In this episode, Byron and Oren talk about AGI, Aristo, the future of work, conscious machines, and Alexa.
Oren Etzioni is a professor of Computer Science and CEO of the Allen Institue for Artificial Intelligence. He is also a venture partner at the Madrona Venture Group.
Byron Reese: This is Voices in AI, brought to you by Gigaom. I’m Byron Reese. Today, our guest is Oren Etzioni. He’s a professor of computer science who founded and ran University of Washington’s Turing Center. And since 2013, he’s been the CEO of the Allen Institute for Artificial Intelligence. The Institute investigates problems in data mining, natural language processing, and the semantic web. And if all of that weren’t enough to keep a person busy, he’s also a venture partner at the Madrona Venture Group. Business Insider called him, quote: “The most successful entrepreneur you’ve never heard of.”
Welcome to the show, Oren.
Oren Etzioni: Thank you, and thanks for the kind introduction. I think the key emphasis there would be, “you’ve never heard of.”
Well, I’ve heard of you, and I’ve followed your work and the Allen Institute’s as well. And let’s start there. You’re doing some fascinating things. So if you would just start off by telling us a bit about the Allen Institute, and then I would love to go through the four projects that you feature prominently on the website. And just talk about each one; they’re all really interesting.
Well, thanks. I’d love to. The Allen Institute for AI is really Paul Allen’s brainchild. He’s had a passion for AI for decades, and he’s founded a series of institutes—scientific institutes—in Seattle, which were modeled after the Allen Institute for Brain Science, which has been very successful running since 2003. We were founded—got started—in 2013. We were launched as a nonprofit on January 1, 2014, and it’s a great honor to serve as CEO. Our mission is AI for the common good, and as you mentioned, we have four projects that I’m really excited about.
Our first project is the Aristo project, and that’s about building a computer program that’s able to answer science questions of the sort that we would ask a fourth grader, and now we’re also working with eighth-grade science. And people sometimes ask me, “Well, gosh, why do you want to do that? Are you trying to put 10-year-olds out of work?” And the answer is, of course not.
We really want to use that test—science test questions—as a benchmark for how well are we doing in intelligence, right? We see tremendous success in computer programs like AlphaGo, beating the world champion in Go. And we say, “Well, how does that translate to language—and particularly to understanding language—and understanding diagrams, understanding science?”
And one way to answer that question is to, kind of, level the playing field with, “Let’s ask machines and people the same questions.” And so we started with these science tests, and we can see that, in fact, people do much better. It turns out, paradoxically, that things that are relatively easy for people are really quite hard for machines, and things that are hard for people—like playing Go at world championship level—those are actually relatively easy for the machine.
Hold on there a minute: I want to take a moment and really dissect this. Any time there’s a candidate chatbot that can make a go at the Turing test, I have a standard question that I start with, and none of them have ever answered it correctly.
It’s a question a four-year-old could answer, which is, “Which is bigger, a nickel or the sun?” So why is that a hard problem? Is what you’re doing, would it be able to answer that? And why would you start with a fourth grader instead of a four-year-old, like really go back to the most basic, basic questions? So the first part of that is: Is what you’re doing, would it be able to answer the question?