Google is putting DeepMind's machine learning to work on managing their sprawling data centers' energy usage, and it's is performing like a boss -- the company reports a 15% drop in consumption since the AI took over. Google is undeniably a huge part of western civilization. The company's data servers pretty much handle all of my mail at this point, along with YouTube, social media platforms and much more. But even so, it's easy to forget that the Google we know and interact with every day is just the tip of the iceberg; it relies on huge data servers to process, transfer and store information -- and all this hardware needs a lot of power. So much power, in fact, that the company decided to do something about it.

Next month Las Vegas will host the Final Event of the DARPA Cyber grand Challenge as an all-computer cyber-defence Capture the Flag tournament. From an initial field of over 100 applicant seven teams will compete for the 3.5 million prize pool. As we reported at the time, DARPA announced this contest in October 2013. "to vastly improve the speed and effectiveness of IT security against escalating cyber threats." These AI systems would be designed to compete in CTF (Capture the Flag) contests, speed-driven bug hunting tournaments where experts reverse-engineer software, probe its weaknesses, search for deeply hidden flaws and create securely patched replacements.

The Intergovernmental Panel on Climate Change (IPCC) highlights six main lines of evidence for climate change. First, we have tracked (see chart) the unprecedented recent increase in the amount of atmospheric carbon dioxide and other greenhouse gases since the beginning of the industrial revolution. By burning coal, oil, and natural gas, we accelerate the process, releasing vast amounts of carbon (carbon that took millions of years to accumulate) into the atmosphere every year.

When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. A computer algorithm issues orders to buy a stock and floods the market with hundreds or thousands of apparently separate orders to buy the same stock. Other algorithms take note of this sudden demand and start raising their buy and sell offers, confident that the market is demanding a higher price. The first algorithm registers this response and sells its shares of stock for the newly higher price, making a tidy profit.

When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. A computer algorithm issues orders to buy a stock and floods the market with hundreds or thousands of apparently separate orders to buy the same stock. Other algorithms take note of this sudden demand and start raising their buy and sell offers, confident that the market is demanding a higher price. The first algorithm registers this response and sells its shares of stock for the newly higher price, making a tidy profit.

Some of society's brightest minds have warned that artificial intelligence (AI) may lead to dangerous unintended consequences, yet leaders of the U.S. intelligence community--with its vast budgets and profound capabilities--have yet to decide who within these organizations is responsible for the ethics of their AI creations. When a new capability is conceived or developed, the intelligence community does not assign anyone responsibility for anticipating how a new AI algorithm may go awry. If scenario-based exercises were conducted, the intelligence community provides no guidelines for deciding when a risk is too great and a system should not be built and assigns no authority to make such decisions. Intelligence agencies use advanced algorithms to interpret the meaning of intercepted communications, identify persons of interest and anticipate major events within troves of data too large for humans to analyze. If artificial intelligence is the ability of computers to create intelligence that humans alone could not have achieved, then the U.S. intelligence community invests in machines with such capabilities.

Video surveillance is one of the fastest growing segments in the physical security industry. In the prevailing security environment, the need for video surveillance is growing exponentially. From smart cities to stadiums, from retail mega-markets to homes, video surveillance has become a pervasive phenomenon. Several petabytes of video data are being generated globally every year from this growing number of video surveillance installations. However, a large amount of video which is captured is never analyzed for actionable intelligence and, in many cases, a large team of human operators is required to monitor the video feeds.

To predict and prevent fraud online even more quickly than cybercriminals adopt new tactics, Sift Science has raised 30 million in a Series C round of venture funding in a round led by Insight Venture Partners. According to the U.S. Internet Crime Complaint Center (IC3) 2015 annual report, reported internet crimes alone, ranging from personal and corporate data breaches to credit card fraud, phishing and identity, theft cost victims 1.07 billion. The financial losses to U.S. businesses as a result of such crimes go well beyond what is reported to IC3, of course. Certain types of sites and apps are under more frequent attacks than others, with digital gift card businesses, money transfer services and on-demand marketplaces rampant with fraud attempts. Sift Science uses machine learning and artificial intelligence to automatically surmise whether an attempted transaction or interaction with a business online is authentic or potentially problematic.

Two years ago, Google spent over half a billion dollars for the tiny artificial intelligence startup DeepMind. Since then, the unit has walloped Atari video games and beaten an impossible board game. But those AI demonstrations have yet to spell actual revenue. Until now -- although the efforts are helping Google save money on its most expensive part. DeepMind chief Demis Hassabis told Bloomberg that his unit recently began applying its advanced AI to Google's data centers, finding ways to reduce the company's sizable energy bill.

Companies working on self-driving cars need to focus on safety -- "we want people who start a trip to finish it," Transportation Secretary Anthony Foxx announced Tuesday, saying his department will issue new guidelines on the vehicles this summer. "Autonomous doesn't mean perfect," he told attendees at an industry conference in San Francisco. "We need industry to take the safety aspects of this very seriously." Foxx's remarks come in the wake of May's fatal crash involving a Tesla Model S sedan being used in semi-autonomous "autopilot" mode. The car crashed into a truck that the autopilot feature did not sense, killing the car's driver. The Transportation Department has been working with Google, BMW, General Motors and other companies developing driverless and partly autonomous cars to adapt existing safety rules to the new technologies.