Google and others think software that learns to learn could take over some work done by AI experts.
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Category: robotics/AI – Page 2,297
I never heard of this sort of making bubbles. And the details given are slim. Anyone here of this?
The first Death Star had a diameter of between 140 and 160 kilometers. The second Death Star’s diameter ranged from 160 to 900 kilometers.
There are two near term technologies which could be applied to making Death Star sized structures:
1. Space bubbles
In Brief:
- Even though AI systems creating AI systems seems like the recipe for a Sci-Fi nightmare, experts agree that it could create a future with a less expensive and more efficient workforce
- The benefits of an AI-powered future might be outweighed by the jobs that the technology makes obsolete
Imagine the conflicted feelings of the machine learning expert who is creating artificial intelligence (AI) that they know will one day, possibly very soon, be able to create better AI than them. It’s the new age’s way of holding on to the time-honored tradition of having to train your own replacement. Machine learning experts are currently being paid a premium wage due to their limited numbers and the high demand for their valuable skills. However, with the dawn of software that is “learning to learn,” those days may be numbered.
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In Brief The success of this work will help healthcare professionals diagnose more accurately and efficiently, and it will allow for more diagnostic care in areas with limited healthcare services and providers.
In early August, IBM announced that it will acquire Merge Healthcare Inc., a company that sells systems that help medical professionals access and store medical images. This move is a critical step in IBM’s plan to put AI to work medically by training its Watson software to identify maladies like heart disease and cancer.
Merge is valuable to IBM because it owns 30 billion images, including computerized tomography, X-rays, and magnetic-resonance-imaging scans. The company can use these images in its deep learning training program. IBM is hoping that the same kind of software that lets Flickr recognize your face or a dog in your photos can help Watson identify symptoms of diseases.
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Faster websites with fewer bugs
Posted in internet, robotics/AI
Daniela Rus loves Singapore. As the MIT professor sits down in her Frank Gehry-designed office in Cambridge, Massachusetts, to talk about her research conducted in Singapore, her face starts to relax in a big smile.
Her story with Singapore started in the summer of 2010, when she made her first visit to one of the most futuristic and forward-looking cities in the world. “It was love at first sight,” says the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and the director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). That summer, she came to Singapore to join the Singapore-MIT Alliance for Research and Technology (SMART) as the first principal investigator in residence for the Future of Urban Mobility Research Program.
“In 2010, nobody was talking about autonomous driving. We were pioneers in developing and deploying the first mobility on demand for people with self-driving golf buggies,” says Rus. “And look where we stand today! Every single car maker is investing millions of dollars to advance autonomous driving. Singapore did not hesitate to provide us, at an early stage, with all the financial, logistical, and transportation resources to facilitate our work.”
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Computer boffins Juan Echeverria and Shi Zhou at University College London have chanced across a dormant Twitter botnet made up of more than 350,000 accounts with a fondness for quoting Star Wars novels.
Twitter bots have been accused of warping the tone of the 2016 election. They also can be used for entertainment, marketing, spamming, manipulating Twitter’s trending topics list and public opinion, trolling, fake followers, malware distribution, and data set pollution, among other things.
In a recently published research paper, the two computer scientists recount how a random sampling of 1 per cent of English-speaking Twitter accounts – about 6 million accounts – led to their discovery.
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