chain of reasoning behind why I look at a rainbow and see bands of distinct colors? What’s the chain of reasoning behind why I paused and scratched my nose just now?

> 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.

A mouse can't do that either. Yet we are unable to mimic mouse intelligence with any of our current tools.

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