Because simpler is not better if it is less predictive.
On Thu, Jul 22, 2010 at 1:21 PM, Abram Demski <abramdem...@gmail.com> wrote: > Jim, > > Why more predictive *and then* simpler? > > --Abram > > On Thu, Jul 22, 2010 at 11:49 AM, David Jones <davidher...@gmail.com>wrote: > >> An Update.... >> >> I think the following gets to the heart of general AI and what it takes to >> achieve it. It also provides us with evidence as to why general AI is so >> difficult. With this new knowledge in mind, I think I will be much more >> capable now of solving the problems and making it work. >> >> I've come to the conclusion lately that the best hypothesis is better >> because it is more predictive and then simpler than other hypotheses (in >> that order.... more predictive... then simpler). But, I am amazed at how >> difficult it is to quantitatively define more predictive and simpler for >> specific problems. This is why I have sometimes doubted the truth of the >> statement. >> >> In addition, the observations that the AI gets are not representative of >> all observations! This means that if your measure of "predictiveness" >> depends on the number of certain observations, it could make mistakes! So, >> the specific observations you are aware of may be unrepresentative of the >> predictiveness of a hypothesis relative to the truth. If you try to >> calculate which hypothesis is more predictive and you don't have the >> critical observations that would give you the right answer, you may get the >> wrong answer! This all depends of course on your method of calculation, >> which is quite elusive to define. >> >> Visual input from screenshots, for example, can be somewhat malicious. >> Things can move, appear, disappear or occlude each other suddenly. So, >> without sufficient knowledge it is hard to decide whether matches you find >> between such large changes are because it is the same object or a different >> object. This may indicate that bias and preprogrammed experience should be >> introduced to the AI before training. Either that or the training inputs >> should be carefully chosen to avoid malicious input and to make them nice >> for learning. >> >> This is the "correspondence problem" that is typical of computer vision >> and has never been properly solved. Such malicious input also makes it >> difficult to learn automatically because the AI doesn't have sufficient >> experience to know which changes or transformations are acceptable and which >> are not. It is immediately bombarded with malicious inputs. >> >> I've also realized that if a hypothesis is more "explanatory", it may be >> better. But quantitatively defining explanatory is also elusive and truly >> depends on the specific problems you are applying it to because it is a >> heuristic. It is not a true measure of correctness. It is not loyal to the >> truth. "More explanatory" is really a heuristic that helps us find >> hypothesis that are more predictive. The true measure of whether a >> hypothesis is better is simply the most accurate and predictive hypothesis. >> That is the ultimate and true measure of correctness. >> >> Also, since we can't measure every possible prediction or every last >> prediction (and we certainly can't predict everything), our measure of >> predictiveness can't possibly be right all the time! We have no choice but >> to use a heuristic of some kind. >> >> So, its clear to me that the right hypothesis is "more predictive and then >> simpler". But, it is also clear that there will never be a single measure of >> this that can be applied to all problems. I hope to eventually find a nice >> model for how to apply it to different problems though. This may be the >> reason that so many people have tried and failed to develop general AI. Yes, >> there is a solution. But there is no silver bullet that can be applied to >> all problems. Some methods are better than others. But I think another major >> reason of the failures is that people think they can predict things without >> sufficient information. By approaching the problem this way, we compound the >> need for heuristics and the errors they produce because we simply don't have >> sufficient information to make a good decision with limited evidence. If >> approached correctly, the right solution would solve many more problems with >> the same efforts than a poor solution would. It would also eliminate some of >> the difficulties we currently face if sufficient data is available to learn >> from. >> >> In addition to all this theory about better hypotheses, you have to add on >> the need to solve problems in reasonable time. This also compounds the >> difficulty of the problem and the complexity of solutions. >> >> I am always fascinated by the extraordinary difficulty and complexity of >> this problem. The more I learn about it, the more I appreciate it. >> >> Dave >> *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> > <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
