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Comment by chriswarbo | Hacker News Reader

I'm a firm believer that it's a mistake to ask machines to do X in the hope that they'll do Y. We can't train an ANN to classify images then ask it to explain its reasoning; that's not the task we trained it for!

How might we train an ANN to explain its reasoning? One approach would be to learn programs: have the ANN write programs which classify images. Then we have a classifier (run the program) and an explanation of how it works (read the program). We don't have an explanation of how the ANN chose the program, but again, we didn't train it to tell us. In principle, we could keep adding meta-layers; in practice, the search space and evaluation time would explode :)

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that's not the task we trained it for!

Yes, exactly. We're not training consciousness networks. That's not even a goal of all this research. We're training dumb high-speed classifiers.

How might we train an ANN to explain its reasoning?

There are networks that exist today that'll happily explain an entire scene to you (the whole "this picture contains a pizza sitting on an oven in a kitchen and there is a dog in the corner"), which is closer, but still just pairwise training. The "thought" process isn't recurrent or turing complete so it can't make progress on its own.

> There are networks that exist today that'll happily explain an entire scene to you (the whole "this picture contains a pizza sitting on an oven in a kitchen and there is a dog in the corner"), which is closer, but still just pairwise training. The "thought" process isn't recurrent or turing complete so it can't make progress on its own.

Aren't those generated by RNNs, which are Turing complete?

> There are networks that exist today that'll happily explain an entire scene to you (the whole "this picture contains a pizza sitting on an oven in a kitchen and there is a dog in the corner")

I was thinking more along the lines of: "This picture contains pizza. This is because there is an ellipse which appears to be covered with cheese and pepperoni. I say that because the dominant colour is yellow and there are elliptical patches of a more reddish colour. ..." and so on, down to the pixel level.

We can kind of get this by running the networks backwards, but it's not really deductive reasoning; for example, we can't correct the network by saying "that yellow ellipse covered with red ellipses is actually a pile of vomit", and watch that knowledge propagate through the weights. Instead, we have to re-train with more examples of pizzas and vomit.

Even humans can’t explain their own reasoning. We can only rationalize our intuitive decisions after the fact, but it’s pure hokum – all decisions are made intuitively using no rational thought whatsoever, only afterward can we make up rationalizations for it.

It follows that it should be perfectly possible for an A.I. to do this, too.

(Edit: I now realize that this rationalization process was theorized by Douglas Adams in Dirk Gently’s Holistic Detective Agency, as described by Wikipedia:

“The story also mocks the corporate world with [the software] package called Reason, which inverts the idea of a decision-making program. Instead of proceeding from ideas and logic to a decision, it takes a decision that has already been made and creates a reasoned justification for it.”

— https://en.wikipedia.org/wiki/Dirk_Gently%27s_Holistic_Detec... )

Let's say you want as much money as you can get. This is probably not rational. Now let's say you have a choice of being given $5 or $10, which do you choose? Why was that not a rational choice? Because it's predicated on an irrational desire?

No, that is not what I meant – the choice itself is not made rationally, even though a theoretical rational actor might have made the same choice.

The most we humans can do it to make an intuitive choice, allow the post-hoc rationalization to occur and then check its rationality by analyzing its logic. If the logic is bad, we go back and try another intuitive choice.

By your own logic, you just made an intuitive decision about whether I have a point, and now you're just rationalizing it. Not much point in trying to have a rational conversation.

> By your own logic, you just made an intuitive decision about whether I have a point, and now you're just rationalizing it.

Yes, this is true. At least, it is what I believe to be happening.

> Not much point in trying to have a rational conversation.

What? Why not? We seem to be able to converse quite easily.

Is your belief falsifiable? Wouldn't the experimenter just be rationalizing their intuitive decision about the meaning of their observations regardless?

It's trivial to brute-force an answer to your question: if I take $5 I'll get $5, if I take $10 I'll get $10. I prefer $10 to $5, so I take the $10.

Intelligence is needed when there are too many possibilities to brute-force. We need to make guesses about which actions may give better results; we have to spot patterns, generalisations and simplifications to allow re-use of previous experience; we need to identify the crucial aspects, in order to narrow down the possibilities; we need to allocate resources efficiently, and know when to give up; and so on.

These are the things we would like machines to do well, but we have no satisfactory theory to explain, measure or compare such things. Introspecting our own thought patterns is not an effective way to reverse-engineer these proceses in humans, as the parent says.

The biggest problem I see to training a network to program is that it would require a lot of example programs to serve as training data. Which means we need a lot of really good, bug free programs that already accomplish similar tasks. And in my opinion, we do not have enough reliable seed programs to make anything useful, at least currently.

It is very easy to take a picture of a car and tell a computer what it is, not so easy to write a program and explain to a computer what it does.

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