Yuuta Banda poses for a photo with OriHime, his robot avatar. When Yuuta Banda was just four years old, he suffered a car accident that left him paralyzed, connected to a respiratory machine, and confined to bed for life. But almost two decades later, he's been able to experience different places, and even find a job thanks to OriHime, his robot avatar. "At first I couldn't understand what was so great about OriHime, but I gradually learned through using it that [the robot] afforded people with a sense of presence," Banda told me in an email. "I felt a greater sense of satisfaction as I spoke with people in different places to me through the robot."

Illinois Structured Learning Package (Illinois-SL) is a general purpose JAVA library for performing structured learning. It houses learning algorithms like averaged Structured Perceptron and Structured SVM with L2-Loss, and provides a minimal interface for your structured learning needs. The training algorithm employed for training SSVM is dual coordinate descent(DCD), which has been proven to have very good convergence properties. Illinois-SL comes with an efficient implementation of DCD with support for multi-threading. Illinois-SL provides a simple and neat framework for developing applications using structured prediction models.

In a world where digital tools support virtually every part of our lives, why is it that the full power of such tools has yet to be unleashed to those who might benefit most - educators and learners? In the latest of its series of publications concerning digital learning, Intelligence Unleashed: An Argument for AI in Education, Pearson, in collaboration with the UCL Knowledge Lab, maps out how artificial intelligence in education (AIEd) can be used to create learning tools that are more efficient, flexible and inclusive than those currently available; tools that will help learners prepare for an economy that is swiftly being reshaped by digital technologies. The authors, led by Professor Rose Luckin of the UCL Knowledge Lab, highlight existing and emergent technology that could be leveraged to address some of the most intractable issues in education, including achievement gaps. For example, technology available today could be applied to support student learning at a scale previously unimaginable by providing one-on-one tutoring to every student, in every subject. Existing technologies also have the capacity to provide intelligent support to learners working in a group, and to create authentic virtual learning environments where students have the right support, at the right time, to tackle real-life problems and puzzles.

Over the past few years the field of Machine Learning has entered the general parlance. From free massive open online courses to image recognition benchmarks being broken and decades of Atari games being mastered. During the same period developers have witnessed the release of several popular open source frameworks and libraries. The chart below shows different open source machine learning projects by initial commit date and programming language. The size represents the popularity of a project based on number of Github stargazers.

The assumption that a college education should take four years is baked into American culture. Colleges in the colonial days were founded on the premise of a four-year degree, a concept imported from Europe. Harvard University experimented with a three-year degree when it was founded in 1636, but the test was short-lived, and the four-year degree has been the standard ever since. We expect students to enter college at 18 and leave when they turn 22, and we worry about those who take a more circuitous route to graduation. But we need to reconsider that long-established, one-size-fits all model.

As we put the finishing touches on what promises to be another outstanding Hardcore Data Science Day at Strata Hadoop World in New York, I sat down with my co-organizer Ben Recht for the the latest episode of the O'Reilly Data Show Podcast. Recht is a UC Berkeley faculty member and member of AMPLab, and his research spans many areas of interest to data scientists including optimization, compressed sensing, statistics, and machine learning. At the 2014 Strata Hadoop World in NYC, Recht gave an overview of a nascent AMPLab research initiative into machine learning pipelines. The research team behind the project recently released an alpha version of a new software framework called KeystoneML, which gives developers a chance to test out some of the ideas that Recht outlined in his talk last year. We devoted a portion of this Data Show episode to machine learning pipelines in general, and a discussion of KeystoneML in particular.

Robots are taking all the jobs. But are we, the average, moderately skilled humans, screwed, or aren't we? Let me just get it out of the way now: We are, unless there are drastic, immediate changes to education and economic systems around the world. The dominant narrative going around today about Pew Research's new report on artificial intelligence and the future of jobs is that experts can't really decide whether automation is going to make working obsolete, that it's really a toss up whether robots will simply create new jobs in other sectors as they destroy ones in other. That's true, in one sense: The 1,896 futurists, CEOs, journalists, and university professors questioned for the report were split in half over robots will "displace significant numbers of both blue- and white-collar workers," with 52 percent of respondents agreeing that "human ingenuity will create new jobs, industries, and ways to make a living, just as it has been doing since the dawn of the Industrial Revolution."

Tree methods are commonly used in data science to understand patterns within data and to build predictive models. The term Tree Methods covers a variety of techniques with different levels of complexity but my aim is to highlight three I find useful. To set the problem up let's assume we have a census dataset containing age, education, employment status and so on. Given all this information we want to see if we can predict whether a person earns more than 50k per year. How can tree methods help us?

During my last interview I had a great talk with Daniel McDuff. Daniel's research is at the intersection of psychology and computer science. He is interested in designing hardware and algorithms for sensing human behavior at scale, and in building technologies that make life better. Applications of behavior sensing that he is most excited about are in: understanding mental health, improving online learning and designing new connected devices (IoT). Listen to more about why it is important to collect data from much larger scales and help computers read our emotional state. Key Learning Points: 1. Understanding the impact, intersection, and meaning of Psychology and Computer Science 2. Facial Expression Recognition 3. How to define Artificial Intelligence, Deep Learning, and Machine Learning 4. Applications of behavior sensing with Online Learning, Health, and Connected Devices 5. Visual Wearable sensors and heart health 6. The impact of education and learning 7. How to build computers to measure phycology, our reactions, emotions, etc 8. Daniel is building and utilizing scalable computer vision and machine learning tools to enable the automated recognition and analysis of emotions and physiology. He is currently Director of Research at Affectiva, a post-doctoral research affiliate at the MIT Media Lab and a visiting scientist at Brigham and Womens Hospital. At Affectiva Daniel is building state-of-the-art facial expression recognition software and leading analysis of the world's largest database of human emotion responses. Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR, the Center for Integrated Medicine and Innovative Technology (CIMIT) and several IEEE conferences. His work has been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine. Daniel has been named a 2015 WIRED Innovation Fellow.

As a challenge problem for AI systems, I propose the use of hand-constructed multiple-choice tests, with problems that are easy for people but hard for computers. Specifically, I discuss techniques for constructing such problems at the level of a fourth-grade child and at the level of a high-school student. For the fourth grade level questions, I argue that questions that require the understanding of time, impossible or pointless scenarios, of causality, of the human body, or of sets of objects, and questions that require combining facts or require simple inductive arguments of indeterminate length can be chosen to be easy for people, and are likely to be hard for AI programs, in the current state of the art. For the high-school level, I argue that questions that relate the formal science to the realia of laboratory experiments or of real-world observations are likely to be easy for people and hard for AI programs.