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 | Modify Your Subscription > > AGI | Archives | Modify Your Subscription ------------------------------------------- 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
