Artificial Intelligence, For Real

Publication Date: August 07, 2017

“General-purpose technologies,” such as the internal combustion engine, have been the fundamental drivers of economic growth for 250 years. Artificial intelligence–particularly machine learning (ML)–is the most important such technology of our era. In the coming decade practically every industry will transform its key processes and business models to take advantage of ML. But not all expectations surrounding AI are realistic. In “The Business of Artificial Intelligence,” Erik Brynjolfsson and Andrew McAfee describe its real potential, its practical implications, and the barriers to its adoption. They note that AI skills are spreading quickly, through online educational resources as well as universities; the necessary algorithms and hardware for modern AI can be bought or rented as needed; and companies may not need all that much data to start making productive use of ML. But, they say, machines may have hidden biases, derived from the data used to train them; neural networks deal with statistical rather than literal truths; and diagnosing and correcting system errors is often difficult, because a solution’s underlying structure can be unimaginably complex. The story of Facebook’s Applied Machine Learning group unfolds in “Inside Facebook’s AI Workshop,” by Scott Berinato, and Walter Frick explains AI’s current limitations with prose in “Why AI Can’t Write This Article (Yet).” To artificial intelligence, humans are nothing more than rows in a spreadsheet, writes Kurt Gray in “AI Can Be a Troublesome Teammate.” Hilary Mason explains the capabilities needed to manage a company’s data science in “How AI Fits into Your Data Science Team.” This month’s Big Idea also includes a webinar with Andrew Ng, “Deep Learning’s Next Frontier,” and the video, “Artificial Intelligence, Real Food,” in which IBM’s Watson assists two chefs (one a famous professional and the other an amateur) in preparing food.

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Artificial Intelligence, For Real

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