[LINK] David Deutsch on why we don't have AGI yet "Creative Blocks"

harshhpareek

Folks here should be familiar with most of these arguments. Putting some interesting quotes below:

http://aeon.co/magazine/being-human/david-deutsch-artificial-intelligence/

"Creative blocks: The very laws of physics imply that artificial intelligence must be possible. What's holding us up?"

Remember the significance attributed to Skynet’s becoming ‘self-aware’? [...] The fact is that present-day software developers could straightforwardly program a computer to have ‘self-awareness’ in the behavioural sense — for example, to pass the ‘mirror test’ of being able to use a mirror to infer facts about itself — if they wanted to. [...] AGIs will indeed be capable of self-awareness — but that is because they will be General

Some hope to learn how we can rig their programming to make [AGIs] constitutionally unable to harm humans (as in Isaac Asimov’s ‘laws of robotics’), or to prevent them from acquiring the theory that the universe should be converted into paper clips (as imagined by Nick Bostrom). None of these are the real problem. It has always been the case that a single exceptionally creative person can be thousands of times as productive — economically, intellectually or whatever — as most people; and that such a person could do enormous harm were he to turn his powers to evil instead of good.[...] The battle between good and evil ideas is as old as our species and will go on regardless of the hardware on which it is running

He also says confusing things about induction being inadequate for creativity which I'm guessing he couldn't support well in this short essay (perhaps he explains better in his books). Not quoting here. His attack on Bayesianism as an explanation for intelligence is valid and interesting, but could be wrong. Given what we know about neural networks, something like this does happen in the brain, and possibly even at a concept level.

The doctrine assumes that minds work by assigning probabilities to their ideas and modifying those probabilities in the light of experience as a way of choosing how to act. This is especially perverse when it comes to an AGI’s values — the moral and aesthetic ideas that inform its choices and intentions — for it allows only a behaviouristic model of them, in which values that are ‘rewarded’ by ‘experience’ are ‘reinforced’ and come to dominate behaviour while those that are ‘punished’ by ‘experience’ are extinguished. As I argued above, that behaviourist, input-output model is appropriate for most computer programming other than AGI, but hopeless for AGI.

His final conclusions are disagreeable. He somehow concludes that the principal bottleneck in AGI research is a philosophical one.

In his last paragraph, he makes the following controversial statement:

For yet another consequence of understanding that the target ability is qualitatively different is that, since humans have it and apes do not, the information for how to achieve it must be encoded in the relatively tiny number of differences between the DNA of humans and that of chimpanzees.

This would be false if, for example, the mother controls gene expression while a foetus develops and helps shape the brain. We should be able to answer this question definitively once we can grow human babies completely in vitro. Another problem would be the impact of the cultural environment. A way to answer this question would be to see if our Stone Age ancestors would be classified as AGIs under a reasonable definition

Folks here should be familiar with most of these arguments. Putting some interesting quotes below:

http://aeon.co/magazine/being-human/david-deutsch-artificial-intelligence/

"Creative blocks: The very laws of physics imply that artificial intelligence must be possible. What's holding us up?"

Remember the significance attributed to Skynet’s becoming ‘self-aware’? [...] The fact is that present-day software developers could straightforwardly program a computer to have ‘self-awareness’ in the behavioural sense — for example, to pass the ‘mirror test’ of being able to use a mirror to infer facts about itself — if they wanted to. [...] AGIs will indeed be capable of self-awareness — but that is because they will be General

Some hope to learn how we can rig their programming to make [AGIs] constitutionally unable to harm humans (as in Isaac Asimov’s ‘laws of robotics’), or to prevent them from acquiring the theory that the universe should be converted into paper clips (as imagined by Nick Bostrom). None of these are the real problem. It has always been the case that a single exceptionally creative person can be thousands of times as productive — economically, intellectually or whatever — as most people; and that such a person could do enormous harm were he to turn his powers to evil instead of good.[...] The battle between good and evil ideas is as old as our species and will go on regardless of the hardware on which it is running

The doctrine assumes that minds work by assigning probabilities to their ideas and modifying those probabilities in the light of experience as a way of choosing how to act. This is especially perverse when it comes to an AGI’s values — the moral and aesthetic ideas that inform its choices and intentions — for it allows only a behaviouristic model of them, in which values that are ‘rewarded’ by ‘experience’ are ‘reinforced’ and come to dominate behaviour while those that are ‘punished’ by ‘experience’ are extinguished. As I argued above, that behaviourist, input-output model is appropriate for most computer programming other than AGI, but hopeless for AGI.

His final conclusions are disagreeable. He somehow concludes that the principal bottleneck in AGI research is a philosophical one.

In his last paragraph, he makes the following controversial statement:

For yet another consequence of understanding that the target ability is qualitatively different is that, since humans have it and apes do not, the information for how to achieve it must be encoded in the relatively tiny number of differences between the DNA of humans and that of chimpanzees.

