The goal of artificial intelligence (at least according to the field's founders) is to create computers whose intelligence equals or surpasses humans'. Achieving this goal is the famous "AI problem." To some, AI is the manifest destiny of computer science. To others, it's a failure: clearly, the AI problem is nowhere near being solved. Why? For the most part, the answer is simple: no one is really trying to solve it. 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: not just machine learning or natural-language understanding, say, but issues within those areas, like classifying objects or parsing sentences.

I think that this "divide and conquer" approach won't work. In AI, the best solution to a problem viewed in isolation often gets in the way of solving the larger problem. To make real progress, we need to work on "end to end" problems--self-contained tasks, like reading text and answering questions, that entail a number of subtasks (see "Intelligent Software Assistant"). Until now, it hasn't really been possible to do this, because the necessary computing power was not available. 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. When this is applied to things that ­people are good at, like vision and language understanding, computers lose.)