Do you understand your fiduciary duties? In his prescient 1942 short story, "Runaround," Isaac Azimov proposed a simple set of laws for robots: Yale Law School Professor Jack Balkin has come up with a few more laws for the age of Big Data, when gadgets like Apple's Siri and Amazon's Alexa pose as kindly robot helpers but also collect terabytes of data about your Internet browsing habits, driving patterns and even conversations inside your home. "Think about the basic structure of the problem: We're going to bring into our houses, and use as agents, lots and lots of algorithmic programs," said Balkin, a First Amendment scholar and director of the Information Society Project at Yale Law. "The assumption most people make is they have a relationship with this thing, but what they have is a relationship with the company that makes and sells this thing." And that company may not always have the consumer's best interest in mind.

Greetings from CES, the annual consumer electronics extravaganza in Las Vegas that began as an opportunity for retailers to see what gadgets they'd be buying to sell to their customers in the coming year and has evolved into a see-and-be-seen event for the technology industry's elite. I led a conversation with Andrew Ng, the leader of Chinese Internet giant Baidu's artificial intelligence research unit. Ng is a Stanford professor who helped start Google Brain and co-founded online education firm Coursera. He is one of the world's foremost experts on AI who speaks clearly about the complicated topic in terms businesspeople can relate to. Get Data Sheet, Fortune's technology newsletter, where this essay originated. AI will become so important and is so confusing to most businesspeople that only an expert can help business leaders make sense of it.

Twitter can sometimes feel like a language of its own, but one lecturer is using the social media site as a tool to teach Arabic. In Mahammed Bouabdallah's classes at the University of Westminster, London, students are set simple tasks using Twitter to complement their lessons. Bouabdallah publishes a photo or link and asks students to comment on it in Arabic, or runs a Twitter poll about events happening in Arabic-speaking countries and discusses the results in class. Sometimes, they will use Twitter's built-in translation tool and judge its accuracy. "They have to tweet outside the class, and we discuss it inside the class," says Bouabdallah. His own research suggests that Twitter is popular as a language learning tool, with 80 per cent of surveyed students responding positively to its use.

It's hard to see anything other than artificial intelligence being the next frontier of the edtech front. More specifically, machine learning has quickly gained the potential to overhaul what educators can accomplish with technology due to how rapidly the field has advanced within the last two years. Using machine learning, truly "smart" education solutions that can perform intelligent, self-informed actions are poised to change how we facilitate learning inside and outside of K-12 and higher education classrooms. Here's four ways artificial intelligence is going to do just that: As edtech authority Eric Sheninger (1) has noted, digital tools can boost student collaboration while they grapple with class material. This increased and streamlined student engagement with other students, as well as the subject matter at hand, has been known to produce more holistic and deeper learning.

Abstract--Recent work in distance metric learning has focused on learning transformations of data that best align with specified pairwise similarity and dissimilarity constraints, often supplied by a human observer . The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similarity measures. Here, we address the problem of learning these transformations when the underlying constraint generation process is nonstationary. This nonstationarity can be due to changes in either the ground-truth clustering used to generate constraints or changes in the feature subspaces in which the class structure is apparent. We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD), a general adaptive, online approach for learning and tracking optimal metrics as they change over time that is highly robust to a variety of nonstationary behaviors in the changing metric. We apply the OCELAD framework to an ensemble of online learners. Specifically, we create a retro-initialized composite objective mirror descent (COMID) ensemble (RICE) consisting of a set of parallel COMID learners with different learning rates, and demonstrate parameter-free RICE-OCELAD metric learning on both synthetic data and a highly nonstationary Twitter dataset. We show significant performance improvements and increased robustness to nonstationary effects relative to previously proposed batch and online distance metric learning algorithms. He effectiveness of many machine learning and data mining algorithms depends on an appropriate measure of pairwise distance between data points that accurately reflects the learning task, e.g., prediction, clustering or classification. The kNN classifier, K-means clustering, and the Laplacian-SVM semi-supervised classifier are examples of such distance-based machine learning algorithms. In settings where there is clean, appropriately-scaled spherical Gaussian data, standard Euclidean distance can be utilized. However, when the data is heavy tailed, multimodal, or contaminated by outliers, observation noise, or irrelevant or replicated features, use of Euclidean inter-point distance can be problematic, leading to bias or loss of discriminative power.

I have been an electronics enthusiast ever since I was in elementary school. To put together an electronic device that interacts with the physical world in some way has been my passion and I still remember the excitement I felt when I built my first circuit in 6th grade – even though it was simply something that periodically turned an LED on and off. After earning an undergraduate degree in electronic systems engineering in my home country of Mexico, I came to the U.S. to study for a PhD in Electrical Engineering, before joining IBM in 2006 to work on silicon integrated millimeter wave circuits and systems. I had the honor of joining a team of IBM scientists who were pioneers of the first monolithic millimeter wave radio that exploited portions of the radio spectrum to boost wireless communications. And since then I have been researching how to engineer more and more complex millimeter wave systems.

Is this really the world we live in? After reminding you about the SEG machine learning contest just before Christmas, I thought I could show you how you train a model in a supervised learning problem, then use it to make predictions on unseen data. So we'll just break a simple contest entry down into ten easy steps (note that you could do this on anything, doesn't have to be this problem). Before we start, let's review quickly what a machine learning problem looks like, and introduct a bit of jargon. To begin, we have a dataset (e.g.

In late December 2016, I announced a small gift of 10 Deep Learning books to people interested in or working in AI. This is my way of paying back to the community which has been extremely generous with ideas and code. I asked people to send me an email letting me know their interest in AI and their contributions to the community. Here is the video of the announcement. I received nearly 300 emails from people from around the world -- pretty much every continent other than Antarctica!

I describe here the projects that I worked on, as well as career progress, starting 25 years ago as a PhD student in statistics, until today, and the transformation from statistician to data scientist that occurred slowly and started more than 20 years ago. This also illustrates many applications of data science, most are still active. My interest in mathematics started when I was 7 or 8, I remember being fascinated by the powers of 2 in primary school, and later purchasing cheap russian math books (Mir publisher) translated in French, for my entertainement. In high school, I participated in the mathematical olympiads, and did my own math research during math classes, rather than listening to the very boring lessons. When I attended college, I stopped showing up in the classroom altogether - afterall, you could just read the syllabus, memorize the material before the exam and regurgitate it at the exam.

This is computer-generated wordplay and an example of how the boundaries of artificial intelligence are shifting. If a computer can crack jokes, what other human activities could they start to replicate? What jobs could it take? Artificial intelligence has become an increasingly big issue for education - not least because many tech companies and publishers are circling around the huge commercial opportunities. But could students really get their answers from a robot rather than a teacher?