Ben,
I see the credit problem as a result from a process of inference, not as a precondition, and certainly not as something that needs to be engineered into a GI system. The problem with your approach is that you are using your own inference, the inference in your own brain, to draw conclusions such as the credit problem, and then you try to engineer it into a system. This is narrow AI. Even if you were to succeed doing that, it would still be narrow AI, because the inference that reached the conclusion would have stayed in your brain. You think you have identified a problem that needs to be solved, and then again you use your own inference to solve that problem. This leads you to another problem, which you also solve in your brain. You solve all problems yourself without ever allowing the system to do it by itself. Are you really trying to address grounding by way of geometric figures and centers of gravity? Did you once say you have a son? How do you educate him? Do you solve all problems for him? No, at some point you trust him, you see him confronting a problem and you let *him* solve it. And pretty soon you discover that he has become an adult and needs you no more. What would he be if you had kept solving all problems for him? I like to think that AGI requires the ultimate sacrifice: the sacrifice of yourself. At some point you have to just let go, get out of the scene and let it be and do its thing. So, yes, there are many problems I have not yet "resolved" and do not feel compelled to resolve myself. Evolution did not create the brain to solve problems or to assign credit. It created the brain to survive. It put EI into it for a reason. Putting credit before inference is a reversal of causality. And of course it is difficult. Because you are left with solving all the problems yourself. Which is what the narrow AI people have been doing all along. Toy problems? Yes, I solved only some toy problems so far. But there is not scale in causets. Where do you set the scale? What property of causets are you going to use to set scale? I solved a set with 18 elements, then one with 33, then one with 1143. So where do I stop? You say it will not work for, say, 3541 elements? The size of the set only affects the time it takes to solve it. It is playing a role in my toy problems only because I lack resouces to go large scale. Sergio From: Ben Goertzel [mailto:[email protected]] Sent: Saturday, June 09, 2012 3:41 PM To: AGI Subject: Re: [agi] Representations and data structures I'm currently happy with a more flexible weighted, labeled hypergraph representation scheme. I think that causation is only one among a host of different sorts of relationship that a human-like GI needs to internally represent, and I don't see a need to give it a role at the foundation of the representational scheme. Inferred causation is important, because it drives the direct choice of actions. However, the difficulty of the assignment of credit problem (which your formalism certainly does not resolve in any way you've yet articulated), seems to imply that directly tying everything in the mind to actions via explicit causal relations, is not a feasible way to proceed in a scalable system, though it will work in toy examples... -- Ben G On Sat, Jun 9, 2012 at 3:42 PM, Sergio Pissanetzky <[email protected]> wrote: In Computer Science, data structures are usually designed for computational efficiency. I use a FIFO queue if I am planning to use a FIFO algorithm, so the queue supports the algorithm efficiently. I use a database if I need to store large amounts of information and retrieve different views of it with the least possible amount of computer effort. Such representations suffer of one limitation: they completely divorce data from meaning, that is, from what is sometimes called "metadata." Database tables contain symbols, but not their meaning. If I have a database with employees and their names and addresses, and I want the address of a certain employee, I have to write a SQL query that references the exact tables where that information is in. Or, write a case-specific front end that "knows" everything that the database does not. Meaning remains with the user. In Physics, representations are chosen based on their mathematical properties, and on how well those properties represent the physics of the system that is being represented. Efficiency in computation is not considered. Frequently, the representations satisfy group-theoretical requirements necessary to account for the symmetries of the physical system. For example, I use tensors to represent mechanical systems because tensor representations are invariant under coordinate transformations and can adequately represent physical quantities that have magnitudes and directions, such as position, velocity, force, and moments of inertia. I use spinors to represent quantum systems with spin because spinors have the adequate transformation properties. I use the Lorentz group to represent relativistic systems again because the representation remains invariant under transformations in spacetime. For complex causal systems, causal sets are the adequate representation. Causal sets have many intrinsic properties that correspond to observed properties of the complex systems. They have attractors, hierarchical structures, potential wells with levels of energy, the butterfly effect, deterministic chaos. Causal sets are isomorphic to algorithms, and as such they can represent behavior. Any algorithm, any computer program, can be considered as a causal set. With the addition of a functional that represents physical action and corresponds to the exact point where Physics enters the pure Mathematics of causal sets, causal sets exhibit transformations that are behavior-preserving. These transformations are a type of inference known as "emergent inference." Inference is any process that can derive new facts from existing facts. Causal sets map from unstructured causal sets, of the kind that are obtained from sensors, to structured causal sets, where the information collected from the sensors is the existing fact and the resulting structure is the new fact. When seen as a map, emergent inference can be considered as a function. The function is deterministic, uncomputable, and unpredictable. The mapping is bijective, the size of the sets is countably infinite. There exists an inverse function, which is deterministic and computable. The structures are the same used in object-oriented analysis, and can be represented by UML diagrams. The behavior-preserving transformations are equivalent to refactoring. Causal sets also apply to models of cognition. Sergio AGI | <https://www.listbox.com/member/archive/303/=now> Archives <https://www.listbox.com/member/archive/rss/303/212726-11ac2389> | Modify Your Subscription <https://www.listbox.com/member/archive/rss/303/212726-11ac2389> <https://www.listbox.com/member/archive/rss/303/212726-11ac2389> -- Ben Goertzel, PhD http://goertzel.org "My humanity is a constant self-overcoming" -- Friedrich Nietzsche <https://www.listbox.com/member/archive/rss/303/212726-11ac2389> AGI | Archives | Modify Your Subscription <https://www.listbox.com/member/archive/rss/303/212726-11ac2389> <https://www.listbox.com/member/archive/rss/303/212726-11ac2389> ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
