Tag Archives: nasa
Boston Dynamics’ SpotMini Robot Dog Goes on Sale in 2019
Stephen Shankland | CNET
“The company has 10 SpotMini prototypes now and will work with manufacturing partners to build 100 this year, said company co-founder and President Marc Raibert at a TechCrunch robotics conference Friday. ‘That’s a prelude to getting into a higher rate of production’ in anticipation of sales next year, he said. Who’ll buy it? Probably not you.”
Also from Boston Dynamics’ this week:
Made In Space Wins NASA Contract for Next-Gen ‘Vulcan’ Manufacturing System
Mike Wall | Space.com
“’The Vulcan hybrid manufacturing system allows for flexible augmentation and creation of metallic components on demand with high precision,’ Mike Snyder, Made In Space chief engineer and principal investigator, said in a statement. …When Vulcan is ready to go, Made In Space aims to demonstrate the technology on the ISS, showing Vulcan’s potential usefulness for a variety of exploration missions.”
Duplex Shows Google Failing at Ethical and Creative AI Design
Natasha Lomas | TechCrunch
“But while the home crowd cheered enthusiastically at how capable Google had seemingly made its prototype robot caller—with Pichai going on to sketch a grand vision of the AI saving people and businesses time—the episode is worryingly suggestive of a company that views ethics as an after-the-fact consideration. One it does not allow to trouble the trajectory of its engineering ingenuity.”
What Artists Can Tech Us About Making Technology More Human
Elizabeth Stinson| Wired
“For the last year, Park, along with the artist Sougwen Chung and dancers Jason Oremus and Garrett Coleman of the dance collective Hammerstep, have been working out of Bell Labs as part of a residency called Experiments in Art and Technology. The year-long residency, a collaboration between Bell Labs and the New Museum’s incubator, New Inc, culminated in ‘Only Human,’ a recently-opened exhibition at Mana where the artists’ pieces will be on display through the end of May.”
The White House Says a New AI Task Force Will Protect Workers and Keep America First
Will Knight | MIT Technology Review
“The meeting and the select committee signal that the administration takes the impact of artificial intellgence seriously. This has not always been apparent. In his campaign speeches, Trump suggested reviving industries that have already been overhauled by automation. The Treasury secretary, Steven Mnuchin, also previously said that the idea of robots and AI taking people’s jobs was ‘not even on my radar screen.’”
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In an interview at Singularity University’s Exponential Medicine in San Diego, Neil Jacobstein shared some groundbreaking developments in artificial intelligence for healthcare.
Jacobstein is Singularity University’s faculty chair in AI and robotics, a distinguished visiting scholar at Stanford University’s MediaX Program, and has served as an AI technical consultant on research and development projects for organizations like DARPA, Deloitte, NASA, Boeing, and many more.
According to Jacobstein, 2017 was an exciting year for AI, not only due to how the technology matured, but also thanks to new applications and successes in several health domains.
Among the examples cited in his interview, Jacobstein referenced a 2017 breakthrough at Stanford University where an AI system was used for skin cancer identification. To train the system, the team showed a convolutional neural network images of 129,000 skin lesions. The system was able to differentiate between images displaying malignant melanomas and benign skin lesions. When tested against 21 board–certified dermatologists, the system made comparable diagnostic calls.
Pattern recognition and image detection are just two examples of successful uses of AI in healthcare and medicine—the list goes on.
“We’re seeing AI and machine learning systems performing at narrow tasks remarkably well, and getting breakthrough results both in AI for problem-solving and AI with medicine,” Jacobstein said.
He continued, “We are not seeing super-human terminator systems. But we are seeing more members of the AI community paying attention to managing the downside risk of AI responsibly.”
Watch the full interview to learn more examples of how AI is advancing in healthcare and medicine and elsewhere and what Jacobstein thinks is coming next.
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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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New planets found in distant corners of the galaxy. Climate models that may improve our understanding of sea level rise. The emergence of new antimalarial drugs. These scientific advances and discoveries have been in the news in recent months.
While representing wildly divergent disciplines, from astronomy to biotechnology, they all have one thing in common: Artificial intelligence played a key role in their scientific discovery.
One of the more recent and famous examples came out of NASA at the end of 2017. The US space agency had announced an eighth planet discovered in the Kepler-90 system. Scientists had trained a neural network—a computer with a “brain” modeled on the human mind—to re-examine data from Kepler, a space-borne telescope with a four-year mission to seek out new life and new civilizations. Or, more precisely, to find habitable planets where life might just exist.
The researchers trained the artificial neural network on a set of 15,000 previously vetted signals until it could identify true planets and false positives 96 percent of the time. It then went to work on weaker signals from nearly 700 star systems with known planets.
The machine detected Kepler 90i—a hot, rocky planet that orbits its sun about every two Earth weeks—through a nearly imperceptible change in brightness captured when a planet passes a star. It also found a sixth Earth-sized planet in the Kepler-80 system.
