In a comment to my last post, Upgrade Zero One A asks:

I was wondering what your opinion was on John Koza’s work specifically (Invention Machine)and Genetic Programming in general.
Is there any hope that GP could help solve or improve upon existing approaches any of the three areas mentioned?

1. Natural language understanding.
2. Vision, Image/Scene understanding.
3. Creating “consciousness”.

Since this excellent question really made me think beyond the scope of the original post, I decided to create a separate post for the answer, which follows.

This is really asking me to predict the future of mankind here..but I’ll give my best shot.
(I’ve changed the order of the areas)

1. Creating consciousness: No, I don’t think this is an area needing more direct interest. When we have more intelligent algorithms for processing input and turning it into output, the arising consciousness will automatically seem more real. Just program in a way for it to “wail” whenever a specific combination of sensory inputs happen, and I guarantee you, it will *be* real. That’s when the “AI Rights” bill will be presented in the house. One of the clauses will be to deem the “Artificial” in Artificial Intelligence as a politically incorrect/insensitive word.

2. Vision,Image/Scene understanding: Yes. Not so much for the “understanding” part, but very much so for the segmentation part. A perfect or even near-perfect image segmentation algorithm is yet to be found/invented/discovered, even though the task happens in the lowest level of processing in most beings. Separating a tiger or a zebra from the background of a forest is trivial even for tiny animals, but it stumps the best of today’s generic algorithms (they might work ONLY if they have been specially constructed to solve this specific problem itself, or if they are run in a “supervised” manner). The final breakthrough might very well come from using a GP approach.

3. Natural Language Understanding: I highly doubt it. Look at it from “real” evolution’s point of view. It took a gazillion number of species living concurrently, some new ones coming up, some going extinct, for billions of years, for ultimately *one* of them to be able to process natural language. The level of *concurrency* and the *interaction* between the species is enormous. Furthermore, “real” evolution is able to transcend all “local” maxima problems, because it, in effect, has an unlimited time scale. Dinosaurs evolved to be the dominant species 65 million years ago, but the next dominant species(us) was not a descendant of dinosaurs, it came from a totally different branch. And after all these years, this one species developed natural language.
One might say that GP is ideally suited to exactly this kind of task, but I think the scale is way off.

The task was to build an algorithm for understanding natural language, but I equated it to almost having to emulate the human species itself, or in essence, a human level intelligence.
Since GP tries to emulate evolution, and saying that current or somewhat futuristic GP could accomplish this task(NLU), is saying that GP can compress into a few days/weeks what real evolution did in billions of years. Give it a few years to run, maybe. But then we would never know if it will really ever converge or not. That’s the curse of evolution, presented to GP.

Therefore, I think the solution to this problem will come not from GP, but from other traditional directions, where a spark in the mind of some genius will be able to “utilize” the inherent knowledge built into the human brain and bootstrap an algorithm with a human-like capacity to learn.