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MIT Technology Review

Visionaries (2015)

These people are showing how technologies will give us new ways of doing things.
  • Lars Blackmore

    Age:
    34

    Would space travel flourish if we could reuse the rockets?

    Sixty years after Sputnik blasted into space, escaping our atmosphere remains absurdly expensive. Lars Blackmore, an engineer at SpaceX, is working on changing that with rockets that could be flown back to Earth in reverse.

    As things stand, every time a space rocket takes off and releases its payload, it breaks up and falls into the ocean. “It’s basically like flying a 747 across the country and then, instead of refueling it, throwing it away,” says Blackmore, a soft-spoken Brit who leads a team at SpaceX that’s developing the onboard software necessary for a rocket to come down gently in an upright position onto a platform in the ocean.

    SpaceX has come agonizingly close to sticking a rocket landing several times, but it didn’t get a chance to try again in its most recent flight, when the Falcon 9 rocket exploded during takeoff.

    Landing a rocket backwards is an insane trick. The descent is extraordinarily unpredictable, and rockets aren’t meant to travel in reverse, so it requires extremely fine control over the boosters and guidance fins. Blackmore has devised algorithms to enable a rocket’s onboard computer to deal with this chaotic situation while safely controlling the craft’s fall.

    If the feat can be perfected, it would change the economics of space travel entirely. Fuel accounts for less than half of 1 percent of the cost of a rocket launch, so refurbishing a rocket would make the next launch considerably cheaper. How much cheaper would depend on how well the booster could be reconditioned following the extreme stress of takeoff.

    Blackmore grew up dreaming of working at NASA Mission Control. After a PhD at MIT, he joined NASA’s Jet Propulsion Lab, where he worked on precision landing systems and a climate probe called SMAP. He went to SpaceX in 2011. “I’d heard that Elon [Musk] had these dreams of making reusable rockets,” Blackmore says. “And since I was working on precision landing for Mars, I thought I would be the right guy to do that.”

    Would he want to go back to NASA someday? “When you hear about the Apollo program in its heyday, it was a bunch of young kids, and no one told them what they could do,” he says. “That is exactly what I’ve found at SpaceX.”

    Will Knight

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  • Adam Coates

    Age:
    33

    Artificial intelligence could make the Internet more useful to the millions of people coming online for the first time.

    Q: You invented ways to put more computing power behind deep learning. Now you lead a lab in Silicon Valley for the Chinese search company Baidu. Why did it need a lab there?

    A: They spin up new projects very fast. It’s partly driven by the dynamism in Chinatech companies have to go quickly from having nothing to having state-of-the-art something. My lab’s mission is to create technology that will have an impact on at least 100 million people; it is intended to move rapidly, like a startup. We’re recruiting AI researchers and many people in Silicon Valley who have amazing skills from working on products and haven’t thought they could use that to make progress on artificial intelligence.

    Q: What is the lab working on?

    A: The first technology that we are focusing on is speech recognition. Touch screens on phones are fine for some things but really awful for others, and there are all kinds of other devices that are crying out for better interfaces. People don’t use speech today because it doesn’t work well enough. Our goal is to get it to a level where it’s as easy to talk to your devices as it is to talk to the person next to you. In December we hit our first milestone with DeepSpeech, a speech engine we built quickly from scratch using deep learning. When there’s a lot of background noise it’s dramatically better.

    Q: Why would that have an impact on 100 million people?

    A: In rapidly developing economies like in China, there are many people who will be connecting to the Internet for the first time through a mobile phone. Having a way to interact with a device or get the answer to a question as easily as talking to a person is even more powerful to them. I think of Baidu’s customers as having a greater need for artificial intelligence than myself.

    —Tom Simonite

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  • Zakir Durumeric

    Age:
    26

    A computer scientist sees a way to improve online security.

    “It’s absolutely astounding what people attach to the Internet,” Zakir Durumeric says. He would know, because he invented a way to probe every computer online in just minutes. “We have found everything from ATM machines and bank safes to industrial control systems for power plants,” he says. “It’s kind of scary.”

    A bank safe! Why would someone put that online? So someone in the bank can operate it from home?

    “Yes. You sit there and you wonder: who on earth thought this was a good idea?”

    Bad computer security practices like that can be mitigated far more readily with the ZMap scanning system -Durumeric developed. It determines not only which machines are online at any given moment, but also whether they have security flaws that should be fixed before miscreants exploit them. It finds everything from obvious software bugs to subtle problems like the ones that can be caused if an IT administrator fails to properly implement an arcane aspect of a cryptography standard.

    Pinging all four billion devices on the Internet took weeks until Durumeric, who is pursuing a PhD at the University of Michigan, came up with a process that now takes about five minutes. He has used it to quickly inform website administrators about their vulnerability to catastrophic flaws such as the Heartbleed bug in 2014, and he hopes other security researchers will routinely do the same when they find weaknesses. “There’s always been this period where a vulnerability is [found] and then it takes weeks, months, or years for administrators to patch their servers,” he says. “We have an opportunity to change that.”

    —Brian Bergstein

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  • Cigall Kadoch

    Age:
    30

    A major vulnerability of certain kinds of cancer is becoming clear.

    Problem:
    The exact biochemical mechanisms involved in many kinds of cancer remain unknown.

    Solution:
    While completing her PhD at Stanford, Cigall Kadoch discovered a link between a genome regulator in cells called the BAF protein complex and a rare cancer called synovial sarcoma. She and colleagues later showed that mutations of BAF are involved in at least 20 percent of human cancers, opening the door for research on drugs that target mutated BAFs.

    BAF’s job in the cell is to open and close DNA to allow the right genes to be expressed at the right time. When mutated, it can “activate sites that it shouldn’t”including genes that drive cancer, says Kadoch,who has appointments at Harvard Medical School and the Broad Institute of Harvard and MIT.

    She learned this by focusing on one particular subunit of BAF. This piece of the protein has a deformed tail in 100 percent of patients with synovial sarcoma. When Kadoch put the deformed subunit into normal cells, she detected “blazing cancer,” she says. “That little tail is entirely responsible for this cancer.”

    The good news is that this is reversible. If she added enough normal pieces of the subunit to cells in a petri dish, it replaced the mutated form, killing the cancerous cells on the spot.

    —Anna Nowogrodzki

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  • Ilya Sutskever

    Age:
    29

    Why one form of machine learning will be particularly powerful.

    Artificial-intelligence researchers are focusing on a method called deep learning, which gets computers to recognize patterns in data on their own (see “Teaching Machines to Understand Us”). One person who demonstrated its potential is Ilya Sutskever, who trained under a deep-learning pioneer at the University of Toronto and used the technique to win an image-recognition challenge in 2012. He is now a key member of the Google Brain research team. I asked him why deep learning could mimic human vision and solve many other challenges.

    “When you look at something, you know what it is in a fraction of a second,” he says. “And yet our neurons operate extremely slowly. That means your brain must only need a modest number of parallel computations. An artificial neural network is nothing but a sequence of very parallel, simple computations.

    “We started a company to keep applying this approach to different problems and expand its range of capabilities. Soon, we joined Google. I’ve shown that the same philosophy that worked for image recognition can also achieve really good results for translation between languages. It should beat existing translation technology by a good margin. I think you will see deep learning make a lot of progress in many areas. It doesn’t make any assumptions about the nature of problems, so it is applicable to many things.”

    —Tom Simonite