AI Handles Big Data
The application of AI to science is being driven by three great advances in technology, according to Ross King from the Manchester Institute of Biotechnology at the University of Manchester, leader of a team that developed an artificially intelligent “scientist” called Eve.
Those three advances include much faster computers, big datasets, and improved AI methods, King said. “These advances increasingly give AI superhuman reasoning abilities,” he told Singularity Hub by email.
AI systems can flawlessly remember vast numbers of facts and extract information effortlessly from millions of scientific papers, not to mention exhibit flawless logical reasoning and near-optimal probabilistic reasoning, King says.
AI systems also beat humans when it comes to dealing with huge, diverse amounts of data.
That’s partly what attracted a team of glaciologists to turn to machine learning to untangle the factors involved in how heat from Earth’s interior might influence the ice sheet that blankets Greenland.
Algorithms juggled 22 geologic variables—such as bedrock topography, crustal thickness, magnetic anomalies, rock types, and proximity to features like trenches, ridges, young rifts, and volcanoes—to predict geothermal heat flux under the ice sheet throughout Greenland.
The machine learning model, for example, predicts elevated heat flux upstream of Jakobshavn Glacier, the fastest-moving glacier in the world.
“The major advantage is that we can incorporate so many different types of data,” explains Leigh Stearns, associate professor of geology at Kansas University, whose research takes her to the polar regions to understand how and why Earth’s great ice sheets are changing, questions directly related to future sea level rise.
“All of the other models just rely on one parameter to determine heat flux, but the [machine learning] approach incorporates all of them,” Stearns told Singularity Hub in an email. “Interestingly, we found that there is not just one parameter…that determines the heat flux, but a combination of many factors.”
The research was published last month in Geophysical Research Letters.
Stearns says her team hopes to apply high-powered machine learning to characterize glacier behavior over both short and long-term timescales, thanks to the large amounts of data that she and others have collected over the last 20 years.
Emergence of Robot Scientists
While Stearns sees machine learning as another tool to augment her research, King believes artificial intelligence can play a much bigger role in scientific discoveries in the future.
“I am interested in developing AI systems that autonomously do science—robot scientists,” he said. Such systems, King explained, would automatically originate hypotheses to explain observations, devise experiments to test those hypotheses, physically run the experiments using laboratory robotics, and even interpret the results. The conclusions would then influence the next cycle of hypotheses and experiments.
His AI scientist Eve recently helped researchers discover that triclosan, an ingredient commonly found in toothpaste, could be used as an antimalarial drug against certain strains that have developed a resistance to other common drug therapies. The research was published in the journal Scientific Reports.
Automation using artificial intelligence for drug discovery has become a growing area of research, as the machines can work orders of magnitude faster than any human. AI is also being applied in related areas, such as synthetic biology for the rapid design and manufacture of microorganisms for industrial uses.
King argues that machines are better suited to unravel the complexities of biological systems, with even the most “simple” organisms are host to thousands of genes, proteins, and small molecules that interact in complicated ways.
“Robot scientists and semi-automated AI tools are essential for the future of biology, as there are simply not enough human biologists to do the necessary work,” he said.
Creating Shockwaves in Science
The use of machine learning, neural networks, and other AI methods can often get better results in a fraction of the time it would normally take to crunch data.
For instance, scientists at the National Center for Supercomputing Applications, located at the University of Illinois at Urbana-Champaign, have a deep learning system for the rapid detection and characterization of gravitational waves. Gravitational waves are disturbances in spacetime, emanating from big, high-energy cosmic events, such as the massive explosion of a star known as a supernova. The “Holy Grail” of this type of research is to detect gravitational waves from the Big Bang.
Dubbed Deep Filtering, the method allows real-time processing of data from LIGO, a gravitational wave observatory comprised of two enormous laser interferometers located thousands of miles apart in California and Louisiana. The research was published in Physics Letters B. You can watch a trippy visualization of the results below.
In a more down-to-earth example, scientists published a paper last month in Science Advances on the development of a neural network called ConvNetQuake to detect and locate minor earthquakes from ground motion measurements called seismograms.
ConvNetQuake uncovered 17 times more earthquakes than traditional methods. Scientists say the new method is particularly useful in monitoring small-scale seismic activity, which has become more frequent, possibly due to fracking activities that involve injecting wastewater deep underground. You can learn more about ConvNetQuake in this video:
King says he believes that in the long term there will be no limit to what AI can accomplish in science. He and his team, including Eve, are currently working on developing cancer therapies under a grant from DARPA.
“Robot scientists are getting smarter and smarter; human scientists are not,” he says. “Indeed, there is arguably a case that human scientists are less good. I don’t see any scientist alive today of the stature of a Newton or Einstein—despite the vast number of living scientists. The Physics Nobel [laureate] Frank Wilczek is on record as saying (10 years ago) that in 100 years’ time the best physicist will be a machine. I agree.”
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