But "Saya" was a different kind of star, because she is the product of a Tokyo computer lab. And like all "parents", her creators have big ambitions for her, writes the BBC's Yvette Tan. "'I think I've seen her somewhere' or'She looks like someone I know' are what people usually say when they see Saya," says Yuka Ishikawa, one half of the husband and wife graphic artist team behind Saya. When the couple first posted pictures of the hyper-realistic schoolgirl online last year, it was a revelation about what can be achieved with computer design. Her slightly askew school tie, heavily fringed hair, freckled skin and teenage pout left thousands trying to work out whether or not she was a real person.

This week – augmenting human senses, stealing AI, Google tries to harness cloud robotics, Samsung gets its own Siri and we get a closer look on a farming robot. Plus – what is life and what is synthetic biology, designer babies and the future of human reproduction and a mask that allows you to smell virtual worlds. Biohacking may open us a way to sense the world in a completely and different way. And there are people who are working on projects aimed at augmenting our senses in a new and creative way. A Device For The Deaf That Lets You "Listen" With Your Skin It's called Versatile Extra-Sensory Transducer, or VEST in short, and it works by picking up sound either from surrounding or from a smartphone, and then translates them into movement of 32 tiny motors that vibrate on your back.

For the third quarter of 2016, MobiHealthNews tracked just shy of 600 million in deals. While a few large deals anchored the quarter, the majority of the 45 we tracked this quarter were small; only seven were more than 20 million, most were 10 million or less. For this analysis, we've omitted investments in joint ventures like Verily and Sanofi's OnDuo as well as grant funding. Read on for the 45 deals in the digital health space we tracked throughout the quarter, listed in order from largest to smallest amount of funding. Canada-based wearable technology company Thalmic Labs, which makes the connected armband Myo, raised 120 million in Series B funding in a round led by Intel Capital, the Amazon Alexa Fund and Fidelity Investments Canada.

"The Great Silence," a story written by science fiction author Ted Chiang and artists Allora & Calzadilla, ponders the question of why humans are so obsessed with the idea of communicating with aliens at the same time that we show such vast indifference to endangered species like parrots, which are perfectly capable of communication. The story achieves much of its emotional impact by being told from the point of view of a parrot. Author Karen Joy Fowler recently selected "The Great Silence" for inclusion in The Best American Science Fiction and Fantasy 2016, which she co-edited with John Joseph Adams. She says the story struck her in particular because of the extensive research into animal intelligence that she did to write her 2013 novel We Are All Completely Beside Ourselves. "I really was the perfect reader for it," Fowler says in Episode 224 of the Geek's Guide to the Galaxy podcast.

Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great success in supervised learning problems, and multiple instance learning as a typical weakly-supervised learning method is effective for many applications in computer vision, biometrics, nature language processing, etc. In this paper, we revisit the problem of solving multiple instance learning problems using neural networks. Neural networks are appealing for solving multiple instance learning problem. The multiple instance neural networks perform multiple instance learning in an end-to-end way, which take a bag with various number of instances as input and directly output bag label. All of the parameters in a multiple instance network are able to be optimized via back-propagation. We propose a new multiple instance neural network to learn bag representations, which is different from the existing multiple instance neural networks that focus on estimating instance label. In addition, recent tricks developed in deep learning have been studied in multiple instance networks, we find deep supervision is effective for boosting bag classification accuracy. In the experiments, the proposed multiple instance networks achieve state-of-the-art or competitive performance on several MIL benchmarks. Moreover, it is extremely fast for both testing and training, e.g., it takes only 0.0003 second to predict a bag and a few seconds to train on a MIL datasets on a moderate CPU.

An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a hill-climbing technique. This algorithm scales to large domains so long as a suitable standard structure learning method for complete data is available. We perform a wide range of experiments to demonstrate the benefits of learning Bayesian networks with such new approach.

NEW YORK A federal appeals court on Friday reinstated a 120 million jury award for Apple Inc (AAPL.O) against Samsung (005930.KS), marking the latest twist in the fierce patent war between the world's top smartphone manufacturers. The court said that there was substantial evidence for the jury verdict related to Samsung's infringement of Apple patents on its slide-to-unlock and autocorrect features, as well as quick links, which automatically turn information like addresses and phone numbers into links. Friday's decision was made by the full slate of judges on the U.S. Court of Appeals for the Federal Circuit in Washington, D.C. In an 8-3 ruling, the judges said that a previous panel of the same court should not have overturned the verdict last February. The three-judge panel did not follow U.S. Supreme Court limits on the scope of its review, because it examined evidence outside the record of the case, the decision said.

This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Such techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know.

Recent breakthroughs in artificial intelligence and machine learning are enabling computers to understand the world and respond intelligently to it. Google is already embracing these technologies for Android, but they're poised to have bigger implications, touching everything from drones to medical diagnosis. He made his fortune as co-founder of Netscape two decades ago, and more recently his firm has invested in successful companies like Facebook, Twitter, Airbnb, Slack, and Lyft. Andreessen is in constant contact with entrepreneurs and investors trying to build the next great technology company. Andreessen argues that recent breakthroughs mean artificial intelligence has the potential to spawn a new generation of big, important technology companies. At the same time, he acknowledges that certain industries have proven stubbornly resistant to technological change -- and he argues that more work is needed to bring the power of software to every corner of the economy. We spoke by phone in late September.

Daniel Lélis Baggio started his work in computer vision through medical image processing at InCor (Instituto do Coração Heart Institute) in São Paulo, where he worked with intra-vascular ultrasound image segmentation. Since then, he has focused on GPGPU and ported the segmentation algorithm to work with NVIDIA's CUDA. He has also dived into six degrees of freedom head tracking with a natural user interface group through a project called ehci (http://code.google.com/p/ehci/). He now works for the Brazilian Air Force. Shervin Emami (born in Iran) taught himself electronics and hobby robotics during his early teens in Australia.