[lbenes]

While I agree with your point that intelligence must be defined rigorously. We don't need to leave it to the philosophers. After categorizing the types of definitions, Shane Legg successfully used "Property of an agent that interacts with its environment so as to successfully achieve goals across a wide range of environments"[1] for his work in AI.

What Alva Noë is hinting at is the flaw with weak or pseudo-intelligence, as Alva calls it. As impressive as deep blue is with chess, or Watson with Jeopardy, they fail the "goals across a wide range of environments" test. This is clear with deep blue, and Watson once you realize it's just a glorified search engine.

I often hear people claim Watson learns because it improves based on the answer in the category. While it does use previous answers to tweak the final ranking algorithm. All of this is lost once the category is finished. To really "teach" Watson, you need to add more data to its index and re-index offline.

We have a workable definition of intelligence. What we're missing is a fundamental primitive unit of strong AI. Until this is found, AI will always remain just a bag of tricks, only capable of solving problems its programmers planner for.

[1] https://www.youtube.com/watch?v=V6umr1OP8uo

[baddox]

There's still a huge dearth of rigor in defining both the width of a range of environments, the degree of success in achieving goals, and the significance of various goals. I'll expound briefly on the first. Who is to say that the range of "environments" a chess supercomputer faces is narrower than the environments a human faces? Obviously, we intuit our range of environments to be wider, but can we explain why rigorously?

[lbenes]

It's more than intuition. Both chess and jeopardy are subsets of the human environment. Watson couldn't handle tic-tac-toe, never-mind chess. Change one rule of chess, and human could cope but Deep Blue would need to be rewritten.