On Tue, Jul 13, 2010 at 2:29 AM, Abram Demski <[email protected]> wrote: [The]complaint that probability theory doesn't try to figure out why it was wrong in the 30% (or whatever) it misses is a common objection. Probability theory glosses over important detail, it encourages lazy thinking, etc. However, this all depends on the space of hypotheses being examined. Statistical methods will be prone to this objection because they are essentially narrow-AI methods: they don't *try* to search in the space of all hypotheses a human might consider. An AGI setup can and should have such a large hypothesis space. ------------------------------- That is the thing. We cannot search all possible hypotheses because we could not even write all possible hypotheses down. This is why hypotheses have to be formed creatively in response to an analysis of a situation. In my arrogant opinion, this is best done through a method that creatively uses discreet representations. Of course it can use statistical or probabilistic data in making those creative hypotheses when there is good data to be used. But the best way to do this is through categorization based creativity. But this is an imaginative method, one which creates imaginative explanations (or other co-relations) for observed or conjectured events. Those imaginative hypotheses then have to be compared to a situation through some trial and error methods. Then the tentative conjectures that seem to withstand initial tests have to be further integrated into other hypotheses, conjectures and explanations that are related to the subject of the hypotheses. This process of conceptual integration, a process which has to rely on both creative methods and rational methods, is a fundamental part of the process which does not seem to be clearly understood. Conceptual Integration cannot be accomplished by reducing a concept to True or False or to some number from 0 to 1 and then combined with other concepts that were also so reduced. Ideas take on roles when combined with other ideas. Basically, a new idea has to be fit into a complex of other ideas that are strongly related to it.
Jim Bromer On Tue, Jul 13, 2010 at 2:29 AM, Abram Demski <[email protected]> wrote: > PS-- I am not denying that statistics is applied probability theory. :) > When I say they are different, what I mean is that saying "I'm going to use > probability theory" and "I'm going to use statistics" tend to indicate very > different approaches. Probability is a set of axioms, whereas statistics is > a set of methods. The probability theory camp tends to be bayesian, whereas > the stats camp tends to be frequentist. > > Your complaint that probability theory doesn't try to figure out why it was > wrong in the 30% (or whatever) it misses is a common objection. Probability > theory glosses over important detail, it encourages lazy thinking, etc. > However, this all depends on the space of hypotheses being examined. > Statistical methods will be prone to this objection because they are > essentially narrow-AI methods: they don't *try* to search in the space of > all hypotheses a human might consider. An AGI setup can and should have such > a large hypothesis space. Note that AIXI is typically formulated as using a > space of crisp (non-probabilistic) hypotheses, though probability theory is > used to reason about them. This means no theory it considers will gloss over > detail in this way: every theory completely explains the data. (I use AIXI > as a convenient example, not because I agree with it.) > > --Abram > > On Mon, Jul 12, 2010 at 2:42 PM, Abram Demski <[email protected]>wrote: > >> 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 >> > > > > -- > Abram Demski > http://lo-tho.blogspot.com/ > http://groups.google.com/group/one-logic > *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/> > ------------------------------------------- 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
