|
|
|
|
|
|
by nefitty
3783 days ago
|
|
|
Oops, excuse my mistaken quote of the Tianhe flops. Regarding the bandwidth bottleneck, it's fascinating to see that as one hardware problem is overcome, the next one looms even greater. The same is happening with the software, as machine learning, etc. is advancing (as contentious as that statement may be to people deep in the industry) the coming hurdles look even more intimidating. The algorithms that need to be developed to reach the milestones of intelligence are incredibly difficult. What excites me is evolutionary algorithms that may be harnessed to reach those milestones. This may be a brute-force method, and researchers would have to know what to tell the algorithms to select for at first, but with increasing computational power, running significant amounts of these algorithms in parallel could be negligible. If you see this comment dhj, have you considered evolutionary computation in your predictions? I'd be interested in what you think, as your clarification of the bandwidth problem was enlightening to me. |
|
|
In other words GAs/EAs are a simplistic and minimal scratching of the surface compared to the complexity we see in nature. The problem is two fold: 1) we guide the evolution with specific artificial goals (get a high score for instance) 2) the ideal "DNA" of a genetic algorithm is undefined.
In evolution we know post-hoc that DNA is at least good enough (if not ideal) for the building blocks. However, we have had very little success with identifying the DNA for genetic algorithms. If we make it commands or function sets we end up with divergence (results get worse or stay the same per iteration rather than better). The most successful GAs are where the DNA components have been customized to a specific problem domain.
Regarding the target goal selection that is a major field of study itself with reinforcement learning. What is the best way to identify reward? In nature it is simple -- survival. In the computer it is artificial in some way. Survival is an attribute or dynamic interaction selected by the programmer.
I believe that multiple algorithmic techniques will come together in a final solution (GA, NN, SVM, MCMC, kmeans, etc). So GA is still part of a large and difficult algorithmic challenge rather than a well defined solution. The algorithmic challenge is definitely non-exponential -- there are breakthroughs that could happen next year or in 100 years.
The bandwidth issue is the main reason I would put AGI at 2045-2065 (closer to 2065), but with the algorithmic issue I would put it post 2065 (in other words, far enough out that 50 years from now it could still be 50 years out). Regardless of the timeframe, it is a fascinating subject and I do think we will get there eventually, but I wouldn't put the algorithmic level closer than 50 years out until we get a good dog, mouse or even worm (c.elegans) level of intelligence programmed in software or robots.