I don't follow. You can write a program to generate random hypotheses, and you can write a program to figure out the implications of those hypotheses and whether they fit in with current experimental data, and if they do, to come up with tests of those ideas for future experiments. Now, just generating hypotheses completely randomly may not be a very efficient way, but it would work. That's very different from saying "It's impossible". It's just a question of figuring out how to make it efficient. So what's the problem here?

I think his claim is basically "we don't know yet how to teach a machine how to identify reasonable hypotheses in a short amount of time," where the "short amount of time" is implicit. The proposal "let's just test every possible program, and see which ones explain Dark Matter" is not a workable approach, even if it seems to describe the class that contains actual workable approaches. (Imagine actually going to a conference and proposing a go-bot that considers every possible sequence of moves possible from the current board position, and then picks the tree most favorable to it.)

But the Turing test is very different from coming up with an explanation of dark matter. The Turing test is a very specific test of use of language and common sense, which is only defined in relation to human beings (and thus needs human beings to test) whereas an explanation of dark matter does not need human beings to test. Thus making this particular argument moot.

I think the Turing test is being used as an illustrative example here. It seems unlikely that you could have a genetic algorithm operate on a population of code and end up with a program that passes the Turing test, because at each step the genetic algorithm (as an optimization procedure) needs to have some sense of what is more or less likely to pass the test. It similarly seems unlikely that you could have a genetic algorithm operate on a population of physics explanations and end up with an explanation that successfully explains Dark Matter, because at each step the genetic algorithm needs to have some sense of what is more or less likely to explain Dark Matter.

I think his claim is that a correct inference procedure will point right at the correct answer, but as I disagree with that point I am reluctant to ascribe it to him. I think it likely that a correct inference procedure involves checking out vast numbers of explanations, and discarding most of them very early on. But optimization over explanations instead of over plans is in its infancy, and I think he's right that AGI will be distant so long as that remains the case.

What else could it possibly be?

My interpretation of that section is that Deutsch is claiming that "induction" is not a complete explanation. If you say "well, the sun rose every day for as long as I can remember, and I suspect it will do so today," then you get surprised by things like "well, the year starts with 19 every day for as long as I can remember, and I suspect it will do so today." If you say "the sun rises because the Earth rotates around its axis, the sun emits light because of nuclear fusion, and I think the sun has enough fuel to continue shining, angular momentum is conserved, and the laws of physics do not vary with time," then your expectation that the sun will rise is very likely to be concordant with reality, and you are very unlikely to make that sort of mistake with the date. But how do you gets beliefs of that sort to begin with? You use science, which is a bit more complicated than induction.

Similarly, the claim that prediction is unimportant seems to be that the target of an epistemology should be at least one level higher than the output predictions- you don't want "the probability the sun will rise tomorrow" but "conservation of angular momentum" because the second makes you more knowledgeable and more powerful.

"It seems unlikely that you could have a genetic algorithm operate on a population of code and end up with a program that passes the Turing test"

Well, we have one case of it working, and that wasn't even with the process being designed with the "pass the Turing test" specifically as a goal.

"because at each step the genetic algorithm (as an optimization procedure) needs to have some sense of what is more or less likely to pass the test."

Having an automated process for determining with certainty that something passes the Turing ... (read more)

3passive_fist12y

My impression was that he was saying that creativity is some mysterious thing that we don't know how to implement. But we do. Creativity is just search. Search that is possibly guided by experience solving similar problems. By learning from past experiences, search becomes more efficient. This idea is quite consistent with studies on how the human brain works. Beginner chess players rely more on 'thinking' (i.e. considering a large variety of moves, most of which are terrible), but grandmasters seem to rely more on their memory. [...] As I said, though, it's quite different, because a hypothetical explanation for dark matter needs to only be consistent with existing experimental data. It's true that it's unfeasible to do this for the Turing test, because you need to test millions of candidate programs against humans, and this cannot be done inside the computer unless you already have AGI. But checking proposals for dark matter against existing data can be done entirely inside the computer. [...] I agree with you. [...] If the machine's only inputs were '1990, 1991, 1992, ... , 1999', and it had no knowledge of math, arithmetic, language, or what years represent, then how on Earth can it possibly make any inference other than the next date will also start with 19? There is no other inference it could make. On the other hand, if it had access to the sequence '1900, 1901, 1902, ... , 1999' then it becomes a different story. It can infer that 1 always follows 0, 2 always follows 1, etc., and 0 always follows 9. It could also infer that when 0 follows 9, the next digit is incremented. Thus it can conclude that after 1999, the date 2000 is plausible, and add it to its list of highly-plausible hypotheses. Another hypothesis could be that the 3rd digit is never affected, and that the next date after 1999 is 1900. Equivalently, if it had already been told about math, it would know how number sequences work, and could say with high confidence that the next year will b

4

[LINK] David Deutsch on why we don't have AGI yet "Creative Blocks"

4

4

4

[LINK] David Deutsch on why we don't have AGI yet "Creative Blocks"

4

4