What does AI mean?
For the most part, the answer is simple: This may come as a surprise to people outside the field. What have all those AI researchers been doing all these years? The reality is that they have largely given up on the grand ambitions of AI and are instead working on increasingly specialized subproblems: In AI, the best solution to a problem viewed in isolation often gets in the way of solving the larger problem.
But within a decade or so, computers will surpass the computing power of the human brain.
While computers are extremely efficient at specific tasks, such as arithmetic, human brains are still ahead in terms of the number of operations they can perform per second.
Previous attempts to solve end-to-end AI problems have failed in one of two ways. Some oversimplified the problems to the point that the solutions did not transfer to the real world.
Others ran into a wall of engineering complexity: Examples from other fields of science and technology demonstrate just how powerful this can be: Today these things seem like second nature to their practitioners, but at the time they were far from obvious.
The key is finding the right language in which to formulate and solve problems. What should be the language of AI? At the least, we need a language that combines logic and probability. Logic can handle the complexity of the real world—large numbers of interacting objects, say, or multiple types of objects—but not its uncertainty.
The last decade has seen real progress in this direction, but these are still early days.
Pedro Domingos is an associate professor of computer science and engineering at the University of Washington in Seattle. Will you lead or follow? Join us at EmTech Digital Feb 24, · Expert chess players run the gamut of feelings when solving chess problems, according to a study with significant implications for machine intelligence.
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