JB: My interest then is in finding the 'right way' to combine some narrow AI algorithms to produce AGI.
So if you combine a prog. that can design lego houses and another that can design skyscrapers, you will get an architectural designer program that can design any structure whatsoever from mud huts to shanty huts to rock houses to columns of toys like a human? Why won't this "cognitive synergy" approach (aka Ben) ever work? Why is it simple-minded? Oh, you're going to add an executive level to the program, something say that's going to think about "building" in general terms, and that'll make a difference? Unfortunately, there isn't any program that can think in concepts like "building", "dwellings", "put together, "take apart" and truly generalise. That's the unsolved conceptualisation problem of AGI. And you're not offering a fraction of a new idea how to solve it. So basically your approach adds up to "more of the same" (cognitive synergy) plus "magic sauce" (executive conceptual level to program). And nothing new. On 4 August 2013 18:03, Jim Bromer <[email protected]> wrote: > > From: [email protected] > > No. AGI is unsolved. I am saying that there are machine learning > > algorithms that build on previous learning. For example, the LZW > > compression algorithm builds its dictionary by extending the words it > > has already learned. Of course this is not AGI because it meets none > > of the requirements of solving language, vision, hearing, robotics, > > art, and predicting human behavior all with human level ability. But > > you don't claim to be trying to solve these problems either. > > > > But you haven't answered my questions. Exactly what will your program > > do? How will you demonstrate that your program "builds on previous > > learning"? What are the tests that you will give it? > > > > Complexity is too great a problem. > 1. What I have said is that if I could get my program to work with text > based IO then it could be modified to work with vision hearing robotics > etcetera. The idea that it could predict human behavior with human level > ability is from your definition, not mine. > 2. What I have directly implied is that if I can't get it to work with > text-based IO I wouldn't be able to get it to work with vision hearing > robotics etcetera. > > However, to continue to get nearer to answering your questions. Since > there are numerous algorithms (narrow AI) that can excel at tasks that > even we would not be able to do, this indicates that if I was able to write > a simple AGI program (which is much less able than the majority of human > beings) then there would be numerous AI things that my program would not be > able to even try. So I would have trouble demonstrating that my Simple > AGI was better than other programs on the kinds of tasks that they excelled > in even if the other program was narrow AI. > > If simple narrow AI algorithms that input numerical values from an IO data > environment and output values in a numerical range were mixed together in > the right way, we could use the old equivalency argument to argue that even > the simplest narrow AI programs could hypothetically be combined to produce > AGI. This argument suggests that machine learning algorithms are not much > more than a step up from the earlier AI methods. My interest then is in > finding the 'right way' to combine some narrow AI algorithms to produce AGI. > > But many people in this group agree that narrow AI programs are not AGI > and so my goal is to build an simple AGI program that could be pitted > against other AGI programs. Even then, another AGI program that included > some powerful narrow methods could presumably beat my program in those > particular challenges. So I started describing conceptual integration and > building on previous learning as a means to begin finding a working > definition of what is needed for an AGI approach. Your responses have give > me great hope that I am as far along in my theories as I thought I was. > > I started to answer your question by pointing to a number of ideas. Think > about the problem of an executive function (starting with meta-awareness) > . And look at the integration problem where integration involves more than > subsequent numerical or logical inclusion of a simple narrow kind. The > executive 'function' won't be involved in every detail but it will > note some of the characteristics of the algorithms as they relate to the > context of the application of the algorithm. The idea that an executive > function should have some greater awareness of the context of an > application of an algorithm sometimes seems to me as if it was a mandatory > requirement of AGI. The executive function will not be able to unerringly > tell if an applied algorithm works or not so it will have to rely on other > methods like cross-analysis and attention to structural integration > methods. If an analysis leads to a strong integration of a model that the > program had been building than the application of the algorithm in that > situation will be explored further. But obviously, there has to be a guard > against self-confirming artificial delusion. > > So while a narrow-AI algorithm could easily defeat my program at the tasks > that it was explicitly designed for, my program, if it works, will be able > to place an idea that it had learned about in a greater context of > ideas. It will be able, if it works, to talk about the idea that it had > just learned about. Since my program has got to be simple, every thing > would be at a very primitive level but it would still be better than a > calculator function which does not know much of anything about the contexts > that it is used in. > > Even this simple example, describing how an AGI program with some > executive meta-awareness that is aware of some of the characteristics of > the context of an application of a function is different than a calculator > function is more subtle than it might first seem. Depending on what you > call awareness or meta-level executive function one might argue (and wisely > so in my opinion) that since certain calculator functions can produce error > remarks in certain situations that even calculator functions have some > meta-level executive artificial 'awareness' of the application of the > function. The only difference is that the calculator 'speaks' and is > programmed in different kinds of languages. So then the meta-awareness and > executive functions of an AGI program must be shaped partially on > previous learning so that it can learn to speak sensibly about the context > and results of an application of a narrower AI method in ways that it > hadn't before and in ways that could be different for different deployments > of the native program. > > Of course, since my program has to be extremely simple, this 'sensible > conversation' is going to be very primitive and open to criticism. > > Jim Bromer > > > > > > > > > > Date: Sat, 3 Aug 2013 19:53:31 -0400 > > Subject: Re: [agi] A Very Simple AGI Project > > From: [email protected] > > To: [email protected] > > > > On Sat, Aug 3, 2013 at 6:07 PM, Jim Bromer <[email protected]> > wrote: > > > > > > From: [email protected] > > > > > > "Building on previous learning" is kind of vague. Doesn't any machine > learning algorithm do that? How will you test your program, measure the > results, and compare it to other approaches to solving the same problems? > > > ----------- > > > > > > Are you saying that there are machine learning algorithms that > constitute working AGI programs? > > > > No. AGI is unsolved. I am saying that there are machine learning > > algorithms that build on previous learning. For example, the LZW > > compression algorithm builds its dictionary by extending the words it > > has already learned. Of course this is not AGI because it meets none > > of the requirements of solving language, vision, hearing, robotics, > > art, and predicting human behavior all with human level ability. But > > you don't claim to be trying to solve these problems either. > > > > But you haven't answered my questions. Exactly what will your program > > do? How will you demonstrate that your program "builds on previous > > learning"? What are the tests that you will give it? > > > > -- > > -- Matt Mahoney, [email protected] > > > > > > ------------------------------------------- > > AGI > > Archives: https://www.listbox.com/member/archive/303/=now > > RSS Feed: > https://www.listbox.com/member/archive/rss/303/24379807-f5817f28 > > 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> > <https://www.listbox.com/member/archive/rss/303/6952829-59a2eca5> | > 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/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
