Earlier this year, Nervana Systems CEO Naveen Rao was asked what would happen if Intel began attacking the fast-growing market for chips designed specifically for running "deep learning" software. Now, Rao will be a key player in Intel's attempt to catch up in one of the most promising new silicon markets to emerge since the smartphone. Intel revealed Tuesday that it is buying Nervana and its deep-learning hardware and software for an undisclosed amount. The acquisition marks a departure for Intel and comes at a crucial moment. The company became the world's largest chip maker with a single-minded strategy to make its x86 microprocessors the standard for running a huge swath of applications, from solitaire to massive payroll systems.

The manufacturing hub for the electronics industry, Kunshan, in Jiangsu province, is seeking a drastic reduction in labour costs as it undergoes a makeover after an industrial explosion killed 146 people in 2014. The county, one-seventh the size of neighbouring Shanghai and the mainland's first county to achieve US 4,000 per capita income, was adjudged the best county for its economic performance by Forbes for seven years in a row. However, the blaze, blamed on poor safety standards and haphazard industrialisation, dented Kunshan's pride. More than a year on, the county, which attracts much of its investment from Taiwan, is trying to reinvent its growth strategy. It is accelerating growth by replacing humans with robots and encouraging start-ups.

As more cyber- threats arise every day, extensive research into prevention and detection schemes is being conducted globally. One of the issues faced is keeping up with the sheer mass of new emerging threats online. Traditional detection schemes are rule or signature based. The creation of a rule or signature relies on prior knowledge of the threats structure, source and operation, making it impossible to stop new threats without prior knowledge. Manually identifying all new and disguised threats is too time-consuming to be humanly possible.

Recent years have seen Artificial Intelligence (AI) become the next big thing, or one of the next big things on a technological level and it is starting to have serious social consequences. The advent of Machine to Machine (M2M) communications and the rapid development of the Internet of Things (IoT) are changing industrial processes, from production to distribution, and bringing robotics and automation into every industry. We are entering the "age of complexity"., where computers can optimize processes based by crunching massive data sets. Uber is preparing to add self-driving cars to its current fleet in Pittsburgh, while the Rio Olympics were partly covered by Artificial Intelligence (as openly admitted by the Washington Post for example). You can relax, we're not quite there yet. The legal framework around automated transportation is still widely debated and Facebook bots are widely known to be, at best, irrelevant.

Breast cancer is the most common cancer in the UK, with one in eight women receiving the terrifying diagnosis in their lifetime. But researchers have now developed artificial intelligence software that can accurately predict breast cancer risk, which would enable doctors to closely monitor those most at risk of developing the potentially life-threatening disease. The AI program reliably interprets mammograms and translates patient data into diagnostic information 30 times faster than a human doctor, with 99 per cent accuracy. It was developed by researchers at Houston Methodist Research Institute in Texas. "This software intelligently reviews millions of records in a short amount of time, enabling us to determine breast cancer risk more efficiently using a patient's mammogram," said Stephen T Wong, chair of the Department of Systems Medicine and Bioengineering at the institute.

Human-robot strike teams, autonomous land mines, and covert swarms of minuscule robotic spies: the US Department of Defense's idea of the future of war seems like a sci-fi movie. According to Engadget, it took a while for the U.S. military to perfect its defense strategies against cyberattacks and it seems that when it comes to artificial intelligence (AI), U.S. military faces a similar deficit. Other countries, especially U.S. rivals such as China and Russia, implement less restrictive policies that deal with killer robots and other lethal AI autonomous weapons. This is one of the reasons U.S. cannot afford to be left behind. The nation's military may need deadly AI technology before it's too late.

Technological revolutions have long animated economic history. The concept of "creative destruction"--in which technological advancement destroys certain sectors of the economy while giving rise to new ones--has roots in some of the earliest economic thought.1 This process hinges on the idea that machines serve to supplement human labor, primarily labor dedicated to repetitive physical and cognitive tasks. At the moment, machines can solve intensive well-defined tasks but for the most part cannot be expected to define problems nor identify and traverse particularly complex systems without human oversight. Robots: A Retrospective The most primitive economies are essentially brawn-based. Human labor is largely priced by the ability to perform physical tasks associated with farming and building. A number of studies (e.g., Thomas and Strauss, 1997) show how in modern-day less-developed economies, men make more than woman as a function of body mass and thus perceived brawn, and that men with more brawn made more than those with less.

Welcome to California Inc., the weekly newsletter of the L.A. Times Business section. Pharmaceutical company Mylan is still in the news after hiking the price of life-saving EpiPens by more than 400%. But keep this in mind: Of roughly 250 million raised for and against 17 ballot measures coming before California voters in November, more than a quarter of that amount -- about 70 million -- has been contributed by deep-pocketed drug companies to defeat the Drug Price Relief Act, which would limit drug prices charged to state healthcare programs. Spending on the measure could set a state record over coming weeks. No buzz kill: New federal rules for small commercial drones go into effect Monday.

NASA scientists are engineering a form of artificial intelligence (AI) that they hope will help firefighters and other first responders escape dangerous situations. Set to launch next year, the system will help first responders through unpredictable fires and chemical leaks by giving them advice based on machine learning of past emergencies. The new system--called AUDREY--the Assistant for Understanding Data through Reasoning, Extraction and sYnthesis--is designed to be distributed to individual firefighters so it can collect a precise network of data directly from the field, and learn from that data for next time. No emergency is the same, which means first responders have to rely on extensive training and experience to stay safe in dangerous conditions that can change rapidly. The AUDREY system hopes to use distributed data collection and machine learning to better inform first responders about the situation at hand.

Machine learning is nipping at the heels of conventional physical modeling of air quality predictions in more and more places. The latest is Johannesburg, South Africa, where computer engineer Tapiwa M. Chiwewe at the newly opened IBM Research lab is adapting IBM's air quality prediction software to local needs and adding new capabilities. The work is an expansion of the so-called Green Horizons initiative, in which IBM researchers partnered with Chinese government researchers and officials, starting two years ago. Last month, Chiwewe presented some of the Johannesburg lab's first results, involving ground-level ozone level predictions, at the 14th International Conference on Industrial Informatics in Poitiers, France. "You can do a lot of physics to understand how ozone is found in different places," he says, "but what we did is we just collected a lot of data and trained these machines on it and they were able to predict [local ozone levels] without any knowledge of how ozone works in the atmosphere."