While some worry that robots may become our adversaries, Daniel Weld, professor of computer science at the University of Washington, sees them as incredibly beneficial helpers. Self-driving cars, for example, might help prevent 1.3 million road deaths each year. Medical robots might avoid the 250,000 annual deaths from human errors in treatment. But there is work still to be done to make that happen, Weld said in a lecture, "Computational Ethics for AI," March 20. He was a guest lecturer in the series "The Emergence of Intelligent Machines: Challenges and Opportunities." "AIs won't wake up and want to kill us," he said, "but they might hurt us by accident."

This week's top Machine Learning stories, including computational models of drug effectiveness, stem cells, sentiment analysis, and more! Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning!

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This report was originally published on The Data Incubator Blog. You can view the the report in it's entirety here: Ranked 16 R Packages for Machine Learning The most frequently asked question in our data science training program is "what is the best programming language for machine learning?" The resulting discussion, depending on the day, either ends in a hotly contested debate between R, Python, and MATLAB fans, or a full on WWE wrestling match. In other words, it depends. However, there is no doubt R is language of choice for the majority of data scientists who want to understand data, especially those looking to leverage its great machine learning packages. R also boasts being open source which is great for anyone looking to get started with machine learning in their spare time.

In recent years, we have become increasingly good at training deep neural networks to learn a very accurate mapping from inputs to outputs, whether they are images, sentences, label predictions, etc. from large amounts of labeled data. What our models still frightfully lack is the ability to generalize to conditions that are different from the ones encountered during training. Every time you apply your model not to a carefully constructed dataset but to the real world. The real world is messy and contains an infinite number of novel scenarios, many of which your model has not encountered during training and for which it is in turn ill-prepared to make predictions. The ability to transfer knowledge to new conditions is generally known as transfer learning and is what we will discuss in the rest of this post. Over the course of this blog post, I will first contrast transfer learning with machine learning's most pervasive and successful paradigm, supervised learning. I will then outline reasons why transfer learning warrants our attention. Subsequently, I will give a more technical definition and detail different transfer learning scenarios.

Every brand wants to tell a story. But a new narrative analytics platform has launched to help brands figure out the stories that are actually being told about them. The platform is called Protagonist, from a company by the same name. Formerly called Monitor 360, the San Francisco-based firm was spun off three years ago from the consulting firm Monitor Group. Protagonist says its newly released platform is the first "specifically designed to analyze complex, cross-platform data to reveal the underlying beliefs and motivations of consumers." Customers include General Mills, MetLife, Warner Brothers and Microsoft.

Let's take a breath: Robots and artificial intelligence systems are nowhere near displacing the human workforce. Nevertheless, no less a voice than Bill Gates has asserted just the opposite and called for a counterintuitive, preemptive strike on these innovations. His proposed weapon of choice? Taxes on technology to compensate for losses that haven't happened. David Kenny (@davidwkenny) is IBM's senior vice president for Watson and the company's cloud platform.

The ritualized procreation in the novel--effectively, state-sanctioned rape--is extrapolated from the Bible. " 'Behold my maid Bilhah, go in unto her; and she shall bear upon my knees, that I may also have children by her,' " Atwood recited. "Obviously, they stuck the two together and out came the baby, and it was given to Rachel.

When you move to another country as an adult, the language flows around you like a river. Perhaps a child can immediately abandon himself to the current, but most older people will begin by picking out the words and phrases that seem to matter most, which is what I did after my family moved to Cairo, in October of 2011. It was the first fall after the Arab Spring; Hosni Mubarak, the former President, had been forced to resign the previous February. Every weekday, my wife, Leslie, and I met with a tutor for two hours at a language school called Kalimat, where we studied Egyptian Arabic. At the end of each session, we made a vocabulary list. In early December, following the first round of the nation's parliamentary elections, which had been dominated by the Muslim Brotherhood, my language notebook read: On many days, I went to Tahrir Square, to report on the ongoing revolution. If I heard unfamiliar words or phrases, I brought them back to class. The following month, I learned "tear gas," "slaughter," and "Can you speak more slowly?" "Conspiracy theory" appeared in my notebook on the same day as "fried potatoes." Sometimes I wondered about the strangeness of Tahrir-speak, and what my Arabic would have been like if I had arrived ten years earlier. But it would have been different at any time, in any place: you can never step into the same language twice. Even eternal phrases took on a new texture in the light of the revolution. After I could understand some of the radio talk shows that cabbies played, I realized that callers and hosts exchanged Islamic greetings for a full half minute before settling down to heated arguments about the new regime. Our textbook was entitled "Dardasha"--"Chatter"--and it outlined set conversations that I soon carried out with neighbors, using phrases that would never be touched by Tahrir: "May peace, mercy, and the blessings of God be upon you." One of our teachers, Rifaat Amin, prepared a five-page handout entitled "Arabic Expressions of Social Etiquette." This supplemented "Dardasha," which also featured some lessons about social traditions, including the evil eye, the belief that envy can cause misfortune. In "Dardasha," icons of little bombs with burning fuses had been printed next to the kind of phrase that, even during a revolution, qualified as explosive: "Your son is really smart, Madame Fathiya."

JUDY WOODRUFF: The continent of Africa does not now hold many internationally well-known universities, but one man is trying to change that, a one-time Microsoft executive who was educated in the United States, Patrick Awuah. As special correspondent Fred de Sam Lazaro reports, as part of his Agents for Change series, one special focus of classes is to teach Africa's next generation of leaders about ethics. FRED DE SAM LAZARO: It looks like a pretty typical college campus, with students working in computer labs, studying at the library, or hanging out with friends. But Ashesi University, in the West African nation of Ghana, has embarked on an experiment which its founder hopes will help start to fundamentally change the entire continent. PATRICK AWUAH, President, Ashesi University: In the next three decades or so, the population of Africa is going to double, and something like 40 percent of working-age people are going to be Africans in the world.