On Tue, Feb 17, 2015 at 4:18 PM, Aaron Hosford <[email protected]> wrote: >> We can prove that good solutions must have high algorithmic complexity. > > Could you give a sketch for such a proof?
Suppose you have a simple predictor with Kolmogorov complexity N. Then I can create a sequence with about the same complexity that your predictor can't predict. My program simulates your program and outputs the opposite of whatever you predict. The long version by Legg: http://arxiv.org/abs/cs/0606070 > Why does the complexity have to > initially reside in the algorithm, rather than in the environment? Because the environment can lie. I could give your predictor some code that would make it a better predictor, or worse. It has no way to know. And once it has collected all human knowledge, learning will slow down because it will have to learn by experiment just like we do. Some experiments take a long time no matter how smart you are. For example, we know very little about what therapies will slow down aging. Any experiment takes decades to get an answer. This is the reason that the rate of increase of worldwide life expectancy peaked at 0.2 years per year in the 1990's (1970's in developed countries) and is declining. And yes, we have machines that can go to the moon. They are stronger and faster. They are also more intelligent, depending on what test you use for intelligence. If the test is arithmetic speed and accuracy, then machines surpassed human level intelligence 100 years ago. But again, intelligence is not the goal. -- -- Matt Mahoney, [email protected] ------------------------------------------- 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
