I also want to mention that I develop solutions to the toy problems with the real problems in mind. I also fully intend to work my way up to the real thing by incrementally adding complexity and exploring the problem well at each level of complexity. As you do this, the flaws in the design will be clear and I can retrace my steps to create a different solution. The benefit to this strategy is that we fully understand the problems at each level of complexity. When you run into something that is not accounted, you are much more likely to know how to solve it. Despite its difficulties, I prefer my strategy to the alternatives.
Dave On Mon, Jun 28, 2010 at 3:56 PM, David Jones <[email protected]> wrote: > That does not have to be the case. Yes, you need to know what problems you > might have in more complicated domains to avoid developing completely > useless theories on toy problems. But, as you develop for full complexity > problems, you are confronted with several sub problems. Because you have no > previous experience, what tends to happen is you hack together a solution > that barely works and simply isn't right or scalable because we don't have a > full understanding of the individual sub problems. Having experience with > the full problem is important, but forcing yourself to solve every sub > problem at once is not a better strategy at all. You may think my strategies > has flaws, but I know that and still chose it because the alternative > strategies are worse. > > Dave > > > On Mon, Jun 28, 2010 at 3:41 PM, Russell Wallace < > [email protected]> wrote: > >> On Mon, Jun 28, 2010 at 4:54 PM, David Jones <[email protected]> >> wrote: >> > But, that's why it is important to force oneself to solve them in such a >> way that it IS applicable to AGI. It doesn't mean that you have to choose a >> problem that is so hard you can't cheat. It's unnecessary to do that unless >> you can't control your desire to cheat. I can. >> >> That would be relevant if it was entirely a problem of willpower and >> self-discipline, but it isn't. It's also a problem of guidance. A real >> problem gives you feedback at every step of the way, it keeps blowing >> your ideas out of the water until you come up with one that will >> actually work, that you would never have thought of in a vacuum. A toy >> problem leaves you guessing, and most of your guesses will be wrong in >> ways you won't know about until you come to try a real problem and >> realize you have to throw all your work away. >> >> Conversely, a toy problem doesn't make your initial job that much >> easier. It means you have to write less code, sure, but what of it? >> That was only ever the lesser difficulty. The main reason toy problems >> are easier is that you can use lower grade methods that could never >> scale up to real problems -- in other words, precisely that you can >> 'cheat'. But if you aren't going to cheat, you're sacrificing most of >> the ease of a toy problem, while also sacrificing the priceless >> feedback from a real problem -- the worst of both worlds. >> >> >> ------------------------------------------- >> 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/?&; >> Powered by Listbox: 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
