On 25/10/2014 16:57, Matt Mahoney via AGI: > No, right now the main problem is the lack of energy efficient computation. > Many AI problems, especially vision, require enormous computation. If > we assume that we need a human brain sized neural network, then we need > several billion 10 petaflop computers to automate the global workforce. > Using current technology, each computer would require several megawatts > of power. The cost of electricity alone makes them uncompetitive with > wages. But I don't think the problem is insurmountable. [...]
This is, I think, contrary to conventional wisdom on the topic - which says that machine intelligence is more of a software problem at this stage. The world spends a lot more on software engineers than hardware engineers. The same also goes for folks like Google - who are seriously working on machine intelligence. Maybe this indicates irrational behaviour - but I think it is more likely that software really is the problem. > Transistor features are already down to less than 100 atoms across. > We will soon reach the physical limits of transistor size and power > consumption, just as Moore's Law has already stalled on clock speed > several years ago. The 2005 clock speed plateau is more to do with our existing computer designs, IMO. It is only on massive synchronous CPUs that clock speeds have reached a plateau. That's not because we can't make smaller and faster components - the progress with networking equipment shows that we can. The problem is synchronously operating such a large number of components in a deterministic manner. Async chips don't have this problem. -- __________ |im |yler http://timtyler.org/ [email protected] Remove lock to reply. ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
