Reid riffs on his personal history with AI

Reid reflects on his early exposure to AI as an undergraduate at Stanford and on navigating the space between academia and industry. Plus, he and Aria discuss human outcomes of technology—and how human-centered technologists such as Fei-Fei Li, Mustafa Suleyman, and Kevin Scott have helped him keep those outcomes top of mind. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Reid Hoffman?

  I'm Reid Hoffman.

• 0:01Aria Finger?

  And I'm Aria Finkler.

• 0:03Reid Hoffman?

  We want to know what happens if, in the future, everything breaks humanity's way.

• 0:08Aria Finger?

  Typically, we ask our guests for their outlook on the best possible future. But now, every other week, I get to ask Reid for his take.

• 0:17Reid Hoffman?

  This is possible.

• 0:20Aria Finger?

  Reid, you opened our conversation with Daphne Koller last week, talking about the early days of AI. We all learned from your speech at Perugia that you first got inspired by AI by reading Gödel, Escher, Bach in high school, and again, while at Stanford, talking with Professor John McCarthy, who actually coined the term artificial intelligence. And of course, since then, you've been investing and building in the AI space. So like, tell us more about your early history with AI and what you remember from those days.

• 0:51Reid Hoffman?

  Obviously, in each technological age, each technological area of invention, there's a new concept of human intelligence. So like, when we were doing fluid mechanics, it's the brain works through fluids, you know, and electricity, it's all electrical. And of course, with computing, it's all computation. And that's, of course, what's kicked off the modern field of artificial intelligence. And then when I was an undergraduate at Stanford, what I got into, this major called Simbolic Systems and AI, is because it's like, thinking about what is it that makes something intelligent and sentient? How do we understand ourselves, the world, our relationship with each other? I kind of started at the very early days, getting into AI with that as an interest.

• 1:36Reid Hoffman?

  And one of the things that's particularly historically poignant is my, you know, key advisor at Stanford was a professor named David Rummelhart, who was one of the earliest co-authors and proponents of PDP, parallel distributed processing, and doing these, you know, along with Jeff Hinton and some other folks. And so this modern approach to AI, something that I was experiencing as, you know, a sophomore at Stanford. Now, part of what I concluded at Stanford was that we were actually, in fact, very far away still from understanding intelligence, from being able to build AI devices. What got me back into it?

• 2:19Reid Hoffman?

  Was this kind of early work from the DeepMind folks, Demis Hassabis, Mustafa Suleyman, Shane Legg, where they started understanding how you could apply scale compute, not just, you know, because there is evolution and change in the algorithms, but the algorithms were already had been baseline created, but creating scale compute. Now, the first things that these DeepMind folks did was to say, hey, we can do self-play of computer games. And with that self-play of computer games, we can create, you know, kind of AlphaZero and kind of equivalent. And that allows you to suddenly apply scale

• 3:01Reid Hoffman?

  compute in ways it hadn't done before. And then part of how that continued to evolve with what OpenAI was doing with the transformer and large language models is say, look, let's just apply scale learning algorithms through the transformer, through convolutional neural networks, through human feedback learnings on a scale amount of data, and we can make something that's super important. And that's the kind of thing where I said, ah, actually, now we're here, you know, we're just at the very beginning of this. Because obviously, some people have had some ability to kind of play with these large language models and see what kind of magic they can do in this kind of interaction. But our application

• 3:41Reid Hoffman?

  to what it means in, you know, very serious things, medicine, you know, as we were talking about drug discoveries, we're talking about with Daphne. But a whole stack of things, we're just at that precipice of, and it obviously is part of what's kicked us off here through this. But the early days was heady and similar discussion, but they hadn't realized scale compute data, et cetera, yet.

• 4:08Aria Finger?

  And we also spoke with Daphne about navigating the space between academia and industry, like between the conceptual, but then how do you get the concrete technology? And I love the story she shared about her postdoc at Berkeley, Stuart Russell, who challenged her to think about applications of her very conceptual thesis by asking, like, what would you do with this if you had a brilliant team at your disposal? And so I will, I will turn to you, Reid, with the same question. Who is someone who has helped you pause and think about human outcomes of technology that you're excited about?

• 4:40Reid Hoffman?

  To kind of answer a few in

• 4:44Reid Hoffman?

