YNH: And increasingly, also biologists! And, you know, it goes back to the question, what should we do? So, we should teach ethics to coders as part of the curriculum, that the people today in the world that most need a background in ethics, are the people in the computer science departments. So it should be an integral part of the curriculum. And also in the big corporations, which are designing these tools, should be embedded within the teams, people with backgrounds in things like ethics, like politics, that they always think in terms of what biases might we inadvertently be building into our system? What could be the cultural or political implications of what we're building? It shouldn't be a kind of afterthought that you create this neat technical gadget, it goes into the world, something bad happens, and then you start thinking, “Oh, we didn't see this one coming. What do we do now?” From the very beginning, it should be clear that this is part of the process.

FL: I do want to give a shout out to Rob Reich, who introduced this whole event. He and my colleagues, Mehran Sahami and a few other Stanford professors have opened this course called Computers, Ethics and Public Policy. This is exactly the kind of class that’s needed. I think this quarter the offering has more than 300 students signed up for that.

"We should be focusing on technology that has a more nuanced understanding of human intelligence." Fei-Fei Li

NT: Fantastic. I wish that course has existed when I was a student here. Let me ask an excellent question from the audience that ties into this. How do you reconcile the inherent trade-offs between explainability and efficacy and accuracy of algorithms?

FL: Great question. This question seems to be assuming if you can explain that you're less good or less accurate?

NT: Well, you can imagine that if you require explainability, you lose some level of efficiency, you're adding a little bit of complexity to the algorithm.

FL: So, okay, first of all, I don't necessarily believe in that. There's no mathematical logic to this assumption. Second, let's assume there is a possibility that an explainable algorithm suffers in efficiency. I think this is a societal decision we have to make. You know, when we put the seatbelt in our car driving, that's a little bit of an efficiency loss because I have to do the seat belt movement instead of just hopping in and driving. But as a society, we decided we can afford that loss of efficiency because we care more about human safety. So I think AI is the same kind of technology. As we make these kind of decisions going forward in our solutions, in our products, we have to balance human well-being and societal well-being with efficiency.

NT: So Yuval, let me ask you the global consequences of this. This is something that a number of people have asked about in different ways and we've touched on but we haven't hit head on. There are two countries, imagine you have Country A and you have Country B. Country A says all of you AI engineers, you have to make it explainable. You have to take ethics classes, you have to really think about the consequences and what you're doing. You got to have dinner with biologists, you have to think about love, and you have to like read John Locke, that's Country A. Country B says, just go build some stuff, right? These two countries at some point are going to come in conflict, and I'm going to guess that Country B’s technology might be ahead of Country A’s. Is that a concern?

YNH: Yeah, that's always the concern with arms races, which become a race to the bottom in the name of efficiency and domination. I mean, what is extremely problematic or dangerous about the situation now with AI, is that more and more countries are waking up to the realization that this could be the technology of domination in the 21st century. So you're not talking about just any economic competition between the different textile industries or even between different oil industries, like one country decides to we don't care about the environment at all, we’ll just go full gas ahead and the other countries are much more environmentally aware. The situation with AI is potentially much worse, because it could be really the technology of domination in the 21st century. And those left behind could be dominated, exploited, conquered by those who forge ahead. So nobody wants to stay behind. And I think the only way to prevent this kind of catastrophic arms race to the bottom is greater global cooperation around AI. Now, this sounds utopian because we are now moving in exactly the opposite direction of more and more rivalry and competition. But this is part of, I think, of our job, like with the nuclear arms race, to make people in different countries realize that this is an arms race, that whoever wins, humanity loses. And it's the same with AI. If AI becomes an arms race, then this is extremely bad news for all humans. And it's easy for, say, people in the US to say we are the good guys in this race, you should be cheering for us. But this is becoming more and more difficult in a situation when the motto of the day is America First. How can we trust the USA to be the leader in AI technology, if ultimately it will serve only American interests and American economic and political domination? So I think, most people when they think arms race in AI, they think USA versus China, but there are almost 200 other countries in the world. And most of them are far, far behind. And when they look at what is happening, they are increasingly terrified. And for a very good reason.