> > 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. Hi, I disagree. It's a balance. Sometimes simpler is better, sometimes "more predictive" is better. Simpler can be better because the decrease in computation time, hence, sometimes you want to solve things quickly. > > 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 | Modify > Your Subscription > > > > > > > -- > 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 | Modify Your > Subscription > ------------------------------------------- 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