  This arena, Fei-Fei Li and the Stanford, you know, high, you know, human centered AI institute. You know, I think Fei-Fei was one of the earliest people to start really clearly articulating. Look, it's not just about kind of like the creation of a robot, the creation of AI. It's human centered AI. So Fei-Fei is definitely, you know, kind of one in this arena. Mustafa Suleyman, my co-founder for Inflection is another because part of when he was at DeepMind, part of how we ended up going deep, pun mildly intended, on a set of things was not only do we need to get the function of governance correct, e.g. how is it that it's good for a broad range of human beings, that it isn't, you know, an instrument of a, you know, totalitarian

• 5:29Reid Hoffman?

  regime or totalitarian, you know, monopolistic company, but how do you do it? But it's also the process by which you get there is important to your moral legitimacy in various ways. So, you know, Mustafa and I've had a huge number of conversations on that. And then, you know, maybe, you know, maybe talking about Kevin Scott, who, you know, was the CTO and VP of Engineering that helped us take LinkedIn public and then is now the CTO of Microsoft. And, you know, part of the thing that, you know, Kevin's specific move, which was his excellent book, here is how AI can help kind of people in, you know, kind of rural America,

• 6:10Reid Hoffman?

  reinvent their economies, reinvent their economic prospects and can be really good for them. And it's part of the thing that from his own, you know, kind of background, you know, he speaks very authoritatively about was it kind of got me to the generalization of, you know, all technology and including AI, whenever you see a problem, see if you can use technology and for example, you see a problem with AI, can you use AI to be part of the solution? So those are three, but I could probably go on for an hour listing additional people. Right.

• 6:44Aria Finger?

  So Reid, you know, you have done so much deep thinking about the ways that AI can benefit humanity, but still a lot of people are nervous about AI in general. And yet I think one of the places where we can see so much hope is actually in the drug discovery field. So how do you think we can most effectively communicate these advancements in AI and biomedicine to fill people with hope about this area for AI?

• 7:10Reid Hoffman?

  The short answer, and part of, of course, the reason you and I are doing Possible and the podcast is to say, let's figure out what are the really good things, the things we should be rowing to, the things to go, that's really exciting. Because like, if you say, for example, our conversation with Daphne and say, hey, there's new drugs that can be created, possible, you know, new therapeutic hypotheses for oncology and other kinds of things. And that might lead to something really different. People go, well, that's good. And, you know, her specific, you know, wave a wand thing, which is something I also have agreed with, which is, can we get people saying, yes, I would love to contribute my data to help us solve collective problem? Because one of the problems, things that most people encounter when they're like, I am suffering with disease X, or genetic condition Y, or something else.

• 7:54Reid Hoffman?

  It's like, if you can learn something from my condition and my suffering to help other people, it adds meaning to what I'm doing. And so, most people actually, in fact, really want to do that. And so, enable it, make it happen. This is the kind of like, but people say, oh, collective data could be really bad. Something bad could happen. It's like, yeah, but by the way, something good, like curing cancer could happen, right? And that's really important. And so, the fact that there might be one isolated footfall, or fumble, or even, you know, fender bender, or car crash, doesn't mean we should close the highway, right? I mean, we should, we should continue to do that. And so, I think you communicate by going, here is what could be great. Here's what could be magical. And then, it doesn't

• 8:39Reid Hoffman?

  mean critics should, should stop speaking, but it means that they should shape their criticism with a thought about, look, it's really important to get down the highway. It's really important to get to the drug discovery. It's really important to get to the cancer fixing. That getting there is what's most important. How do I contribute to our navigating risks and negativity while we're doing it? Versus going, you know, banging pots and pans together, going, danger, Will Robinson. And I think that's the, that's, that's at least a beginning to share the hope that we see and, and make that more public and more common.

• 9:19Aria Finger?

  Yeah, and you would hope, especially on the sort of data front and drug discovery, that besides the polarization, et cetera, that, you know, the COVID vaccine should be something that everyone can say, oh, wow, that was an incredible drug discovery that we got because of years of research. That was an incredible cooperation between governments, private industry, academia. Like, these are things that we need for the common good, and that at least benefited, you know, millions, billions of people around the world. So, Reid, thank you so much. Appreciate it.

• 9:51Reid Hoffman?

  Awesome.

• 9:56Reid Hoffman?

  Possible is produced by Wondery Media Network. It's hosted by Ari Finger and me, Reid Hoffman. Our showrunner is Sean Young. Possible is produced by Katie Sanders, Edie Aller, Sarah Schlied, Adrian Bain, and Paloma Moreno Jimenez. Jenny Kaplan is our executive producer and editor.

• 10:14Aria Finger?

  Special thanks to Surya Yalamanchili, Sayida Sepieva, Ian Ellis, Greg Beato, Ben Relis, Parth Patel, and Little Monster Media Company.