SELLING FULL AUTONOMY BEFORE IT’S READY COULD BACKFIRE FOR TESLA
Tesla has a history of pre-selling products based on optimistic delivery schedules. This approach has served the company pretty well in the past, as customers ultimately loved their cars once they ultimately showed up. But that strategy could backfire hugely when it comes to Autopilot. Source: arstechnica.com
GRAALVM: RUN PROGRAMS FASTER ANYWHERE
Zero overhead interoperability between programming languages allows you to write polyglot applications and select the best language for your task. Source: graalvm.org
IBM RELEASES OPEN SOURCE AI SECURITY TOOL
IBM releases Adversarial Robustness Toolbox, an open source software library designed to help researchers and developers secure artificial intelligence (AI) systems Source: securityweek.com
CHINA’S TECH GIANTS ARE VENTURING INTO AUTONOMOUS DRIVING
If there’s one thing China’s tech giants are known for, it’s their ability to venture into everything from social media, to online payments, to delivery services. The latest thing they’re all speeding towards? Autonomous driving. Source: qz.com
IMPROVED HUBBLE DATA PROVIDE FRESH EVIDENCE FOR NEW PHYSICS
Measurements made by the European Space Agency’s Planck satellite, which maps the cosmic microwave background, predicted that the Hubble constant value should now be 67 kilometers per second per megaparsec (3.3 million light-years), and could be no higher than 69 kilometers per second per megaparsec. This means that for every 3.3 million light-years farther away a galaxy is from us, it is moving 67 kilometers per second faster. But Riess’s team measured a value of 73 kilometers per second per megaparsec, indicating galaxies are moving at a faster rate than implied by observations of the early universe.
Read moreTHIN FILM CONVERTS HEAT FROM ELECTRONICS INTO ENERGY
Nearly 70 percent of the energy produced in the United States each year is wasted as heat. Much of that heat is less than 100 degrees Celsius and emanates from things like computers, cars or large industrial processes. Engineers at the University of California, Berkeley, have developed a thin-film system that can be applied to sources of waste heat like these to produce energy at levels unprecedented for this kind of technology.
Read moreNASA’S GOT A PLAN FOR A ‘GALACTIC POSITIONING SYSTEM’ TO SAVE ASTRONAUTS LOST IN SPACE
Your phone’s GPS works fast, but Arzoumian said the galactic positioning system would work slower —taking the time needed to traverse long stretches of deep space. It would be a small, swivel-mounted X-ray telescope, which would look a lot like the big, bulky NICER stripped down to its barest minimum components. One after another, it would point at at least four millisecond pulsars, timing their X-ray ‘ticks’ like a GPS times the ticks of satellites.
Read more20 ENTANGLED QUBITS BRING THE QUANTUM COMPUTER CLOSER
In 1981, Richard Feynman suggested that a quantum computer might be able to simulate the evolution of quantum systems much better than classical computers. Except for several proof-of-principle experiments, no working quantum computer has yet been built. Source: ieee.org
AN AUGMENTED REALITY MICROSCOPE FOR CANCER DETECTION
Applications of deep learning to medical disciplines including ophthalmology, dermatology, radiology, and pathology have recently shown great promise to increase both the accuracy and availability of high-quality healthcare to patients around the world. At Google, we have also published results showing that a convolutional neural network is able to detect breast cancer metastases in lymph nodes at a level of accuracy comparable to a trained pathologist. However, because direct tissue visualization using a compound light microscope remains the predominant means by which a pathologist diagnoses illness, a critical barrier to the widespread adoption of deep learning in pathology is the dependence on having a digital representation of the microscopic tissue.
Read moreTHIS AI WILL TURN YOUR DOG INTO A CAT
As detailed in a paper published to arXiv, the neural net is actually a generative adversarial network (GAN), which is a way of training a machine learning algorithm without human supervision. In GANs, two neural nets are pitted against one another: One neural net generates new images and tries to trick the other neural net into thinking the images are real. If the other neural net is able to tell the generated images are false
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