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The article was addressing the validity of the viewpoint that 'non-biological entities have more reasoning and processing power than biological entities...I thi
by jonmc12 17y ago
The article was addressing the validity of the viewpoint that 'non-biological entities have more reasoning and processing power than biological entities...I think this is all going to happen in the next 40 years...' I tend to agree with you that there is no known quantitative measure to answer this question. Instead (because there is no known quantification method), I proposed the question - if a given learning problem can be solved most cost effectively using only computers, has it satisfied this requirement (of a non-biological entity achieving superior reasoning on a specific problem(s))?
You said 'no', due to the cost of R&D. However, computer-driver R&D, particularly in the form of generative hypothesis testing may one day be able to perform the job of 'understanding the strategies' more accurately and at a lower cost than a human.
Lets say your cost of learning is a function of the resource limitations the article laid out. ie, Cost of Learning = Cost of R&D + Cost of Data + Cost of Domain expertise. What I am suggesting is that if this Cost of Learning is lower for a computer alone than it is for a computer + human, than this is perhaps an adequate test to say 'In this problem area, a non-biological entity has achieved superior reasoning compared to a biological entity'.
btw, I tend to agree with your digression as well. This is a deep problem that is not really investor friendly, but needs sponsorship one way or another to advance the state of the art.
- sdrinf 17y agoPicking single-aspect reasoning lightens the condition (and thus, the strength of the argument) quite a bit. If you take a single learning problem domain, there are a number of fully unsupervised ML/IR systems already out there with superhuman reasoning capabilities. The second part of your argument is a funky one. Fully autonomous R&D-capable AIs (AKA self-improving that is, a problem-solving / reasoning system, that is capable of creating new, and improving upon existing problem-solving / reasoning systems -which, coincidently, might include itself) are the modern equalent of the philosopher's stone, and has not only withstanded attacks in ways that few other problem domains did, but seems to be an AI-hard[1] problem by itself. As I laid down above, market forces does not favour these kind of R&D; thus I predict this problem to be unsolved for the short-term future. On the other paw, the second parameter of your cost function - "cost of data" - can be exchanged by "amount of available data" -which seems to be in an explosive boom from where I stand. Thus, while we're talking predictions, I would hypothise a short-term future with a slow, and incremental improvements upon tackling domains combined with an exponentially growing amount of available data, making even relatively naive learning methods to have results well within the optimal range. It will still require humans. It will still require R&D. But the "cost of learning" will be so much lower, that the process will be routinely employed (successfully) by developers; thus diversifying the overall software market even further. I know it doesn't make for a fancy movie, but throwing away 6.5M years of R&D by mother evolution doesn't seems to be a wise diversification move. [1] http://en.wikipedia.org/wiki/AI-complete http://en.wikipedia.org/wiki/AI-complete