Why predictions about the future of AI are wrong

I came across this great conversation with Rodney Brooks on econtalk on why many people are wrong about the future of AI with his accompanying MIT technology article. He argues that the following seven reasons lead to wrong predictions about the future of AI:

1. Overestimating and underestimating (Amara’s Law: We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run)
2. Imagining magic (If it is far enough away from the technology we have and understand today, then we do not know its limitations. And if it becomes indistinguishable from magic, anything one says about it is no longer falsifiable. Nothing in the universe is without limit.)
3. Performance vs competence (People hear that some robot or some AI system has performed some task. They then generalize from that performance to a competence that a person performing the same task could be expected to have. And they apply that generalization to the robot or AI system)
4. Suitcase words (When people hear that machine learning is making great strides in some new domain, they tend to use as a mental model the way in which a person would learn that new domain. However, machine learning is very brittle, and it requires lots of preparation by human researchers or engineers, special-purpose coding, special-purpose sets of training data, and a custom learning structure for each new problem domain.)
5. Exponentials (When people are suffering from exponentialism, they may think that the exponentials they use to justify an argument are going to continue apace. But Moore’s Law and other seemingly exponential laws can fail because they were not truly exponential in the first place.)
6. Hollywood scenarios (The plot for many Hollywood science fiction movies is that the world is just as it is today, except for one new twist. They ignore the fact that if we are able to eventually build such smart devices, the world will have changed significantly by then. We will not suddenly be surprised by the existence of such super-intelligences. They will evolve technologically over time, and our world will come to be populated by many other intelligences, and we will have lots of experience already.)
7. Speed of deployment (Capital costs keep physical hardware around for a long time, even when there are high-tech aspects to it, and even when it has an existential mission. Almost all innovations in robotics and AI take far, far, longer to be really widely deployed than people in the field and outside the field imagine.)