Faceshift: Apple buys Star Wars motion-capture company - BBC News

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Apple has purchased the company behind motion-capture technology used in the latest Star Wars film. Faceshift, a Zurich based start-up, specialises in software that allows 3D animated characters to mimic the facial expressions of an actor. Apple has now bought the company, though it is not known how much the deal cost the tech giant. It is also unclear what Apple's plans are for the company following its acquisition. A spokesman said: "Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans."


How facial recognition is helping astronomers reveal the secrets of dark matter Digital Trends

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Could the same technology that is used to unlock people's smartphones also help unlock the secrets of the universe? It may sound unlikely, but that's exactly what researchers from Switzerland's science and technology-focused university ETH Zurich are working to achieve. Using a variation of the type of artificial intelligence neural network behind today's facial recognition technology, they have developed new A.I. tools that could prove a game-changer in the discovery of so-called "dark matter." Physicists believe that understanding this mysterious substance is necessary to explain fundamental questions about the underlying structure of the universe. "The algorithm we [use] is very close to what is commonly used in facial recognition," Janis Fluri, a Ph.D. student who works in an ETH Zurich lab focused on applying neural networks to cosmological problems, told Digital Trends.


How to Steal an AI

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In the burgeoning field of computer science known as machine learning, engineers often refer to the artificial intelligences they create as "black box" systems: Once a machine learning engine has been trained from a collection of example data to perform anything from facial recognition to malware detection, it can take in queries--Whose face is that? Is this app safe?--and spit out answers without anyone, not even its creators, fully understanding the mechanics of the decision-making inside that box. But researchers are increasingly proving that even when the inner workings of those machine learning engines are inscrutable, they aren't exactly secret. In fact, they've found that the guts of those black boxes can be reverse-engineered and even fully reproduced--stolen, as one group of researchers puts it--with the very same methods used to create them. In a paper they released earlier this month titled "Stealing Machine Learning Models via Prediction APIs," a team of computer scientists at Cornell Tech, the Swiss institute EPFL in Lausanne, and the University of North Carolina detail how they were able to reverse engineer machine learning-trained AIs based only on sending them queries and analyzing the responses.


How to Steal an AI

#artificialintelligence

In the burgeoning field of computer science known as machine learning, engineers often refer to the artificial intelligences they create as "black box" systems: Once a machine learning engine has been trained from a collection of example data to perform anything from facial recognition to malware detection, it can take in queries--Whose face is that? Is this app safe?--and spit out answers without anyone, not even its creators, fully understanding the mechanics of the decision-making inside that box. But researchers are increasingly proving that even when the inner workings of those machine learning engines are inscrutable, they aren't exactly secret. In fact, they've found that the guts of those black boxes can be reverse-engineered and even fully reproduced--stolen, as one group of researchers puts it--with the very same methods used to create them. In a paper they released earlier this month titled "Stealing Machine Learning Models via Prediction APIs," a team of computer scientists at Cornell Tech, the Swiss institute EPFL in Lausanne, and the University of North Carolina detail how they were able to reverse engineer machine learning-trained AIs based only on sending them queries and analyzing the responses.


Scientists develop more accurate method to find good targets for cancer immunotherapy

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Ludwig Cancer Research scientists have developed a new and more accurate method to identify the molecular signs of cancer likely to be presented to helper T cells, which stimulate and orchestrate the immune response to tumors and infectious agents. The study, led by David Gfeller and Michal Bassani-Sternberg of the Lausanne Branch of the Ludwig Institute for Cancer Research, is reported in the current issue of Nature Biotechnology. The new method combines two powerful new technologies. One is a mass spectrometry technology developed by Bassani-Sternberg's lab to rapidly and inexpensively obtain the amino acid sequences of thousands of peptide antigens--or protein fragments--bound to a molecular complex known as HLA that is expressed on the surface of cells. The other is a novel computational tool developed in Gfeller's lab that is based on machine learning, the computational approach that powers face-recognition software, among other things.