Robots and the Future of Jobs: The Economic Impact of Artificial Intelligence

I want to make one point, that this is on the record. But we're going to have a great time discussing "Robots and the Future of Jobs: The Economic Impact of Artificial Intelligence." So I'll start with simple introductions, and then we'll lay out some definitions about the kinds of terms that will be involved in this conversation. So my name is John Paul Farmer. Very happy to be here with three experts on the topic. Next to me is Dr. James Manyika, who is a recovering roboticist. And his day job is at McKinsey, at the McKinsey Global Institute, where he's been focusing on the future of jobs and the future of work in this new era. In the middle, we have Dr. Daniela Rus. Dr. Rus is a professor and roboticist at MIT, and she is also the director of the Computer Science and Artificial Intelligence Lab there. And at the end, we have Edwin van Bommel. Edwin is formerly of McKinsey, but now he's the chief cognitive officer at IPsoft. So, with that, let me lay out some definitions that are going to be important, I think, to following this conversation. You may have read in Foreign Affairs and elsewhere about this fourth industrial revolution, the changes that are happening in our society today and many more that will be coming down the pike. So as we--as we talk about these things, one, we should all be on the same page in terms of what artificial intelligence is. What do we mean when we say AI? And the definition that many accept is it's the development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and even translation between languages. AI is sometimes humorously referred to as whatever computers can't do today. Machine learning is another term you're going to hear a lot, sometimes thought of as a rebranding of AI, of artificial intelligence. But there's one key difference, which is that it takes a much more probabilistic approach as opposed to deterministic. So it looks at not just yes or no; it looks at a 30 percent chance of X, a 10 percent chance of Y, and so on. Big data, a term that I think we've all heard. Data is the raw material. Some people call it the new oil for this new era.