The weakest arguments for and against human level AI

The weakest argument against AI was the standard: Free will (or creativity) hence no AI!

I am most appalled by "philosophical externalism about mental content, therefore no AI." Another silly one is "humans can be produced for free with unskilled labor, so AGI will never be cost-effective."

The weakest argument in favour of AI was the perenial: Moore's Law hence AI!

On the other hand, imagine that computer hardware was stagnant at 1970s levels. It would be pretty plausible that the most efficient algorithms for human-level AI we could find would just be too computationally demanding to experiment with or make practical use of. Hardware on its own isn't sufficient, but it's certainly important for the plausibility of human-level AI when we find performance on so many problems scales with hardware, and our only existence proof of human-level intelligence has high hardware demands..

Also, you occasionally see weak arguments for human-level AI by people who are especially interested in some particular narrow AI field, which reaches superhuman performance, that assume the difficulty of that field is highly representative of all the remaining problems in AI.

http://en.wikipedia.org/wiki/AI_effect seems to detail a weak argument against AI. I was going to sum it up, but the Wikipedia page was doing a better job than I was, so I'll just mention a few quotes from the beginning of the article.

The AI effect occurs when onlookers discount the behavior of an artificial intelligence program by arguing that it is not real intelligence.

Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was chorus of critics to say, 'that's not thinking'."[1] AI researcher Rodney Brooks complains "Every time we figure out a piece of it, it stops being magical; we say, Oh, that's just a computation."[2]

You know what I'd like to see? A strong argument for or against human level AI.

I read the SI's brief on the issue, and all I discovered was that they did a spectacularly good job of listing all of the arguments for and against and demonstrating why they're all manifestly rubbish. The overall case seemed to boil down to "There's high uncertainty on the issue, so we should assume there's some reasonable chance". I'm not saying they're wrong, but it's a depressing state of affairs.

Perhaps in the introduction (or title?) it should be mentioned that AI in the context of the article means human-level AI.

But very rarely do any of these predictors try and show why having computers with say, the memory capacity or the FOPS of a human brain, will suddenly cause an AI to emerge.

Legg put the matter this way:

more computer power makes solving the AGI design problem easier. Firstly, more powerful computers allow us to search larger spaces of programs looking for good algorithms. Secondly, the algorithms we need to find can be less efficient, thus we are looking for an element in a larger subspace.

Moore's Law hence AI!

How is that such a weak argument? I'm all for smarter algorithms - as opposed to just increasing raw computing power - but given the algorithms that are already in existence (e.g. AIXItl, others) we'd strongly - and based on theoretical results - expect there to exist some hardware threshold, that once crossed, would empower even the current algorithms sufficiently for an AGI-like phenomenon to emerge.

Since we know that exponential growth is, well, quite fast, it seems a sensible conclusion to say "(If) Moore's Law, (then eventually) AGI", without even mandating more efficient programming. That or dispute established machine learning algorithms and theoretical models. While the software-side is the bottle-neck, it is one that scales with computing power and can thus be compensated.

Of course smarter algorithms would greatly lower the aforementioned threshold, but if (admittedly a big if) Moore's Law were to hold true for a few more iterations, that might not be as relevant as we assume it to be.

The number of steps for current algorithms/agents to converge on an acceptable model of their environment may still be very large, but compared to future approaches, we'd expect that to be a difference in degree, not in kind. Nothing that some computronium shouldn't be able to compensate.

This may be important because as long as there's any kind of consistent hardware improvement - not even exponential - that argument would establish that AGI is just a matter of time, not some obscure eventuality.

While the internal complexity of software has increased in pace with hardware, the productive complexity has increased only slightly; I am much more impressed by what was done in software twenty years ago than what is being done today, with a few exceptions. Too many programmers have adopted the attitude that the efficiency of their code doesn't matter because hardware will improve enough to offset the issue in the timeframe between coding and release.

At least the Eder quote refers to sheer power - you'll have to provide more for it to come across as an argument for AI.