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Letter from the Editor

The Thinking Behind the 10 Breakthrough Technologies

Here’s what we look for, and what we hope to avoid.

  • This letter appeared in the March/April 2017 issue.
  • by Jason Pontin
  • February 22, 2017

Every year, MIT Technology Review selects the 10 technologies we believe are the big breakthroughs of the last year: innovations that are a clear advance in their field.

We’ve published a list of the year’s most impactful technologies since 2002. We’re sometimes wrong. We once thought social media and broadcast television would merge (see “Social TV”). But they remain separate streams that people can experience simultaneously, tweeting their impressions of presidential debates as they watch them on TV. More often, we’re not so much wrong as too early: cancer genomics, where doctors sequence the mutations of a patient’s tumor to better match the drugs most likely to help, was less practicable when sequencing was more expensive (see “Cancer Genomics”).

This story is part of our March/April 2017 Issue
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What do we like? We are cross-­disciplinary in our thinking: we enjoy tracing how developments in one field lead to advances in another. A breakthrough in artificial intelligence (see “Deep Learning”) has become crucial to the ambitions for self-driving cars (see “Tesla Autopilot”). We applaud ambitious solutions, such as Google’s plan to bring Internet access to everyone in the world (see “Project Loon”). And we admire elegance and power: we were blown away when scientists used CRISPR to engineer two macaque monkeys, demonstrating the vast potential of gene editing (see “Genome Editing”).

This year’s 10 Breakthrough Technologies reflect the same tastes. “Reinforcement Learning” describes an ambitious approach in AI: computers repeat an action until something difficult goes more smoothly, whereupon the system favors the behavior that led to that outcome. According to senior editor Will Knight, reinforcement learning is an old idea, toyed with by AI pioneers like Marvin Minsky, that never quite worked. But in March 2016, “AlphaGo, a program trained using reinforcement learning, destroyed one of the best Go players of all time … The feat was astonishing, because it is virtually impossible to build a good Go-playing program … Not only is the game extremely complex, but even accomplished players may struggle to say why certain moves are good or bad.” Knight says that reinforcement learning, currently being explored by Uber, OpenAI, and DeepMind, could speed the development of self-driving cars and robots that can reliably grasp objects.

Or consider the cross-disciplinary approach in “Reversing Paralysis”, which combines neuroscience and electronics. Senior editor Antonio Regalado describes how a French neuroscientist named Grégoire Courtine installed a recording device inside the skull of a semi-paralyzed macaque monkey, and then sutured electrodes around the animal’s partially severed spinal cord. “A wireless connection joined the two electronic devices. The result: a system that read the monkey’s intention to move and then transmitted it immediately in the form of bursts of electrical stimulation to its spine.” Suddenly, the monkey’s leg could extend and flex, and it “hobbled forward.” In the past, a few people have controlled robotic arms using brain implants; but by wirelessly connecting brain-reading technologies to electrical stimulators, researchers like Courtine are creating “neural bypasses” that could allow the disabled to walk.

These are just two of the technologies in this year’s list. Read all 10 technologies, and tell me which you think are most remarkable at jason.pontin@technologyreview.com.

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