Tag Archives: review
China Wants to Shape the Global Future of Artificial Intelligence
Will Knight | MIT Technology Review
“China’s booming AI industry and massive government investment in the technology have raised fears in the US and elsewhere that the nation will overtake international rivals in a fundamentally important technology. In truth, it may be possible for both the US and the Chinese economies to benefit from AI. But there may be more rivalry when it comes to influencing the spread of the technology worldwide. ‘I think this is the first technology area where China has a real chance to set the rules of the game,’ says Ding.”
Astronaut’s Gene Expression No Longer Same as His Identical Twin, NASA Finds
Susan Scutti | CNN
“Preliminary results from NASA’s Twins Study reveal that 7% of astronaut Scott Kelly’s genetic expression—how his genes function within cells—did not return to baseline after his return to Earth two years ago. The study looks at what happened to Kelly before, during and after he spent one year aboard the International Space Station through an extensive comparison with his identical twin, Mark, who remained on Earth.”
This Cheap 3D-Printed Home Is a Start for the 1 Billion Who Lack Shelter
Tamara Warren | The Verge
“ICON has developed a method for printing a single-story 650-square-foot house out of cement in only 12 to 24 hours, a fraction of the time it takes for new construction. If all goes according to plan, a community made up of about 100 homes will be constructed for residents in El Salvador next year. The company has partnered with New Story, a nonprofit that is vested in international housing solutions. ‘We have been building homes for communities in Haiti, El Salvador, and Bolivia,’ Alexandria Lafci, co-founder of New Story, tells The Verge.”
Our Microbiomes Are Making Scientists Question What It Means to Be Human
Rebecca Flowers | Motherboard
“Studies in genetics and Watson and Crick’s discovery of DNA gave more credence to the idea of individuality. But as scientists learn more about the microbiome, the idea of humans as a singular organism is being reconsidered: ‘There is now overwhelming evidence that normal development as well as the maintenance of the organism depend on the microorganisms…that we harbor,’ they state (others have taken this position, too).”
Stephen Hawking, Who Awed Both Scientists and the Public, Dies
Joe Palca | NPR
“Hawking was probably the best-known scientist in the world. He was a theoretical physicist whose early work on black holes transformed how scientists think about the nature of the universe. But his fame wasn’t just a result of his research. Hawking, who had a debilitating neurological disease that made it impossible for him to move his limbs or speak, was also a popular public figure and best-selling author. There was even a biopic about his life, The Theory of Everything, that won an Oscar for the actor, Eddie Redmayne, who portrayed Hawking.”
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The tech industry touts its ability to automate tasks and remove slow and expensive humans from the equation. But in the background, a lot of the legwork training machine learning systems, solving problems software can’t, and cleaning up its mistakes is still done by people.
This was highlighted recently when Expensify, which promises to automatically scan photos of receipts to extract data for expense reports, was criticized for sending customers’ personally identifiable receipts to workers on Amazon’s Mechanical Turk (MTurk) crowdsourcing platform.
The company uses text analysis software to read the receipts, but if the automated system falls down then the images are passed to a human for review. While entrusting this job to random workers on MTurk was maybe not so wise—and the company quickly stopped after the furor—the incident brought to light that this kind of human safety net behind AI-powered services is actually very common.
As Wired notes, similar services like Ibotta and Receipt Hog that collect receipt information for marketing purposes also use crowdsourced workers. In a similar vein, while most users might assume their Facebook newsfeed is governed by faceless algorithms, the company has been ramping up the number of human moderators it employs to catch objectionable content that slips through the net, as has YouTube. Twitter also has thousands of human overseers.
Humans aren’t always witting contributors either. The old text-based reCAPTCHA problems Google used to use to distinguish humans from machines was actually simultaneously helping the company digitize books by getting humans to interpret hard-to-read text.
“Every product that uses AI also uses people,” Jeffrey Bigham, a crowdsourcing expert at Carnegie Mellon University, told Wired. “I wouldn’t even say it’s a backstop so much as a core part of the process.”
Some companies are not shy about their use of crowdsourced workers. Startup Eloquent Labs wants to insert them between customer service chatbots and human agents who step in when the machines fail. Many times the AI is pretty certain what particular work means, and an MTurk worker can step in and quickly classify them faster and cheaper than a service agent.
