[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 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.

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.

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.

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 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 agree with you.

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 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 be 2000. Yes, going to school counts as 'past experiences'.

It's is a common mistake that people make when talking about induction. They think induction is simply just 'X has always happened, therefore it will always happen'. But induction is far more complicated than that! That's why it took so long to come up with a mathematical theory of induction (Solomonoff induction). Solomonoff induction considers all possible hypotheses - some of them possibly extremely complex - and weighs them according to how simple they are and if they fit the observed data. That is the very definition of science. Solomonoff induction could accurately predict the progression of dates, and could do 'science'. People have implemented time-limited versions of Solomonoff induction on a computer and they work as expected. We do need to come up with faster and more efficient ways of doing this, though. I agree with that.

I agree that there's a lot more work to be done in AI. We need to find better learning and search algorithms. What I disagree with is that the work must be this kind of philosophical work that Deutsch is proposing. I think the work that needs to be done is very much engineering work.

Creativity is just search.

Correct, but not helpful; when you say "just search," that's like saying "but Dark Matter is just physics." The physicists don't have a good explanation of Dark Matter yet, and the search people don't have a good implementation of creativity (on the level of concepts) yet.

I agree that there's a lot more work to be done in AI. We need to find better learning and search algorithms. What I disagree with is that the work must be this kind of philosophical work that Deutsch is proposing. I think the work that n

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4Lumifer12y

That doesn't look helpful to me. Yes, you can define creativity this way but the price you pay is that your search space becomes impossibly huge and high-dimensional. Defining sculpture as a search for a pleasing arrangement of atoms isn't very useful.