There is a distinction between gathering known knowledge and representing that known knowledge within a knowledge base of a given structure and learning
Knowledge is generally facts. The "Total Known Knowledge" and "Machine Known Knowledge" representation approach a ratio of 1... The ratio is the "Machine Known Knowledge" divided by "Total Known Knowledge" Real Machine Learning is the deductions-inferences that can be drawn from machine known knowledge by the AI programs. Here lies the true learning to learn. Rather than the acquisition of known knowledge. Quote from a SureTrade Commercial... "Insight is critical. Everything that you respond to has already taken place. Understanding and being able to act on that knowledge is power. Fail to do either and you are simply left a witness to history. Dan Goe ---------------------------------------------------- >From : Yan King Yin <[EMAIL PROTECTED]> To : [email protected] Subject : Re: [agi] Re: Representing Thoughts Date : Mon, 12 Sep 2005 23:09:57 +0800 > Will Pearson wrote: > > Define what you mean by an AGI. Learning to learn is vital if you wish to > > try and ameliorate the No Free Lunch theorems of learning. > > I suspect that No Free Lunch is not very relevant in practice. Any learning > algorithm has its implicit way of generalization and it may turn out to be > good enough. > > Having a system learn from the environment is superior than programming it > > by hand and not be able to learn from the environment. They are not mutually > > exclusive. It is superior because humans/genomes have imperfect knowledge of > > what the system they are trying to program will come up against in the > > environment. > > I agree that learning is necessary, like any sensible person would. The > question is how to learn efficiently, and *what* to learn. High level > mechanisms of thinking can be hard-wired and that would save a lot of time. > > It depends what you characterise as learning, I tend to include such things > > as the visual centres being repurposed to act for audio processing in blind > > individuals as learning. there you do not have labeled examples. > > My point is that unsupervised learning still requires labeled examples > eventually. Your human brain example in not pertinent to AGI because you're > talking about a brain that is already intelligent, recruiting extra > resources. We should think about how to build an AGI from scratch. Then you > may realize that unsupervised learning is problematic. > > Personally as someone trying to build a system that can be modify itself as > > much as possible, I am simply following in the footsteps of dealing with the > > problems that evolution had to deal with when building us. It is all problem > > solving of sorts (and as such comes under the heading of AI), but dealing > > with failure, erroneous inputs , energy usage are much more fundemental > > problems to solve than high level cognition. > > We do not have to duplicate the evolutionary process. I think directly > programming a general reasoning mechanism is easier. My approach is to look > at how such a system can be designed from an architectural viewpoint. > > This I don't agree with. Humans and other animals can reroute things > > unconsciously, such as switching the visual system to see things upside down > > (having placed prisms in front of the eyes 24/7). It takes a while (over > > weeks), but it then it does happen and I see it as > > evidence for low-level self-modification. > > Your example is show that experience can alter the brain, which is true. It > does not show that the brain's processing mechanism is flexible -- namely > the use of neural networks for feature extraction, classification, etc. > Those mechanisms are fixed. Likewise, we can directly program an AGI's > reasoning mechanisms rather than evolve them. > > It can speed up the acquisition of basic knowledge, if the programmer got > > the assumptions about the world wrong. Which I think is very likely. > > This is not true. We *know* the rules of thinking: induction, deduction, > etc, and they are pretty immutable. Why let the AGI re-learn these rules? > > That is all I am trying to do at the moment make tools. Whether they are > > tools to do what you describe as making a Formula 1 car, I don't know. > > We need a bunch of researchers to focus on making the first functional AGI. > This requires a lot of determination and not getting distracted by too many > theoretical issues. Which doesn't mean that theory is unimportant. But we > need an attitude that is more practical and down-to-earth. My observation so > far is that a lot of researchers have slight different goals in mind and the > result is that we're not really connecting with each other. > "Coming together is a beginning; keeping together is progress; working > together is success." -- Henry Ford > yky > > ------- > To unsubscribe, change your address, or temporarily deactivate your subscription, > please go to http://v2.listbox.com/member/[EMAIL PROTECTED] ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