Fashion retailer Gilt provides “pre-emptive shipping,” which uses data analytics to predict what people will buy to get products to them faster. The company uses MTurk workers to provide subjective critiques of clothing that feed into their models.
MTurk isn’t the only player. Companies like Cloudfactory and Crowdflower provide crowdsourced human manpower tailored to particular niches, and some companies prefer to maintain their own communities of workers. Unlabel uses an army of 50,000 humans to check and edit the translations its artificial intelligence system produces for customers.
Most of the time these human workers aren’t just filling in the gaps, they’re also helping to train the machine learning component of these companies’ services by providing new examples of how to solve problems. Other times humans aren’t used “in-the-loop” with AI systems, but to prepare data sets they can learn from by labeling images, text, or audio.
It’s even possible to use crowdsourced workers to carry out tasks typically tackled by machine learning, such as large-scale image analysis and forecasting.
Zooniverse gets citizen scientists to classify images of distant galaxies or videos of animals to help academics analyze large data sets too complex for computers. Almanis creates forecasts on everything from economics to politics with impressive accuracy by giving those who sign up to the website incentives for backing the correct answer to a question. Researchers have used MTurkers to power a chatbot, and there’s even a toolkit for building algorithms to control this human intelligence called TurKit.
So what does this prominent role for humans in AI services mean? Firstly, it suggests that many tools people assume are powered by AI may in fact be relying on humans. This has obvious privacy implications, as the Expensify story highlighted, but should also raise concerns about whether customers are really getting what they pay for.
One example of this is IBM’s Watson for oncology, which is marketed as a data-driven AI system for providing cancer treatment recommendations. But an investigation by STAT highlighted that it’s actually largely driven by recommendations from a handful of (admittedly highly skilled) doctors at Memorial Sloan Kettering Cancer Center in New York.
Secondly, humans intervening in AI-run processes also suggests AI is still largely helpless without us, which is somewhat comforting to know among all the doomsday predictions of AI destroying jobs. At the same time, though, much of this crowdsourced work is monotonous, poorly paid, and isolating.
As machines trained by human workers get better at all kinds of tasks, this kind of piecemeal work filling in the increasingly small gaps in their capabilities may get more common. While tech companies often talk about AI augmenting human intelligence, for many it may actually end up being the other way around.
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Can A.I. Be Taught to Explain Itself?Cliff Kuang | New York Times“Kosinski’s results suggested something stranger: that artificial intelligences often excel by developing whole new ways of seeing, or even thinking, that are inscrutable to us. It’s a more profound version of what’s often called the ‘black box’ problem—the inability to discern exactly what machines are doing when they’re teaching themselves novel skills—and it has become a central concern in artificial-intelligence research.”
Semi-Synthetic Life Form Now Fully Armed and OperationalAntonio Regalado | MIT Technology Review “By this year, the team had devised a more stable bacterium. But it wasn’t enough to endow the germ with a partly alien code—it needed to use that code to make a partly alien protein. That’s what Romesberg’s team, reporting today in the journal Nature, says it has done.”
4 Strange New Ways to ComputeSamuel K. Moore | IEEE Spectrum “With Moore’s Law slowing, engineers have been taking a cold hard look at what will keep computing going when it’s gone…What follows includes a taste of both the strange and the potentially impactful.”
Google X and the Science of Radical CreativityDerek Thompson | The Atlantic “But what X is attempting is nonetheless audacious. It is investing in both invention and innovation. Its founders hope to demystify and routinize the entire process of making a technological breakthrough—to nurture each moonshot, from question to idea to discovery to product—and, in so doing, to write an operator’s manual for radical creativity.”
PRIVACY AND SECURITY
Uber Paid Hackers to Delete Stolen Data on 57 Million PeopleEric Newcomer | Bloomberg “Hackers stole the personal data of 57 million customers and drivers from Uber Technologies Inc., a massive breach that the company concealed for more than a year. This week, the ride-hailing firm ousted its chief security officer and one of his deputies for their roles in keeping the hack under wraps, which included a $100,000 payment to the attackers.”
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