Problems and challenge!

Challenge:

If we compare either software systems, or robotic systems to biological systems, we find that our creations are incredibly fragile by comparison. Below we pose some challenges that are aimed at narrowing this distance. The challenges themselves are general in nature--they do not solve particular problems, but the acts of meeting these challenges will force the creation of new general purpose techniques and tools which will allow us to solve more particular problems. 

Challenge 1. 

Biological systems can adapt to new environments--not perfectly, they die in some environments, but often they can adapt. Currently our programs are very brittle, and certainly a program compiled for one architecture cannot run on another architecture. Can we build a program which can install itself and run itself on an unknown architecture? This sounds very difficult. How about a program which can probe an unknown architecture from a known machine and reconfigure a version of itself to run on the unknown machine? Still rather difficult, so perhaps we have to work up to this by making some "blocks worlds" artificial architectures where we can do this. This might lead to some considerations of how future architectures might be designed so that software is self-configurable, and then even perhaps self-optimizing.

Challenge 2.

In reality lots of problems we face in building real AI systems do not get out of hand in terms of the size of problems for individual modules--in particular with behavior-based systems most of the submodules need only deal with bounded size problems. There are other ways theory could have gone. For instance one might try to come up with a theory of computation based on how much divergence there might be in programs given a one bit error in either the program or data representation. If theory were based on this fundamental concern we might start to understand how to make programs more robust.

Challenge 3. 

Recent work with evolutionary system has produced some tantalizing spectacular results, but it is hard to know how to take things from successes and apply them to new problems. We do not have the equivalent of the Perceptron book for evolutionary systems. (Note that these evolutionary systems are much more that straight genetic algorithms as there is both a variable length genotype and a morphogenesis phase that produces a distinctly different phenotype.) We need such a new book of mathematics so that we understand the strengths and weaknesses of this exciting new approach.

Challenge 4. 

Can newer models provide us with new computational tools, and will they lead to new insights to challenge the learning capabilities that we see in biological learning?

Over time we become trapped in our shared visions of appropriate ways to tackle problems, and even more trapped by our funding sources where we must constantly justify ourselves by making incremental progress. Sometimes it is worthwhile stepping back and taking an entirely new (or perhaps very old) look at some problems and to think about solving them in new ways. This takes courage as we may be leading ourselves into different sorts of solutions that will for many years have poorer performance than existing solutions. With years of perseverance we may be able to overcome initial problems with the new approaches and eventually leapfrog to better performance. Or we may turn out to be totally wrong. That is where the courage comes in.

Challenge 5. 

Despite some early misgivings back when chess playing programs had search trees only two deep (Newell et al. 1958), our modern chess programs completely rely on deep search trees and play chess not at all like humans. Can we build a program that plays chess in the way that a human plays? If we could, then perhaps we could prove how good it was by getting it to play GO--tree search just cannot cut it with GO.

Challenge 6. 

All of the competitive speech understanding systems today use hidden Markov models. While trainable, these systems have some unfortunate properties. They have much higher error rates than we might desire, they require some restriction in domain, and they are often inordinately sensitive to the choice of microphone. It seems doubtful that people use HMM's internally (even if one doesn't believe that generative grammars are the right approach either). Can we build a speech understanding system that is based on very different principles?

Challenge 7. 

We live in an environment where we make extensive use of non-speech sound cues. There has been very little work on noise understanding. Can we build interesting noise understanding systems?

Challenge 8. 

Can we build a system by evolution that is better at a non-trivial task than anything that has been built by hand?