Brown CS News

Michael Littman's New Book Recommends That We "Code To Joy" In A New Age Of Programming

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    We’re at a crucial moment, Brown CS University Professor Michael Littman believes, as the users and potentially the programmers of enormously powerful machines. In the face of doomsday artificial intelligence (AI) scenarios, algorithmic bias, and fears of job loss due to automation, he has a simple recommendation: we can get more happiness from our machines by telling them what our hearts desire. It’s the theme of his new book, Code to Joy, which was recently published by The MIT Press.

    “We can look at learning to code in terms of helping us be happier, or more productive,” Michael says, “but fundamentally, I see it as being about empowerment. Whether we’re excited about AI or concerned about it, or both, it’s still up to us to decide what we want to happen and convey that desire to the machine. Traditionally, that’s been done using programming.” 

    Aimed at the layperson, Code to Joy walks the reader through sequencing, conditionals, loops, and more, offering easily comprehensible analogies drawn from daily human interactions. Delivered with Michael’s trademark humor, it’s a toolkit but also a bit of a chemistry set, offering the reader opportunities to immediately experiment with the ideas in each chapter, using publicly available systems. The reader also benefits from Littman’s expertise in machine learning (ML) through reflections on how each chapter’s programming components can be expedited through ML techniques.

    “I want everyone to know,” Michael tells us, “that AI is making programming easier, but it's still something we need to learn to do, in some form, to play an active role in deciding what machines will do for us.”

    Michael currently serves as the National Science Foundation's Division Director for Information and Intelligent Systems. He has earned multiple awards for teaching and research and has served on the editorial boards for The Journal of Machine Learning Research and The Journal of Artificial Intelligence Research. He served as General Chair of the International Conference on Machine Learning and Program Chair of the AAAI Conference in 2013. He's also an AAAI Fellow and is general chair of the Reinforcement Learning and Decision Making Conference, held last year in Providence. He is an ACM Fellow and was an AAAS Leshner Fellow; recently, he received the AIJ Classic Paper Award at the International Joint Conference on Artificial Intelligence and the AAAI/EAAI Patrick Henry Winston Outstanding Educator Award.

    For more information, click the link that follows to contact Brown CS Communications Manager Jesse C. Polhemus.