Re: [agi] general patterns induction
oh tanks a lot, would you please email it to me!!? Pablo, If you are interested in Solomonoff induction and don't want to spend all the money to buy the book just yet then you might be interested in a paper I wrote a few years back that starts from basic computation theory and proves all the key results. It about 25 pages of mathematics in PDF format, I can email it to you if you like. The key points are: Solomonoff induction will learn anything that is computationally expressible (i.e. anything useful) with an error rate that falls to zero faster than 1/n where n is the number of bits of input data that the system has been given. Solomonoff induction is not computable and it is difficult to approximate well. Most learning methods in statistics and machine learning can in some sense be proven to be computable approximations to Solomonoff induction. So, in short, it's an interesting theoretical model that's amazingly powerful but it's not something you can directly use in practice. The prize for an amazingly powerful and practical system is still very much up for grabs :) If you're serious about data compression also check out a book called Text Compression by Bell, Cleary and Witten. Cheers Shane --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/? [EMAIL PROTECTED] --- Estas pagando mas por lo mismo?. Tarifa plana con Antivirus en tu mail. Internet con garantia Montevideo COMM. Informate por el 402 25 16 o en http://www.montevideo.net.uy/hnnoticiaj1.exe?47,0 --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
RE: [agi] Grounding
True. The more fundamental point is that symbols representing entities and concepts need to be grounded with (scalar) attributes of some sort. How this is *implemented* is a practical matter. One important consideration for AGI is that data is easily retrievable by vector distance (similarity) and that new patterns can be leaned (unlearned) incrementally. Peter Again, I agree with your general point, but I'll observe that *vector distance* is only one among many ways of measuring similarity! We do use vector distance for some things in Novamente, but our more fundamental distance measure is based on what we call the inference metric... a different way of measuring distances that still obeys the metric space axioms, but cooperates more nicely with probabilistic inference. Somewhere in the future, there lies a general theory of AGI of which all our current attempts will be comprehensible as special cases ;) -- Ben G --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
Re: [agi] introduction
Damien Sullivan wrote: Hi! I joined this list recently, figured I'd say who I am. Well, some of you may know already, from extropians, where I used to post a fair bit :) or from my Vernor Vinge page. But now I'm a first year comp sci/cog sci PhD student at Indiana University, hoping to work on extending Jim Marshall's Metacat in Hofstadter's lab. Nothing much has really happened beyond hope and a few meetings and taking his group theory class. I've been reading Eliezer's _Levels_ pages, and having Andy Clark's _Being There_ around, but mostly my life has been classes. Mostly the OS class, actually. Sigh. -xx- Damien X-) --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED] Damien, Hi. I'm am quite interested in Jim's Metacat also. It's on my To-Do list to get it running under linux... but the way my workload is going I think Jim will get his planned re-write done first. :)It would be interesting to hear about what new directions Metacat is going in. Welcome to the list. Michael Roy Ames --- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
