A synthetic skin could help add the sensation of touch to prosthetic hands or give video games a more realistic feel. The skin comes as a battery-free patch that can be stuck onto any part of the body. To create the sensation of touch, the patch vibrates and gently pushes the skin surface. An internal magnet and copper coil allow it to be powered wirelessly, while the cloth covering can be coloured to match the user's skin. The synthetic skin was created by John Rogers at Northwestern University in Illinois and his colleagues.

Female fruit flies get a boost in their long-term memory after mating thanks to a molecule found in male fly semen. This molecule – called the sex peptide – binds to the sperm of male flies and is passed on to females, where it travels from the reproductive tract to the brain. It was already known that this molecule, which is unique to fruit flies, alters behaviour. After mating, it changes what females prefer to eat and makes them reject future mating partners, for example. It does this by acting on nerve cells, or neurons, located throughout the body.

Training software that emulates brain networks to identify dog breeds or sports equipment is by now old news. But getting such an AI network to learn a process on its own that is innate to early child development is truly novel. In a paper published Wednesday in Science Advances, a neural network distinguished between different quantities of things, even though it was never taught what a number is. The neural net reprised a cognitive skill innate to human babies, monkeys and crows, among others. Without any training, it suddenly could tell the difference between larger and smaller amounts--a skill called numerosity, or number sense.

Low-rank approximations of data matrices have become an important tool in Machine Learning in the field of bio-informatics, computer vision, text processing, recommender systems, and others. They allow for embedding high dimensional data in lower dimensional spaces which mitigate effects due to noise, uncover latent relations, or facilitate further processing. In general, MF is a process to find two factor matrices, P R, k m and Q R, k n, to describe a given m-by-n training matrix R in which some entries may be missing. MF can be found in many applications, but we only use collaborative filtering in recommender systems as examples. It is based on the assumption that the entries of R are the historical users' preferences for merchandises, and the task on hand is to predict unobserved user behavior (i.e., missing entries in R) to make a suitable recommendation.

DOE's Advanced Research Projects Agency-Energy (ARPA-E) announced $15 million in funding for 23 projects.

As long as there will be financial markets, there will be financial crashes. Most suffer when the market takes a dip… those who can foresee it can protect their assets or take risky short positions to make a profit (a nevertheless stressful situation to be in, as depicted in the Big-short). An asset on the market is associated to a dynamical system, whose price varies in function of the available information. The price of an asset on a financial market is determined by a wide range of information, and in the efficient market hypothesis, a simple change in the information will be immediately priced in. In the same way that phase transitions occur between solids, liquids, and gases, we can discern a normal regime on the market from a chaotic one.

Engineers at Duke University have developed a microscope that adapts its lighting angles, colors and patterns while teaching itself the optimal settings needed to complete a given diagnostic task. In the initial proof-of-concept study, the microscope simultaneously developed a lighting pattern and classification system that allowed it to quickly identify red blood cells infected by the malaria parasite more accurately than trained physicians and other machine learning approaches. The results appear online on November 19 in the journal Biomedical Optics Express. "A standard microscope illuminates a sample with the same amount of light coming from all directions, and that lighting has been optimized for human eyes over hundreds of years," said Roarke Horstmeyer, assistant professor of biomedical engineering at Duke. "But computers can see things humans can't," Hortmeyer said. "So not only have we redesigned the hardware to provide a diverse range of lighting options, we've allowed the microscope to optimize the illumination for itself."

This is a global problem, yet one of the most striking examples is the case of Japan. From 800 until 1900 CE, Japan used a writing system called Kuzushiji, which was removed from the curriculum in 1900 when the elementary school education was reformed. Currently, the overwhelming majority of Japanese speakers cannot read texts which are more than 150 years old. The volume of these texts -- comprised of over three million books in storage but only readable by a handful of specially-trained scholars -- is staggering. One library alone has digitized 20 million pages from such documents.

The amplified efficiency of AI means that, once a system is trained and deployed, malicious AI can attack a far greater number of devices and networks more quickly and cheaply than a malevolent human actor. Given sufficient computing power, an AI system can launch many attacks, be more selective in its targets and more devastating in its impact. Currently, the use of AI for attackers is mainly pursued at an academic level and we have yet to see AI attacks in the wild. However, there is much talk in the industry about attackers using AI in their malicious efforts, and defenders using machine learning as a defense technology. AI-based Cyberattacks: The malware operates AI algorithms as an integral part of its business logic.

Jaimi has worked with Brendan's team enough to know how much talent acquisition professionals are craving to hire candidates quickly, fairly, and efficiently. At the same time, there has been explosive growth in the number of software tools that have been brought to market to help recruiters do just that. "On LinkedIn, you'll see a bunch of ads," Jaimi says, "for a bunch of different vendors that say, 'Hey, we can use machine learning to help you find the right candidate.'" She notes that often these companies also tout that their products eliminate human bias. "It sounds great," Jaimi tells Brendan, "and it's not to say that it couldn't be great, but there can be really serious unintended consequences that can cause a legal liability."