David, I tend to think of probability theory and statistics as different things. I'd agree that statistics is not enough for AGI, but in contrast I think probability theory is a pretty good foundation. Bayesianism to me provides a sound way of integrating the elegance/utility tradeoff of explanation-based reasoning into the basic fabric of the uncertainty calculus. Others advocate different sorts of uncertainty than probabilities, but so far what I've seen indicates more a lack of ability to apply probability theory than a need for a new type of uncertainty. What other methods do you favor for dealing with these things?
--Abram On Sun, Jul 11, 2010 at 12:30 PM, David Jones <[email protected]> wrote: > Thanks Abram, > > I know that probability is one approach. But there are many problems with > using it in actual implementations. I know a lot of people will be angered > by that statement and retort with all the successes that they have had using > probability. But, the truth is that you can solve the problems many ways and > every way has its pros and cons. I personally believe that probability has > unacceptable cons if used all by itself. It must only be used when it is the > best tool for the task. > > I do plan to use some probability within my approach. But only when it > makes sense to do so. I do not believe in completely statistical solutions > or completely Bayesian machine learning alone. > > A good example of when I might use it is when a particular hypothesis > predicts something with 70% accuracy, well it may be better than any other > hypothesis we can come up with so far. So, we may use that hypothesis. But, > the 30% unexplained errors should be explained if possible with the > resources and algorithms available, if at all possible. This is where my > method differs from statistical methods. I want to build algorithms that > resolve the 30% and explain it. For many problems, there are rules and > knowledge that will solve them effectively. Probability should only be used > when you cannot find a more accurate solution. > > Basically we should use probability when we don't know the factors > involved, can't find any rules to explain the phenomena or we don't have the > time and resources to figure it out. So you must simply guess at the most > probable event without any rules for figuring out which event is more > applicable under the current circumstances. > > So, in summary, probability definitely has its place. I just think that > explanatory reasoning and other more accurate methods should be preferred > whenever possible. > > Regarding learning the knowledge being the bigger problem, I completely > agree. That is why I think it is so important to develop machine learning > that can learn by direct observation of the environment. Without that, it is > practically impossible to gather the knowledge required for AGI-type > applications. We can learn this knowledge by analyzing the world > automatically and generally through video. > > My step by step approach for learning and then applying the knowledge for > agi is as follows: > 1) Understand and learn about the environment(through Computer Vision for > now and other sensory perceptions in the future) > 2) learn about your own actions and how they affect the environment > 3) learn about language and how it is associated with or related to the > environment. > 4) learn goals from language(such as through dedicated inputs). > 5) Goal pursuit > 6) Other Miscellaneous capabilities as needed > > Dave > > On Sat, Jul 10, 2010 at 8:40 PM, Abram Demski <[email protected]>wrote: > >> David, >> >> Sorry for the slow response. >> >> I agree completely about expectations vs predictions, though I wouldn't >> use that terminology to make the distinction (since the two terms are >> near-synonyms in English, and I'm not aware of any technical definitions >> that are common in the literature). This is why I think probability theory >> is necessary: to formalize this idea of expectations. >> >> I also agree that it's good to utilize previous knowledge. However, I >> think existing AI research has tackled this over and over; learning that >> knowledge is the bigger problem. >> >> --Abram >> > *agi* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > -- Abram Demski http://lo-tho.blogspot.com/ http://groups.google.com/group/one-logic ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
