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
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