Making Machines More Human: Author Brian Christian Talks the Alignment Problem

Not many can claim to be a computer programmer, nonfiction author and poet, but Brian Christian has established himself as all three.

Christian has just released his newest book, The Alignment Problem, which delves into the disparity that occurs when AI models don’t do exactly what they’re intended to do.

The book follows on the success of Christian’s previous work, The Most Human Human and Algorithms to Live By. Now a visiting scholar at UC Berkeley, Christian joined AI Podcast host Noah Kravitz to talk about the alignment problem and some new techniques being used to address the issue.

The AI Podcast · Making Machines More Human: Best-Selling Author Brian Christian on the Alignment Problem – Ep. 135

The alignment problem can be caused by a range of reasons — such as data bias, or datasets used incorrectly and out of context. As AI takes on a variety of tasks, from medical diagnostics to parole sentencing decisions, machine learning researchers are expressing concern over the problem.

Listen to the full podcast to hear about this and more — including Christian’s book club experience with Elon Musk and why he chose to double major in philosophy and computer science.

Key Points From This Episode:

The Alignment Problem features insights from hundreds of interviews Christian did with those he calls “first responders” to the ethical and scientific concerns surrounding the issue. He believes this group is evolving into a new interdisciplinary field.
Christian is also director of technology at McSweeney’s Publishing and scientific communicator in residence at Simon’s Institute for the Theory of Computing. He talks to Kravitz about how he managed to combine his love for both computer science and creative writing in his current career.

Tweetables:

“Philosophy and computer science are really on a collision course.” — Brian Christian [20:23]

“This new interdisciplinary field, thinking about … how exactly are we going to get human norms into these ML systems.” — Brian Christian [26:25]

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