Open thread, 21-27 April 2014

Metus

If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Thread started before the end of the last thread to ecourage Monday as first day.

If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Thread started before the end of the last thread to ecourage Monday as first day.

Not like I have anything against AI and machine learning literature, but can you give examples of misconceptions?

OK. I've seen a lot of people here say that Eliezer's idea of a 'Bayesian intelligence' won't work or is stupid, or is very different from how the brain works. Those familiar with the machine intelligence literature will know that, in fact, hierarchical Bayesian methods (or approximations to them) are the state of the art in machine learning, and recent research suggests they very closely model the workings of the cerebral cortex. For instance, refer to the book "Data Mining: Concepts and Techniques, 3rd edition" (by Han and Kamber) and the 2013 ... (read more)

2Punoxysm12y

Not so much a specific misconception, but understanding the current state of AI research and understanding how mechanical most AI is (even if the mechanisms are impressive) should make you realize that being a "Friendly AI researcher" is a bit like being a unicorn tamer (and I mean that in a nice way - surely some enterprising genetic engineer will someday make unicorns). Edit: Maybe I was being a little snarky - my meaning is simply this: Given how little we know about what actual Strong AI will look like (And we genuinely know very very little), any FAI effort will face tremendous obstacles in transforming theory into practice - both in the fact that the theory will have been developed without the guidance that real-world constraints and engineering goals provide, and the fact that there is always overhead and R&D involved in applying theoretical research. I think many people here underestimate this vast difference.