![]() And roughly speaking, people’s ability to watch continuous scenes with no change is not 100%. Well, computer vision is a solved problem compared to human vision. So in peacetime anyway and I think also in wartime what do our men and women do mostly? They mostly watch things. ![]() That we recommend, for example, the creation of the equivalent of an AI institute.Īnd I can give you a positive and a negative example. One of the most important point we made is that the military is not leading in AI. There are all sorts of examples of all of this. And they cover a lot of ground, that looking at this audience you’re familiar with: the nature of innovation in the military, the kind, what are the problems and so for the Third Offset. We made a set of 10 recommendations, which are public and you can read them and I would encourage you to do so. Paul Scharre: So in your role as the chair of the Defense Innovation Advisory Board, what is your assessment of how is the Defense Department is doing in terms of looking at artificial intelligence and incorporating this?Įric Schmidt: So a couple of years ago I became the chairman of an external advisory board called the Defense Innovation Board and the idea was to try to get the military up to speed with things which are going outside the military. And the fact that we have so many examples of us that we can measure means enormous gains in our healthcare globally, for everybody. Whereas trying to understand traffic, right? As an example something that occurs every day, is far, far easier because you have so much training data.Īnother reason to think that AI works so well in biology is that as much as we would like to find differences among ourselves, we are all the same biologically. Right? So it’s much, much harder, if you will, to apply AI to that problem. So the classic example: people would say “well why can’t you figure out terrorism?” Well the good news is terrorism is very rare. And when I say a great deal I mean like, millions of entries in the matrices, billions of pieces of data. That’s how valuable these people are in the marketplace.īut the other thing that’s worth saying is that these algorithms, at least today, require a great deal of training data. And we’re in a situation where those kinds of people, graduating out of Carnegie Mellon and others, are in the highest demand I’ve ever seen with huge multimillion dollar packages in their twenties coming out. So at the moment it’s in the province of very highly skilled mathematicians, PhDs, those sorts of people. ![]() So it’s a slightly different animal if you will. There’s evidence for example that the face recognition and image recognition is using slightly different parameters than what we do as humans. We don’t really exactly understand how the learning occurs. The scientists that are working on it cannot for example explain certain failure modes. The first observation is that this stuff is still essentially magic. Paul Scharre: What do you see as the key drivers of competitive advantage? What are the things that allow a company to be ahead of rivals? Is it human capital? Is it data? What are those key components?Įric Schmidt: It’s sort of both. When I was CEO we had mobile first, the new CEO announced AI-first as our core strategy. A lot of people believe it’ll be more than that, but that I know from the standpoint of Alphabet because it touches everything we’re doing. And I think it’s true that pretty much all of the knowledge system kinds if companies will become AI companies in that sense. I wanted to start by asking you from where you sit looking at your companies and thinking about the United States as a whole, how big is artificial intelligence? How important this to be a leader in Artificial Intelligence?Įric Schmidt: If you’re in a business that has a lot of data, and in an information business of some kind, it’s more than big, it’s essential because it will become your competitive advantage. This transcript has been edited for clarity.
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