Being interested in the psychology of creativity, I am fascinated by the ways in which people get creatively stuck – and the excuses they give themselves for not tackling creative problems. This is a beauty:
Steve: My assertion is that it is probably IMPOSSIBLE to understand many of the aspects of intelligence (like self-organization) without heavy math, wet lab experimentation, new scanning technology, and/or other out-of-discipline research. If nothing else, the last half-century has clearly shown that there are no easy answers, no "low hanging fruit" to gather. Plenty of people just as smart as us have dashed their careers by trying to "reason things out" without the advanced tools to simply examine the solution. I have enough of a sense of history not to do the same. ”Wow, intelligence/the brain is so-o-o-o complex, dude....” Well, depends which brain – and which problems – you’re looking at. The classic mistake is to think of intelligence purely in terms of the brain (or the intelligent machine/material). That’s like thinking of photography purely in terms of cameras. You also – in fact first - have to look at the problems intelligence tackles – just like you also – in fact first – have to look at the subjects the photographer captures, and the problems of capturing those subjects. It’s so easy to get lost in technology. In fact, the simple nematode worm has only 200 neurons and yet manages to solve all kinds of problems. And a slime mould has even less resources and yet also manages to solve problems. Problems on the other side, can be thought of in extremely complex terms - like how to tackle mathematical problems of everyone’s favourite (and total irrelevance) – complexity. “Wow, complexity is so.o.o.o complex, dude...” Or you can think of – and represent tackling problems as ... negotiating the forking paths of a maze. All problems *are* – or were – represented by programmers as negotiating the forking paths of a maze – in the form of a flow chart. So if you want to start solving the problem of AGI, try and have ideas about how a slime mould navigates a maze: http://goose.ycp.edu/~kkleiner/fieldnaturalhistory/fnhimages/l12images/Maze-solving%20amoeboid.asp_files/cs_client_data/3636046.pdf Tackling a maze problem like that was how Shannon got AI started. Tackling a problem like this can get AGI started. Just remember - and this is EXTREMELY important - the slime mould has a DIFFERENT problem to that of Shannon’s mechanical mouse. You have to look at the problem from the POV of the *slime mould* and NOT the programmer – really put yourself physically in its place. Shannon’s mouse was effectively working with Shannon’s *full knowledge* and *full overview* of mazes – the classic error all AGI-ers make. But a real world slime mould (or animal) doesn’t have an overview or full knowledge of any maze. It just sees two walls and an opening. It doesn’t know what lies beyond. It’s not doing mathematical computations. It’s exploring unknown territory – just as all our evolutionary ancestors have done throughout evolution – and all human creative.types have done. So how can a machine do that? Ideas, (and not excuses), Steve? ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
