When data scientists in Chicago, Illinois, set out to test whether a machine-learning algorithm could predict how long people would stay in hospital, they thought that they were doing everyone a favour. Keeping people in hospital is expensive, and if managers knew which patients were most likely to be eligible for discharge, they could move them to the top of doctors' priority lists to avoid unnecessary delays. It would be a win–win situation: the hospital would save money and people could leave as soon as possible. Starting their work at the end of 2017, the scientists trained their algorithm on patient data from the University of Chicago academic hospital system. Taking data from the previous three years, they crunched the numbers to see what combination of factors best predicted length of stay.
Agbiotech newcomer Inari has raised $89 million to pursue an ambitious goal: to challenge the status quo in agriculture. Plants edited with the new genome editing tools will incorporate useful traits and will not be classed as GMOs.Credit: reHAWKEYE / Alamy Stock Photo Inari is one of a several small companies with similarly lofty goals who are capitalizing on new editing technologies, such as CRISPR, and computational methods for predictive modeling. Such tools make crop development faster and less expensive, and potentially could give startups a shot at competing with the big players by sidestepping onerous and expensive regulatory oversight. Just a few years ago, a seed developer could plan on spending a decade and up to $100 million on bringing one new crop trait to market (Nat. That's not only because the old tools for altering the genetics of these crops, such as Agrobacterium-mediated transformation, were slower, more expensive and more unpredictable than CRISPR, but also because of regulations, both in the United States and especially in Europe.
Over the next three years, Houssam Abbas will carefully send 80 modified Traxxas RC rally cars--the Ford Fiesta model--to research facilities around the country. Some will go to Arizona State University, others to Clemson University, the State University of New York at Stony Brook, UCLA, Vanderbilt University, or the University of Iowa. In each place, researchers will open their packages, take out the 21-inch, modified, 1/10th-scale car, and begin to run tests. Abbas, an assistant professor of electrical engineering and computer science at Oregon State University, hopes the toys are the key to cracking the self-driving car. He and colleagues believe their miniature, cheap, open source, self-driving "platform" will give 33 scientists of all stripes chances to experiment with cutting-edge technology at a critical moment: before autonomous vehicles hit the streets en masse.
Images of seven people were passed on by local police for use in a facial recognition system at King's Cross in London in an agreement that was struck in secret, the details of which were made public for the first time today. A police report, published by the deputy London mayor Sophie Linden on Friday, showed that the scheme ran for two years from 2016 without any apparent central oversight from either the Metropolitan police or the office of the mayor, Sadiq Khan. Writing to London assembly members, Linden said she "wanted to pass on the [Metropolitan police service's] apology" for failing to previously disclose that the scheme existed and announced that similar local image sharing agreements were now banned. There had been "no other examples of images having been shared with private companies for facial recognition purposes" by the Met, Linden said, according to "the best of its knowledge and record-keeping". The surveillance scheme – controversial because it involved tracking individuals without their consent – was originally agreed between borough police in Camden and the owner of the 27-hectare King's Cross site in 2016.
It is possible to train just a neural network to answer questions about a scene by feeding in millions of examples as training data. But a human child doesn't require such a vast amount of data in order to grasp what a new object is or how it relates to other objects. Also, a network trained that way has no real understanding of the concepts involved--it's just a vast pattern-matching exercise. So such a system would be prone to making very silly mistakes when faced with new scenarios. This is a common problem with today's neural networks and underpins shortcomings that are easily exposed (see "AI's language problem").
It will still be a while before you are able to order drone-delivered packages, however. The news: The Federal Aviation Administration has granted UPS's drone business a Part 135 certification, meaning it is treated as a full-fledged airline, able to operate as many drones in as many locations as it wishes (although there are a lot of obstacles and caveats before that can happen in reality). UPS has dubbed its new drone airline "UPS Flight Forward," and it's the first in the US to gain official recognition. Currently: UPS has been providing a drone delivery service at the WakeMed hospital and campus in Raleigh, North Carolina, since March, moving medical samples around the site about 10 times a day. This new certification means UPS can expand beyond this site.
Work to bring driverless cars to Britain's streets has reached a milestone with the first demonstration of an autonomous fleet driving in a "complex urban environment" in London. Ford Mondeos fitted with autonomous technology from the UK tech firm Oxbotica operated on public roads around the former Olympic Park in Stratford this week. Driven programme, a partially government-funded consortium, said it had "exceeded their initial plan" and was a significant step in confirming autonomous vehicles could operate in real-life situations in a large European city. Oxbotica said first passenger trials of a separate venture, an autonomous ride-sharing taxi service planned with the cab firm Addison Lee in the capital, could now start in June 2020. The Driven team – a combination of local authority planners, insurers, cyber-security and data experts, as well as Oxbotica – have been conducting trials in Oxford to examine what they called the "ecosystem" around autonomous vehicles, such as potential problems with hackers, communications technology and the legal framework.
Prolific science and science fiction writer Isaac Asimov (1920–1992) developed the Three Laws of Robotics in the hope of guarding against potentially dangerous artificial intelligence. They first appeared in his 1942 short story Runaround. "Many computer engineers use the three laws as a tool for how they think about programming," says Chris Stokes, a philosopher at Wuhan University in China. But the trouble is, they don't work. In "Why the Three Laws of Robotics Do Not Work," published in the International Journal of Research in Engineering and Innovation, Stokes writes that "the Three Laws are not sufficient when it comes to controlling an artificial intelligence."
Prosthetic legs with sensors can help people avoid unseen obstacles underfoot. Three people who have had a leg amputated found that they perceived such prostheses as an extension of their own body and were able to climb stairs more quickly than they could with a conventional prosthetic leg. Prosthetic limbs are often abandoned due to people's poor mobility when using them. The devices don't restore sensation, leaving people to rely on touch feedback from the stump meeting the socket. Stanisa Raspopovic at the Swiss Federal Institute of Technology in Zurich and his colleagues modified a commercially available prosthetic leg by adding sensors to an insole on the foot and inside the knee.
The story of Alberta AI starts on April 1, 1964, with the opening of the University of Alberta's department of computing science. Researchers such as Randy Goebel would graduate from the department and set the foundation for early work on the science of natural language processing and AI in the 80s, the latter of which took the form of studying games like chess. By the '90s, however, several'AI winters' -- periods of funding constraints for research and lack of press interest to generate public excitement -- inhibited the discoveries coming out of the university. "The symptoms of the winter are more that industries [that] bought into the idea that expert systems could help them found that it was way too expensive to implement," said Goebel, now a U of A professor and principal investigator at the Alberta Machine Intelligence Institute (Amii). "If you scale up, it costs a lot to have you and me write down processes for chemistry, for example."