13 Must-Read Books About Artificial Intelligence for 2020, According to 3 AI Pros | Artificial Intelligence - Built In

Looking for a good book on artificial intelligence? Check out these 13 picks from our panel of AI experts, says Stephen Gossett, tech writer for Built In.

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Artificial intelligence is inescapable. 

It’s correcting your bad grammar and personalizing your music playlists. It’s protecting banks against fraud and appraising real estate. It’s even trouncing the world’s best Jeopardy! players. And the fastest growing job title in America? AI specialist.

13 Recommended AI Books

• The Master Algorithm by Pedro Domingos
• You Look Like a Thing and I Love You by Jenelle Shane
• Inspired by Marty Cagan
• Accelerate: The Science of Lean Software and DevOps by Nicole Forsgren, Jez Humble and Gene Kim
• Technically Wrong by Sara Wachter-Boettcher
• Rebooting AI by Gary Marcus and Ernest Davis
• Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
• Interpretable Machine Learning by Christoph Molar
• How the Mind Works by Steven Pinker
• AI for People and Business by Alex Castrounis
• The Hundred-Page Machine Learning Book by Andriy Burkov
• Machine Learning Yearning by Andrew Ng
• Neural Networks and Deep Learning by Michael Nielson

That degree of ubiquity — not to mention AI’s potential to upend the future of work — means even tech agnostics would benefit from at least a working knowledge of its concepts...

To that end, we asked three AI experts to pick some of their favorite books about artificial intelligence. The panel includes:

Jana Eggers, CEO of Nara Logics, a machine-learning-powered recommendation engine




Garrett Smith, founder of Guild AI, an open-source machine-learning engineering platform



Alex Castrounis, an AI consultant and author of AI for People and Business: A Framework for Better Human Experiences and Business Success



Their selections range from a highly technical consideration of AI’s so-called black box problem to a historical overview of machine learning; from a sober counterpoint to the field’s deep-learning fixation to a thoughtful critique of algorithm bias.
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Source: Built In