Issei Kato / REUTERS A humanoid robot works alongside an employee on an assembly line in Kazo, Japan, July 2015.

Human Work in the Robotic Future

Policy for the Age of Automation

Purchase Article
Purchase Audio

The promises of science fiction are quickly becoming workaday realities. Cars and trucks are starting to drive themselves in normal traffic. Machines have begun to understand our speech, figure out what we want, and satisfy our requests. They have learned to write clean prose, generate novel scientific hypotheses (that are supported by later research), compose evocative music, and beat us, quite literally, at our own games: chess, poker, and even go.

This technological surge is just getting started, and there’s much more to come. For one thing, the fundamental building blocks that launched it will continue to improve rapidly. The costs of processing, memory, bandwidth, sensors, and storage continue to fall exponentially. Cloud computing will make all these resources available on demand across the world. Digital data will become only more pervasive, letting us run experiments, test theories, and learn at an ever-greater scale. And the billions of humans around the world are growing increasingly connected; they’re not only tapping into the world’s knowledge (much of which is available for free) but also expanding and remixing it. This means that the global population of innovators, entrepreneurs, and geeks is growing quickly and, with it, the potential for breakthroughs.

Most important, humanity has recently become much better at build­ing machines that can figure things out on their own. By studying lots of examples, identifying relevant patterns, and applying them to new examples, computers have been able to achieve human and super­human levels of performance in a range of tasks: recognizing street signs, parsing human speech, identifying credit fraud, modeling how materials will behave under different conditions, and more.

Building machines that can learn on their own is critical, because when it comes to accomplishing many tasks, we humans “know more than we can tell,” as the scientist and philosopher Michael Polanyi put it. Historically, this served as a hard barrier to digitizing much work: after all, if no human could explain all the steps followed when completing

Browse Related Articles on {{search_model.selectedTerm.name}}

{{indexVM.results.hits.total | number}} Articles Found

  • {{bucket.key_as_string}}