Brian Christian – How to Live with Computers

**Patrick O'Shaughnessy** (0:04)
Hello and welcome everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, methods, stories, and of strategies that will help you better invest both your time and your money.

**SPEAKER_1** (0:17)
You can learn more and stay up to date at investorfieldguide.com Patrick O'Shaughnessy is the CEO of O'Shaughnessy Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of O'Shaughnessy Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.
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**Patrick O'Shaughnessy** (0:49)
My guest this week is Brian Christian, the author of two of my favorite recent books, Algorithms to Live By and The Most Human Human. Our conversation covers the present and future of how humans interact with and use computers. Brian's thoughts on the nature of intelligence and what it means to be human continue to make me think about what work and life will be like in the future. I hope you enjoy our conversation.
I'd love to begin with some framing from you around just what your general interest is that unites the first two books that you've written and published and the third that you're working on now. How would you sum up your set of interests?

**Brian Christian** (1:22)
I think I have this very on paper, eclectic academic background where in college, I studied computer science and philosophy and then I went to graduate school for creative writing.
And I remember at the time, raising a lot of eyebrows at family reunions and so forth, people saying, so you're studying computer science, this very precise exacting engineering discipline, and philosophy, like this abstract, vague field. How do these two things connect? Increasingly, people don't ask me that question and I think we're perhaps just coincidentally living through the great synthesis of computer science and philosophy, that as the sort of computational metaphor for mind becomes increasingly literal, we've developed these neural networks, for example, starting in the 1940s, through this analogy to the way that the nervous system worked and are now discovering in the last 10 years that this is the best mechanism that we know of for actually implementing AI, which maybe shouldn't surprise us that we're rediscovering what evolution found after millions of years of trial and error. Computation gives us a way of asking questions about what does it mean to think, what does it mean to have a mind, what does it mean to make rational decisions, and I found, as I was studying these two things in undergraduate, the questions that I was interested in asking in philosophy about the mind were in some ways better answered through the tools and the vocabulary of computer science than they were in philosophy itself. So that's the sort of collision of those two fields has been kind of the overriding interest of my whole professional career.

**Patrick O'Shaughnessy** (2:59)
What do you think the biggest open questions are today in AI and computer science?

**Brian Christian** (3:04)
I think it's clear that we're on the road to building AGI, and the open question that I think is on the minds of many of the people in the technical research community is how many significant breakthroughs away are we? I was just at an AI conference a few weekends ago and we were talking about this and I said, I think we're zero to two major breakthroughs away from AGI. And that got a general consensus of hmm and nodding. And I think that's in some ways the holy grail. That's the biggest question hanging over the field.
So I think that is going to be one of the great things that we'll learn in the next couple decades.

**Patrick O'Shaughnessy** (3:43)
We're gonna talk about the most human human in some detail in your own kind of journey, which was so interesting to define sort of what it is that makes us human, that line, that goal keeps moving, the goalpost keeps moving. But can you define AGI for people listening?
What sort of the history of that idea is? What is like the equivalent Turing test? Is it still the Turing test that would lead us to believe we've achieved this? And why might the answer be zero?

**Brian Christian** (4:05)
Right, okay, so AGI is artificial general intelligence, which is a fairly recent term, but the underlying idea has been around for a long time. And basically means a system that is human level competent or beyond at not just a specific thing that humans do, but basically everything that humans do. So we've long had systems that could outperform humans at arithmetic since the 40s, and then things like chess since the 1990s. And we're seeing these particular domain specific milestones kind of fall into the rear view mirror, more recently Go, for example. But there's still this question of the kind of flexible general intelligence where you can ask someone to write you a poem, you can ask them to add some figures, you can ask them to ruminate on their childhood. What do we need in order to build a system capable of competence with that degree of flexibility? So that's the question of AGI. And I think the Turing test, I mean, Alan Turing was, of course, one of the great founding fathers of computer science and was amazingly prescient in thinking about some of these philosophical questions decades before other people were thinking about it. Already by 1950, he writes this famous paper called Computing Machinery and Intelligence. And the opening line is, can machines think? Of course, you have to remember, this was back in the time when the computer as we know it was this room-sized thing, processing punch cards. But he was already asking these philosophical questions. He was kind of seeing the long view ahead.