It's easy to do bad things with Facebook data. From targeting ads for bizarrely specific T-shirts to manipulating an electorate, the questionable purposes to which the social media behemoth can be put are numerous. But there are also some people out there trying to use Facebook for good--or, at least, to improve the diagnosis of mental illness. On December 3, a group of researchers reported that they had managed to predict psychiatric diagnoses with Facebook data--using messages sent up to 18 months before a user received an official diagnosis. The team worked with 223 volunteers, who all gave the researchers access to their personal Facebook messages.

Dr. Zoe Danielle Birnbaum and James Matthew Frankel are to be married Feb. 9 by Rabbi Jeffrey Sirkman at Tappan Hill Mansion in Tarrytown, N.Y. The bride and groom graduated from Colgate. Dr. Birnbaum, 30, is a third-year resident in the field of psychiatry at NYU Langone Medical Center, and received a medical degree from N.Y.U. She is a daughter of Dr. Lisa Turtz and Jesse Birnbaum of Larchmont, N.Y. The bride's father is a member of the quality assurance team at the Mahwah, N.J., manufacturing facility of Nobel Biocare, the Swiss-based maker of dental implants and individualized prosthetics.

In a clandestine, 40,000-square-foot cultivation facility in northern Arizona, we met with the founders of CEAD, Royce Birnbaum and Adam Klaasmeyer, a company that develops artificial intelligence applications for the cannabis industry. These two are no strangers to innovation. Birnbaum, the lead back-end developer for the project, has had a career in developing systems for monitoring nuclear reactors for the Navy and AI technology for the defense industry, while Klaasmeyer, the front-end developer and coder, has contributed to projects for companies such as Atari and Microsoft. The CEAD technology currently being tested at a cannabis research and development center in Arizona monitors operational and environmental systems including plant nutrition, growth rates, life cycles, and predictive pest outbreaks. In addition, it keeps a log of all movements made by the cultivation team and gathers data in regards to specific feeding and pruning schedules.

Companies must choose whether humans or machines should get the last word on employee performance. Digital tools and technologies are now relentlessly and remorselessly transforming how performance management works. Customized and continuous data-driven feedback is becoming a new normal for enterprises worldwide. This feedback appears both qualitatively and quantitatively superior to its performance review precursors and should lead to better outcomes. But does AI-flavored feedback require a human touch to measurably improve its impact?

At dawn on 17 March the inhabitants of Los Angeles were woken by a mild tremor. Less than three minutes later the Los Angeles Times website published an initial piece on the subject, at first sight a wire drafted in haste by a press agency: "A shallow magnitude 4.7 earthquake was reported Monday morning five miles [8km] from Westwood, California, according to the US Geological Survey. The temblor occurred at 6.25am Pacific time at a depth of 5.0 miles. According to the USGS, the epicentre was six miles from Beverly Hills, California, seven miles from Universal City, California, seven miles from Santa Monica, California, and 348 miles from Sacramento, California. In the past 10 days, there have been no earthquakes magnitude 3.0 and greater centred nearby. This information comes from the USGS Earthquake Notification Service and this post was created by an algorithm written by the author."

Most approaches to semantics in computational linguistics represent meaning in terms of words or abstract symbols. Grounded-language research bases the meaning of natural language on perception and/or action in the (real or virtual) world. Machine learning has become the most effective approach to constructing natural-language systems; however, current methods require a great deal of laboriously annotated training data. Ideally, a computer would be able to acquire language like a child, by being exposed to language in the context of a relevant but ambiguous environment, thereby grounding its learning in perception and action. We will review recent research in grounded language learning and discuss future directions.

Journalists and editors work under pressure to collect relevant details and background information about specific events. They spend a significant amount of time sifting through documents and finding new information such as facts, opinions or stakeholders (i.e. people, places and organizations that have a stake in the news). Spotting them is a tedious and cognitively intense process. One task, essential to this process, is to find and keep track of stakeholders. This task is taxing cognitively and in terms of memory. Tell Me More offers an automatic aid to this task. Tell Me More is a system that, given a seed story, mines the web for similar stories reported by different sources and selects only those stories which offer new information with respect to that original seed story. Much like a journalist, the task of detecting named entities is central to its success. In this paper we briefly describe Tell Me More and, in particular, we focus on Tell Me More's entity detection component. We describe an approach that combines off-the-shelf named entity recognizers (NERs) with WPED, an in-house publicly available NER that uses Wikipedia as its knowledge base. We show significant increase in precision scores with respect to traditional NERs. Lastly, we present an overall evaluation of Tell Me More using this approach.