I heard the author of this book on a Dallas/Fort Worth NPR interview show called "Think". In it he poo-pooed the importance of learning the connectome, saying the connectome wasn't really important, that what was important was learning the brain's algorithm.
It is stunningly dumb to minimize the importance of the connection architecture of a neural net, particularly if you include connection weights in the connectome, which many brain connection metrics partially measure. On Sat, Feb 13, 2016 at 9:54 AM, Fatmah via AGI <[email protected]> wrote: > Hi all, > > I scanned the book, and agree with some of your points below. > > I need resources for intelligent algorithms implemented nowadays in > collective intelligence, distributed intelligence...etc. Also I need > resources for best winning algorithms used in social Apps. > > Your help is appreciated, > Fatmah > > Sent from my iPhone > > On Feb 12, 2016, at 9:44 AM, Danko Nikolic <[email protected]> > wrote: > > Dear Tim, > > Thank you for that information. I am with you on your main critique. > > Do you think that Legg's proof is somehow related to the "no free lunch" > theorem in optimization theory? > > The two combined seem to point quite strongly that: > - there never will be a silver bullet algorithm for learning or for > prediction > - the "solution" to strong AI will be complex > > Am I stretching it if I expand the conclusions even further by stating: > > - strong AI needs to be so complex that human developers cannot understand > it sufficiently to "program" it? (Legg also points out that complex > algorithms cannot be analyzed due to Goedel's incompleteness.) > > > If that sounds correct, would it be fair to say that then necessarily this > follows: > - So, in theory, strong AI can only be evolved ? > > > And if all of the above was right, is there any alternative but seeking > methods to accelerate the evolving procedures (such as e.g., > AI-Kindergarten)? > > > > Thank you. > > Danko > > On 12/02/16 04:52, TimTyler wrote: > > I read and reviewed Pedro Domingos's book "The Master Algorithm". > My review is here: > > http://smile.amazon.com/gp/cdp/member-reviews/AYJ8P83FHQARZ > > To summarize my biggest criticism: > > The book documents the search for a silver bullet of machine intelligence. > The author didn't seem to be familiar with Legg's 2008 proof that machine > intelligence will be complex. In "Is there an Elegant Universal Theory > of Prediction?" Legg offers a simple, constructive proof that, for any > prediction algorithm, there exist sequences with similar Kolmogorov > complexity to the prediction algorithm, that the predictor can never > learn how to predict. Legg's conclusion is that successful general > purpose predictors of complex sequences will themselves necessarily > be highly complex. Machine intelligence doesn't have a silver bullet. > > -- > __________ > |im Tyler http://timtyler.org/ > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/27953441-c5c84d1c> | > Modify <https://www.listbox.com/member/?&;> Your Subscription > <http://www.listbox.com> > > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/18570668-a1f923df> | > Modify <https://www.listbox.com/member/?&;> Your Subscription > <http://www.listbox.com> > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/8630185-a57a74e1> | Modify > <https://www.listbox.com/member/?&;> > Your Subscription <http://www.listbox.com> > ------------------------------------------- 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
