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In the former category some of the most practical advances have been made in relation to speech.

Voice recognition is still far from perfect, but millions of people are now using it — think Siri, Alexa, and Google Assistant.

The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning.

The bottleneck now is in management, implementation, and business imagination.

Each one catalyzed waves of complementary innovations and opportunities.

The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs.

We can now build systems that learn how to perform tasks on their own. Prior to ML, this inability to articulate our own knowledge meant that we couldn’t automate many tasks. Excellent digital learners are being deployed across the economy, and their impact will be profound.In the sphere of business, AI is poised have a transformational impact, on the scale of earlier general-purpose technologies.Although it is already in use in thousands of companies around the world, most big opportunities have not yet been tapped.(It took 40.) In 1967 the cognitive scientist Marvin Minsky said, “Within a generation the problem of creating ‘artificial intelligence’ will be substantially solved.” Simon and Minsky were both intellectual giants, but they erred badly.Thus it’s understandable that dramatic claims about future breakthroughs meet with a certain amount of skepticism.Erik Brynjolfsson (@erikbryn) is the director of MIT’s Initiative on the Digital Economy, the Schussel Family Professor of Management Science at the MIT Sloan School of Management, and a research associate at NBER.His research examines the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets.The text you are now reading was originally dictated to a computer and transcribed with sufficient accuracy to make it faster than typing.A study by the Stanford computer scientist James Landay and colleagues found that speech recognition is now about three times as fast, on average, as typing on a cell phone. What’s striking is that this substantial improvement has come not over the past 10 years but just since the summer of 2016. You may have noticed that Facebook and other apps now recognize many of your friends’ faces in posted photos and prompt you to tag them with their names.Vision systems, such as those used in self-driving cars, formerly made a mistake when identifying a pedestrian as often as once in 30 frames (the cameras in these systems record about 30 frames a second); now they err less often than once in 30 million frames.The error rate for recognizing images from a large database called Image Net, with several million photographs of common, obscure, or downright weird images, fell from higher than 30% in 2010 to about 4% in 2016 for the best systems. ”) The speed of improvement has accelerated rapidly in recent years as a new approach, based on very large or “deep” neural nets, was adopted.

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