A Tesla Model S electric vehicle is shown in San Francisco, California, U.S., April 7, 2016. The first U.S. fatality using self-driving technology took place in May when the driver of a Tesla S sports car operating the vehicle's "Autopilot" automated driving system died after a collision with a truck in Florida, federal officials said Thursday. The government is investigating the design and performance of Tesla's system. Tesla said on its website that neither the driver nor the Autopilot noticed the white side of the trailer, which was perpendicular to the Model S, against the brightly lit sky, and neither applied the brakes. "The high ride height of the trailer combined with its positioning across the road and the extremely rare circumstances of the impact caused the Model S to pass under the trailer," the company said.

We learned yesterday evening that NHTSA is opening a preliminary evaluation into the performance of Autopilot during a recent fatal crash that occurred in a Model S. This is the first known fatality in just over 130 million miles where Autopilot was activated. Among all vehicles in the US, there is a fatality every 94 million miles. Worldwide, there is a fatality approximately every 60 million miles. It is important to emphasize that the NHTSA action is simply a preliminary evaluation to determine whether the system worked according to expectations. Following our standard practice, Tesla informed NHTSA about the incident immediately after it occurred.

Federal regulators are investigating Tesla's autopilot feature after a fatal crash involving a tractor trailer and one of its Model S cars. The U.S. National Highway Traffic Safety Administration opened the investigation after a man was killed while driving a Model S with the self-driving mode engaged. "This is the first known fatality in just over 130 million miles where Autopilot was activated," Tesla said in a statement Thursday. It called the incident a "tragic loss." The car was on a divided highway when a tractor trailer apparently turned in front of it.

Computer vision has been having a moment. No more does an image recognition algorithm make dumb mistakes when looking at the world: these days, it can accurately tell you that an image contains a cat. But the way it pulls off the party trick may not be as familiar to humans as we thought. Most computer vision systems identify features in images using neural networks, which are inspired by our own biology and are very similar in their architecture--only here, the biological sensing and neurons are swapped out for mathematical functions. Now a study by researchers at Facebook and Virginia Tech says that despite those similarities, we should be careful in assuming that both work in the same way.

The probe by the National Highway Traffic Safety Administration may become a setback for the growing number of tech and car companies investing heavily in autonomous driving technology. Regulators scrambled last year to write new rules for self-driving cars after Tesla announced hasty plans to release its limited Autopilot feature. But by then, the company had already sent ripples through the auto industry. In January, at the Consumer Electronics Show -- more or less the Detroit Auto Show of tech -- it seemed nearly every major car company unveiled some kind of autonomous feature. The first death in a self-driving car could stoke fears over the technology and temper the industry's growth. The majority of traffic accidents, which result in about 35,000 deaths in the U.S. every year, are caused by human error.

Artificial intelligence that can understand and answer any work-related question it is asked has been made available in the UK for the first time. The computer software, called Starmind, uses machine learning to understand queries, then source answers from previous staff conversations on a subject or track down experts within the company who are able to help. Its creators refer to it as "brain technology", adding its aim is to become a central knowledge bank within any company, an instant database of information that can be accessed by anyone. Starmind co-founder Pascal Kaufmann said of the technology: "Thousands of human brains connected can outsmart any machine today. "But if you can find ways for humans and AI (artificial intelligence) inspired technologies to autonomously collaborate rather than focusing on ways for them to compete, you can bring out the best in both." The algorithm within the system, which was developed in Switzerland, becomes more powerful the more it is used and is able to build a map of the people in a business and the areas in which all of them are experts, or are able to provide relevant information. "Starmind acts like an artificial hyper brain that seamlessly exists at the core of a company," Mr Kaufmann added. "The algorithm is then fuelled by the know-how stored inside the brains of everyone that engages with the system." Several major companies in Europe, including UBS and Bayer are already using the system. A new version of the software - called Starmind NOW - has also been launched which enables the software to be accessed outside of company intranet for the first time. Starmind says this makes the technology more "intuitive and seamless" to use. Former Microsoft executive Peter Waser has also joined the company as CEO. "It's a new technology that has never been available on the market in this form," he said. "Brain technology is the latest technology in the megatrend of machine learning and artificial intelligence.

The quantitative explosion in digital data stemming from the surge in Internet communication and the widespread use of sensors is today a major driver of business opportunities for companies. In this new world, a great deal of ink is being spilled on the subject of progress in'machine learning'. This increasingly common expression denotes families of algorithms which enable computer-aided systems to accumulate knowledge and intelligence automatically without being explicitly programmed to do so. Machine learning methods have applications in a wide range of fields including the manufacturing industries (process optimisation), the finance sector (risk management), the luxury goods and wider online markets (strategic marketing), defence (situational analysis) and in the biomedical sector (patient typology). However, 'machine learning' is far from foolproof and if applied to the economic or political field it looks certain to raise some major issues.

What was the evolution of your interest in machine learning, and how did you zero-in on your Ph.D. work? Ilya Sutskever: I started my Ph.D. just before deep learning became a thing. I was working on a number of different projects, mostly centered around neural networks.

The TensorFlow project is bigger than you might realize. The fact that it's a library for deep learning, and its connection to Google, has helped TensorFlow attract a lot of attention. Cool stuff, but--especially for someone hoping to explore machine learning for the first time--TensorFlow can be a lot to take in. Let's break it down so we can see and understand every moving part. We'll explore the data flow graph that defines the computations your data will undergo, how to train models with gradient descent using TensorFlow, and how TensorBoard can visualize your TensorFlow work. The examples here won't solve industrial machine learning problems, but they'll help you understand the components underlying everything built with TensorFlow, including whatever you build next!

First it was whimsical poems, then full-length movies. But, now artificial intelligence is writing sports articles. The Associated Press announced it is expanding the publication's coverage to include Minor League Baseball and will use automated software to cover the 10,000 games. This AI reporter is capable of analyzing data from the games, pulling out the most important highlights to formulate a well-constructed and informative stories. The Associated Press announced it is expanding the publication's coverage to include Minor League Baseball and will use automated software to cover the 10,000 games, like the Altoona Curve.