Fredkin University Professor of Artificial Intelligence and Machine Learning Tom Mitchell has been elected to the American Academy of Arts and Sciences. Tom Mitchell, the Fredkin University Professor of Artificial Intelligence and Machine Learning at Carnegie Mellon University, has been elected to the American Academy of Arts and Sciences (AAAS), joining the world's most accomplished scholars, scientists, writers, artists and civic leaders. Mitchell founded the world's first Machine Learning Department at CMU's School of Computer Science in 2006 and led the department until earlier this year. His research focuses on statistical learning algorithms for understanding natural language text and on understanding how the human brain represents information. His work has been featured in The New York Times and on CBS's "60 Minutes."

While incumbent RDMS providers like Oracle and big data platforms like Hadoop don't face an immediate threat, they ignore this technological trend at their peril." Kurt blew out the search engines with this fascinating piece, Do GPU optimized databases threaten the hegemony of Oracle, Splunk and Hadoop?. For non-techies, the gist is that "hardware-optimized" software and databases is becoming a force. As Kurt says, he doesn't use the "R" word lightly – and he's not talking R the language, he's talking revolution, fueled by the looming specialization of cloud hardware, Meanwhile, Jessica posted fresh customer views on big data from her stint at the recent Hadoop Summit in Dublin (Talking Big Data culture at Hadoop Summit with Royal Mail, Centrica, Markel). Skill-building, which doesn't need to be the province of data science PhDs alone. But IT has to shift perception and make a transformative impact. As Centrica put it, "What this journey has done for my team, and for me as an IT manager, is modernise the culture of work.

This year won't be the year of artificial intelligence (AI). But it's coming and artificial intelligence will be transformational. That summarizes the sentiments of Barry Diller, the chairman and senior executive of both Expedia and IAC/InterActiveCorp, who says that machine learning and artificial intelligence will combine to manage companies' big data troves and there will be layers of innovation "tacked onto distribution systems." He argued that there won't be a single next big thing but there will be several tracks, including mobile, machine learning, artificial intelligence and big data. Examples of artificial intelligence applications to personalize services, such as "Connie" the robot concierge that's being piloted at some Hilton properties, are appearing at a modest pace, and Diller thinks it will take a generation or two before the impact is truly felt with "unimaginable consequences," potentially both positive and negative.

Artificial intelligence and machine learning are everywhere these days. We've seen human resources software makers employing AI to help managers optimize employee satisfaction. We've seen healthcare and financial services companies considering the use of robots to find clinical trial patients and detect instances of insider trading, respectively. We've even seen chatbots spewing hate speech on Twitter. Now the online contract review platform LawGeex is pioneering the use of AI to evaluate business contracts.

A new center to study the implications of artificial intelligence and try to influence its ethical development has been established at the U.K.'s Cambridge University, the latest sign that concerns are rising about AI's impact on everything from loss of jobs to humanity's very existence. The Leverhulme Trust, a non-profit foundation that awards grants for academic research in the U.K., on Thursday announced a grant of 10 million ( 15 million) over ten years to the university to establish the Leverhulme Centre for the Future of Intelligence. The new facility will be directed by Professor Huw Price, the university's Bertrand Russell Professor of Philosophy. Others on the team include political scientists, lawyers, psychologists and technologists, said Prof. Gordon Marshall, the director of the Leverhulme Trust. The Trust sprang out of a company that now is part of Unilever.

It was a weeknight, after dinner, and the baby was in bed. My wife and I were alone--we thought--discussing the sorts of things you might discuss with your spouse and no one else. I was midsentence when, without warning, another woman's voice piped in from the next room. "I HELD THE DOOR OPEN FOR A CLOWN THE OTHER DAY," the woman said in a loud, slow monotone. It took us a moment to realize that her voice was emanating from the black speaker on the kitchen table. We stared slack-jawed as she--it--continued: "I THOUGHT IT WAS A NICE JESTER." Was that," I said after a moment of stunned silence. Alexa, the voice assistant whose digital spirit animates the Amazon Echo, did not reply. She--it--responds only when called by name. Or so we had believed. We pieced together what must have transpired. Somehow, Alexa's speech recognition software had mistakenly picked the word Alexa out of something we said, then chosen a phrase like "tell me a joke" as its best approximation of whatever words ...

Concrete uses for artificial intelligence (A.I.) continue to multiply as the technology improves. Earlier this week EdTech reported that Gradescope has raised 2.6 million from investors. The firm, started as a side project at U.C. Berkeley in 2012, makes software that helps STEM professors grade exam questions on handwritten tests. The company currently operates on a "freemium" model -- grading simple exam questions is free, while the A.I. feature (which is still a few months away) will be available for a price. "We want a feature that will offer the convenience of a Scantron without watering down the questions," said cofounder Arjun Singh.

In this special guest feature, Vasco Pedro, CEO and Co-Founder of Unbabel, discusses the importance of machine translation for natural languages and how it currently lacks the quality companies demand for their content. Dr. Pedro' company is Unbabel, the Y Combinator-backed startup that combines crowdsourced human translation and machine learning to deliver fast translation services to businesses with human tone and nuance. Vasco previously worked for Google helping to develop technology for data computation and language at scale, and served as a research faculty member at the Technical University of Lisbon. Vasco holds a PhD in Language Technologies from Carnegie Mellon University in the field of computational semantics. Additionally, Vasco is a Fulbright Scholar, mentor, and advisor to a number of startups on top of being a serial entrepreneur.

This topic describes how to load machine learning (ML) models that have been built using Spark MLlib and stored in Azure Blob Storage (WASB), and how to score them with datasets that have also been stored in WASB. It shows how to pre-process the input data, transform features using the indexing and encoding functions in the MLlib toolkit, and how to create a labeled point data object that can be used as input for scoring with the ML models. The models used for scoring include Linear Regression, Logistic Regression, Random Forest Models, and Gradient Boosting Tree Models. You need an Azure account and an HDInsight Spark cluster to begin this walkthrough. See the Overview of Data Science using Spark on Azure HDInsight for these requirements, for a description of the NYC 2013 Taxi data used here, and for instructions on how execute code from a Jupyter notebook on the Spark cluster.

I create a lot of data everyday. Most of this data, like step counts, messenger chats or GPS tracking from my phone, is useful in its own way but completely disconnected from the larger context in which I generate it. Was I busy or relaxed? Was I running to a meeting or in the same place all day? Was it snowing and zero degrees or was it a nice spring day?