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 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
