Wirelessly powered, biomimetic spybots…
A new contest seeks flight systems inspired by Mother Nature and powered by directed-energy beams.
Tired: multi-rotor copters and fixed-wing drones. Wired: flying robots that move like living animals, are crafted of next-generation materials, and draw their power not from batteries but energy beamed from nearby aircraft.
Posted in information science, law, robotics/AI
December was a big month for advocates of regulating artificial intelligence. First, a bipartisan group of senators and representatives introduced the Future of A.I. Act, the first federal bill solely focused on A.It would create an advisory committee to make recommendations about A.I. — on topics including the technology’s effect on the American work force and strategies to protect the privacy rights of those it impacts. Then the New York City Council approved a first-of-its-kind bill that once signed into law will create a task force to examine its own use of automated decision systems, with the ultimate goal of making its use of algorithms fairer and more transparent.
Sure, the technology poses risks. But the current approach to regulating it is a mistake.
China has unveiled three-year plans to increase the country’s economic competitiveness by developing “key technologies” in nine industrial sectors, from robotics to railways.
Other areas include smart cars, robotics, advanced shipbuilding and maritime equipment, modern agricultural machinery, advanced medical devices and drugs, new materials, smart manufacturing and machine tools.
The aim is “to make China a powerful manufacturing country” and upgrade the nation’s industrial power through “the internet, big data and artificial intelligence”, the commission said.
To achieve that goal, the agency has laid out specific targets to develop key technologies and guide research and the flow of funds in each sector.
After less than eight months of development, the algorithms are helping intel analysts exploit drone video over the battlefield.
Earlier this month at an undisclosed location in the Middle East, computers using special algorithms helped intelligence analysts identify objects in a video feed from a small ScanEagle drone over the battlefield.
A few days into the trials, the computer identified objects — people, cars, types of building — correctly about 60 percent of the time. Just over a week on the job — and a handful of on-the-fly software updates later — the machine’s accuracy improved to around 80 percent. Next month, when its creators send the technology back to war with more software and hardware updates, they believe it will become even more accurate.
Researchers have developed an easy-to-build camera that produces 3D images from a single 2D image without any lenses. In an initial application of the technology, the researchers plan to use the new camera, which they call DiffuserCam, to watch microscopic neuron activity in living mice without a microscope. Ultimately, it could prove useful for a wide range of applications involving 3D capture.
The camera is compact and inexpensive to construct because it consists of only a diffuser — essentially a bumpy piece of plastic — placed on top of an image sensor. Although the hardware is simple, the software it uses to reconstruct high resolution 3D images is very complex.
“The DiffuserCam can, in a single shot, capture 3D information in a large volume with high resolution,” said the research team leader Laura Waller, University of California, Berkeley. “We think the camera could be useful for self-driving cars, where the 3D information can offer a sense of scale, or it could be used with machine learning algorithms to perform face detection, track people or automatically classify objects.”
Brendan John Frey FRSC (born 29 August 1968) is a Canadian-born machine learning and genome biology researcher, known mainly for his work on factor graphs, the wake-sleep algorithm for deep learning, and using machine learning to model genome biology and understand genetic disorders. He founded Deep Genomics and is currently its CEO, and he is a Professor of Engineering and Medicine at the University of Toronto. He co-developed a new computational approach to identifying the genetic determinants of disease, was one of the first researchers to successfully train a deep neural network, and was a pioneer in the introduction of iterative message-passing algorithms.
Frey studied computer engineering and physics at the University of Calgary (BSc 1990) and the University of Manitoba (MSc 1993), and then studied neural networks and graphical models as a doctoral candidate at the University of Toronto under the supervision of Geoffrey Hinton (PhD 1997). He was an invited participant of the Machine Learning program at the Isaac Newton Institute for Mathematical Sciences in Cambridge, UK (1997) and was a Beckman Fellow at the University of Illinois at Urbana Champaign (1999).
Following his undergraduate studies, Frey worked as a Junior Research Scientist at Bell-Northern Research from 1990 to 1991. After completing his postdoctoral studies at the University of Illinois at Urbana-Champaign, Frey was an Assistant Professor in the Department of Computer Science at the University of Waterloo, from 1999 to 2001.
In 2001, Frey joined the Department of Electrical and Computer Engineering at the University of Toronto and was cross-appointed to the Department of Computer Science, the Banting and Best Department of Medical Research and the Terrence Donnelly Centre for Cellular and Biomolecular Research. From 2008 to 2009, he was a Visiting Researcher at Microsoft Research, Cambridge, UK, and a Visiting Professor in the Cavendish Laboratories and Darwin College at Cambridge University. Between 2001 and 2014, Frey consulted for several groups at Microsoft Research and acted as a member of its Technical Advisory Board.
When Kumar lost his job, he became part of a wave of layoffs washing through the Indian IT industry—a term that includes, in its vastness, call centers, engineering services, business process outsourcing firms, and infrastructure management and software companies. The recent layoffs are part of the industry’s most significant period of churn since it began to boom two decades ago. Companies don’t necessarily attribute these layoffs directly to automation, but at the same time, they constantly identify automation as the spark for huge changes in the industry. Bots, machine learning, and algorithms that robotically execute processes are rendering old skills redundant, recasting the idea of work and making a smaller labor force seem likely.
Technology outsourcing has been India’s only reliable job creator in the past 30 years. Now artificial intelligence threatens to wipe out those gains.
With machine learning algorithms evolving at an incredibly fast pace, concerns are mounting whether artificial intelligence (AI) is the logical continuation of human history or its demise. RT talked to three experts in the field about the benefits and dangers of AI.
Three AI experts engaged in a debate on RT about the benefits and dangers of rapidly-developing technology and AI.
Stanford researchers have developed an algorithm that offers diagnoses based off chest X-ray images. It can diagnose up to 14 types of medical conditions and is able to diagnose pneumonia better than expert radiologists working alone.
A paper about the algorithm, called CheXNet, was published Nov. 14 on the open-access, scientific preprint website arXiv.
“Interpreting X-ray images to diagnose pathologies like pneumonia is very challenging, and we know that there’s a lot of variability in the diagnoses radiologists arrive at,” said Pranav Rajpurkar, a graduate student in the Machine Learning Group at Stanford and co-lead author of the paper. “We became interested in developing machine learning algorithms that could learn from hundreds of thousands of chest X-ray diagnoses and make accurate diagnoses.”
In the mid-1900s, art historian Maurits Michel van Dantzig developed a system to identify artists by their brush or pen strokes, which he called Pictology. Dantzig found shape, length, direction, and pressure all contributed to a kind of stroke signature, unique to each artist.
New research with contributions from The Hague suggests that Pictology might be the key to helping machines understand art, allowing systems to quickly verify whether brushstrokes were from an original painter or a forger.
After analyzing 80,000 brushstrokes from 297 digitized sketches and drawings, an AI system was able to spot forged drawings in the style of Pablo Picasso, Henri Matisse, and Egon Schiele with 100% accuracy. The “fakes” were commissioned recreations of specific drawings, which the algorithms had not been shown previously.
