I agree mathematicians are likely to useful in making AGI. If the folks as SIAI were terrible at math, that would be a bad sign indeed.
I wouldn’t say ‘simple’ but, I would be surprised if it were complex in the same way that Watson is complex. Watson is complex because statistical algorithms can be complex, and Watson has a lot of them. As far as I can tell, there’s nothing conceptually revolutionary about Watson, it’s just a neat and impressive statistical application. I don’t see a strong relationship between Watson-like narrow AI and the goal of AGI.
An AGI might have a lot of algorithms (because intelligence turns out to have a lot of separate components), but the difficulty will be understanding the nature of intelligence and coming up with algorithms and proving the important properties about those algorithms. I wouldn’t expect “practical implementation” to be a separate step where you need programmers because I would expect everything to be implemented in some kind of proof environment.
I agree mathematicians are likely to useful in making AGI. If the folks as SIAI were terrible at math, that would be a bad sign indeed.
I wouldn’t say ‘simple’ but, I would be surprised if it were complex in the same way that Watson is complex. Watson is complex because statistical algorithms can be complex, and Watson has a lot of them. As far as I can tell, there’s nothing conceptually revolutionary about Watson, it’s just a neat and impressive statistical application. I don’t see a strong relationship between Watson-like narrow AI and the goal of AGI.
An AGI might have a lot of algorithms (because intelligence turns out to have a lot of separate components), but the difficulty will be understanding the nature of intelligence and coming up with algorithms and proving the important properties about those algorithms. I wouldn’t expect “practical implementation” to be a separate step where you need programmers because I would expect everything to be implemented in some kind of proof environment.