Re: [agi] Re: Huge Progress on the Core of AGI
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 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 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
[agi] Mechanical Analogy for Neural Operation!
Everyone has heard about the water analogy for electrical operation. I have a mechanical analogy for neural operation that just might be "solid" enough to compute at least some characteristics optimally. No, I am NOT proposing building mechanical contraptions, just using the concept to compute neuronal characteristics (or AGI formulas for learning). Suppose neurons were mechanical contraptions, that receive inputs and communicate outputs via mechanical movements. If one or more of the neurons connected to an output of a neuron, can't make sense of a given input given its other inputs, then its mechanism would physically resist the several inputs that didn't make mutual sense because its mechanism would jam, with the resistance possibly coming from some downstream neuron. This would utilize position to resolve opposing forces, e.g. one "force" being the observed inputs, and the other "force" being that they don't make sense, suggest some painful outcome, etc. In short, this would enforce the sort of equation over the present formulaic view of neurons (and AGI coding) that I have suggested in past postings may be present, and show that the math may not be all that challenging. Uncertainty would be expressed in stiffness/flexibility, computed limitations would be handled with over-running clutches, etc. Propagation of forces would come close (perfect?) to being able to identify just where in a complex network something should change to learn as efficiently as possible. Once the force concentrates at some point, it then "gives", something slips or bends, to unjam the mechanism. Thus, learning is effected. Note that this suggests little difference between forward propagation and backwards propagation, though real-world wet design considerations would clearly prefer fast mechanisms for forward propagation, and compact mechanisms for backwards propagation. Epiphany or mania? Any thoughts? Steve --- 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
