How Algorithms can untangle Human Questions. Interview with Brian Christian
“I do think that one of the fascinating outcomes of the progress of AI is that it gives us a new opportunity and new means of understanding the nature of human intelligence — a chance to better know ourselves. That’s a powerful thing, and a good thing.”–Brian Christian
I have interviewed Brian Christian, coauthor of the bestseller book Algorithms to Live By.
RVZ
Q1. You have worked with cognitive scientist Tom Griffiths (professor of psychology and cognitive science at UC Berkeley) to show how algorithms used by computers can also untangle very human questions. What are the main lessons learned from such a joint work?
Brian Christian: I think ultimately there are three sets of insights that come out of the exploration of human decision-making from the perspective of computer science.
The first, quite simply, is that identifying the parallels between the problems we face in everyday life and some of the canonical problems of computer science can give us explicit strategies for real-life situations. So-called “explore/exploit” algorithms tell us when to go to our favorite restaurant and when to try something new; caching algorithms suggest — counterintuitively — that the messy pile of papers on your desk may in fact be the optimal structure for that information.
Second is that even in cases where there is no straightforward algorithm or easy answer, computer science offers us both a vocabulary for making sense of the problem, and strategies — using randomness, relaxing constraints — for making headway even when we can’t guarantee we’ll get the right answer every time.
Lastly and most broadly, computer science offers us a radically different picture of rationality than the one we’re used to seeing in, say, behavioral economics, where humans are portrayed as error-prone and irrational. Computer science shows us that being rational means taking the costs of computation — the costs of decision-making itself — into account. This leads to a much more human, and much more achievable picture of rationality: one that includes making mistakes and taking chances.
Q2. How did you get the idea to write a book that merges computational models with human psychology (*)?
Brian Christian: Tom and I have known each other for 12 years at this point, and I think both of us have been thinking about some of these questions our whole lives. My background is in computer science and philosophy, and my first book The Most Human Human uses my experience as a human “confederate” in the Turing test to ask a series of questions about how computer science is changing our sense of what it means to be human. Tom’s background is in psychology and machine learning, and his research focuses around developing mathematical models of human cognition. The idea of using computer science as a means for insights about human decision-making really emerged naturally as a consequence of those interests and inquiries. One night we were having dinner together and discussing our current projects and, long story short, we realized we were each writing the same book in parallel! It was immediately apparent that it should take the form of a single collaborative effort.
Q3 In your book you explore the idea of human algorithm design. What is it?
Brian Christian: The idea is, quite simply, to look for optimal ways of approaching everyday human decision making — and to do that by identifying the underlying computational structure of the problems we face in daily life.
“Optimal stopping” problems can teach us about when to look and when to leap; the explore/exploit tradeoff tells us when to try new things and when to stick with what we know and love; caching tells us how to manage our space; scheduling theory tells us how to manage our time.
Q4. What are the similarities between the workings of computers and the human mind?
Brian Christian: First I’ll turn that question on its head and highlight one of the biggest differences.
To paraphrase the UNM’s Dave Ackley, the imperative of a computer program is “the precisely correct answer, as prompt as possible,” whereas the imperative of animal cognition (including our own) is the reverse: “a prompt response, as correct as possible.”
As computer scientists explore systems with real-time constraints, and problems sufficiently difficult that grinding out the exact solution (no matter how long it takes) simply doesn’t make sense, we are starting to see that distinction begin to narrow.
Q5. Our lives are constrained by limited space and time, limits that give rise to a particular set of problems. Do computers, too, face the same constraints?
Brian Christian: Computers of course have space constraints — there are limits to how much data can be kept in the caches or the RAM, for instance, which gives rise to caching algorithms. These, in turn, offer us ways of thinking about how we manage the limited space in our own lives (in the book we compare some of Martha Stewart’s edicts about home organization to some of the canonical results in caching theory to see which hold water and which don’t).
Many systems, too, operate under time constraints, which offers us a whole other set of insights we can draw from.
For instance, algorithms for high-frequency trading must determine in a matter of microseconds whether to take an offer or to let it go by — do you hold out for a better price, risking that you may never get as good of a deal ever again?
Many human decisions take this form, across a wide range of domains. This type of “optimal stopping” structure underlies everything from buying and selling houses to our romantic lives. Modern operating systems also include what’s known as a “scheduler,” which determines the best way to make use of the CPU’s limited time. There have been a number of high-profile cases of scheduling failures, including the 1997 Mars Pathfinder mission, where the lander made it all the way to the Martian surface successfully, but then appeared to start procrastinating once it got there: whiling away on low-priority tasks while critical work languished. Studying these failures and the methods for avoiding them can in turn give us strategies for making the most of our own limited time.
Q6. Can computer algorithms help us to have better hunches?
Brian Christian: I think so. For instance, we have a chapter on inductive reasoning that focuses on Bayesian inference. There are some lovely rules of thumb that come out of that. For instance, if you need to predict how long something will last — whether it’s how long a romantic relationship will continue, how long a company will exist, or simply how long it will be before the next bus pulls up — the best you can do, absent any familiarity with the domain, is to assume you’re exactly halfway through, and so it will last exactly as long into the future as it’s lasted already. More broadly, one of the upshots of developing an understanding of Bayesian inference is that if you have experience in a domain, your hunches are likely to be quite good. That’s intuitive enough, but the problem comes when your experiences are not a representative sample of reality.
In the modern world, you can get situations where gun violence in reality is in decline, yet the representation of gun violence in the news is going up. For this reason, it’s probably harder to be a good Bayesian than it’s ever been.
Q7. What will happen if AI system becomes better than humans at most or all cognitive tasks?
Brian Christian: That’s a huge question, and the subject in part of my next book. I think a fundamental restructuring of society is likely to happen — and may be necessary. That’s likely to be a bumpy ride, but it will raise critical and important questions.
And I do think that one of the fascinating outcomes of the progress of AI is that it gives us a new opportunity and new means of understanding the nature of human intelligence — a chance to better know ourselves. That’s a powerful thing, and a good thing.
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Brian Christian is the author of The Most Human Human, a Wall Street Journal bestseller, New York Times editors’ choice, and New Yorker favorite book of the year. His writing has appeared in The New Yorker, The Atlantic, Wired, The Wall Street Journal, The Guardian and The Paris Review, as well as scientific journals such as Cognitive Science, and has been translated into eleven languages. He lives in San Francisco.
Resources
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– Wrestling Knowledge into Computable Intelligence. By Michael K. Bergman. ODBMS.org NOVEMBER 17, 2016
– Machines of Loving Grace. Interview with John Markoff. ODBMS Industry Watch, August 11, 2016
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