It was AI versus Warren Buffett. The artificial intelligence was unleashed by Winton, the London hedge fund, to test an old principle of the Berkshire Hathaway Inc. chairman with a view to trading on it: that major acquisitions usually hurt the buyers' shareholders. Researchers collected and analyzed data on almost 9,000 U.S. deals back to the 1960s. Winton says Buffett's thesis doesn't hold up -- big acquisitions don't inherently destroy value. "It prevented us from trading on a false signal and potentially losing money," said Daniel Mitchell, who runs a team of data scientists at the $30 billion hedge fund.

Humans know to silence a ringing phone when they're in a quiet library, and to say "thank you" after someone lends a helping hand. Now, robots will learn this etiquette as well, thanks to a research project that aims to teach robots manners. By teaching robots such social norms, researchers think the machines could more seamlessly interact with humans. The initial stages of the project were recently completed by a team of researchers funded by the Defense Advanced Research Projects Agency, a branch of the Department of Defense dedicated to the development of new military technologies. The researchers studied how humans recognize and react to social norms, and developed a machine-learning algorithm that allows a robot to learn these "manners" by drawing on human data.

Last year, the FBI reported 465,676 entries for missing children in the United States.1 Many of those children are runaways--either from their homes or the care of a social services agency. A nonprofit organization serves as a clearinghouse for critical information that can help find these children. When electronic service providers detect suspicious activity that might be a clue to locate a missing child, they pass the tip on to the organization, where analysts attempt to pinpoint a physical location for the suspected perpetrator and then deliver that information to the proper law enforcement agency as quickly as possible. The volume of tips is massive.

Google will dedicate more than 10,000 staff to rooting out violent extremist content on YouTube in 2018, the video sharing website's chief has said. Writing in the Daily Telegraph, Susan Wojcicki said some users were exploiting YouTube to "mislead, manipulate, harass or even harm". She said the website, owned by Google, had used "computer-learning" technology that could find extremist videos. More than 150,000 of these videos have been removed since June, she said. In March, the UK government suspended its adverts from YouTube, following concerns they were appearing next to inappropriate content.

The government will aim to double the country's labor productivity to 2 percent in the three years through 2020 from 0.9 percent, the average in the five years through 2015, informed sources said. The government is set to include the target in a package of policy measures it plans to adopt on Friday to help realize a "productivity revolution," an initiative designed to ensure sustainable wage growth and overcome deflation, the sources said. The policy package is also expected to call for increasing corporate capital spending by 10 percent in fiscal 2020 from the fiscal 2016 level and achieving wage growth of at least 3 percent every year during the three-year intensive reform period through 2020. The government is set to pledge that it will utilize all policy measures to improve productivity, including the greater use of big data and artificial intelligence technology. Also in the package, the government plans to reduce corporate tax burdens to internationally competitive levels for companies actively boosting their wages and capital expenditures.

We are in the fourth industrial revolution, which is characterized by advances in robotics and self-driving car technology, the proliferation of smart home appliances, and more. At the forefront of all these is artificial intelligence (AI), which is the development of automated computer systems that could match or even surpass humans in intelligence. AI is regarded as the next big thing--so big that future technologies will be dependent on it. But then, do we really know what we are getting ourselves into? Here are ten scary facts about artificial intelligence. Let's assume you're driving down a road.

Evaluating a policy by deploying it in the real world can be risky and costly. Off-policy policy evaluation (OPE) algorithms use historical data collected from running a previous policy to evaluate a new policy, which provides a means for evaluating a policy without requiring it to ever be deployed. Importance sampling is a popular OPE method because it is robust to partial observability and works with continuous states and actions. However, the amount of historical data required by importance sampling can scale exponentially with the horizon of the problem: the number of sequential decisions that are made. We propose using policies over temporally extended actions, called options, and show that combining these policies with importance sampling can significantly improve performance for long-horizon problems. In addition, we can take advantage of special cases that arise due to options-based policies to further improve the performance of importance sampling. We further generalize these special cases to a general covariance testing rule that can be used to decide which weights to drop in an IS estimate, and derive a new IS algorithm called Incremental Importance Sampling that can provide significantly more accurate estimates for a broad class of domains.

In my last blog, I talked about the simplicity of the electric engine compared to the internal combustion engine and how this changes everything. From climate to the structure of the auto industry to the way we store, manage, and distribute energy, electric cars are having tremendous impact. But what I left out of that discussion was the Internet of Things. The fact is, most electric cars are connected cars – connected through the Internet of Things. This means that sensors in the car constantly communicate with mission control (the manufacturer), sending data on the status of components in real time.

When Timnit Gebru was a student at Stanford University's prestigious Artificial Intelligence Lab, she ran a project that used Google Street View images of cars to determine the demographic makeup of towns and cities across the U.S. While the AI algorithms did a credible job of predicting income levels and political leanings in a given area, Gebru says her work was susceptible to bias--racial, gender, socio-economic. She was also horrified by a ProPublica report that found a computer program widely used to predict whether a criminal will re-offend discriminated against people of color. So earlier this year, Gebru, 34, joined a Microsoft Corp. team called FATE--for Fairness, Accountability, Transparency and Ethics in AI. The program was set up three years ago to ferret out biases that creep into AI data and can skew results.

DJ Patil, the first U.S. Chief Data Scientist, has joined the Advisory Board of DataScience.com, "He will be an active advisor to our product and engineering teams, and with his experience at LinkedIn, Greylock and The White House, he really has a pulse on the landscape." But beyond the data-powered advances in artificial intelligence and machine learning, lies an important challenge for both scaling startups and modernizing enterprise companies -- what good is data if you can't find it, deploy it, or make it meaningful for your customers? I asked Mr. Patil all the questions that a CTO, CIO or VP of Engineering would love. But, as Mr. Merchan says, "DataScience is part of a trend toward internal marketplaces of knowledge," so these issues are also important for non-technical executives, and other business leaders to understand." We centered around four key themes that leaders should know about building a sustainable data science program. Forget the data scientist'shortage' -- focus on building the data science function. "First, data science in business is a relatively new phenomenon.