The virtual world is built on physical infrastructure. Every search that gets submitted, email sent, page served, comment posted, and video loaded passes through data centers that can be larger than a football field. Those thousands of racks of humming servers use vast amounts of energy; together, all existing data centers use roughly 2% of the world's electricity, and if left unchecked, this energy demand could grow as rapidly as Internet use. So making data centers run as efficiently as possible is a very big deal. Thankfully, despite skyrocketing demand for computing, data center electricity use has flattened over the past few years, largely due to enormous opportunities to improve efficiency as these facilities scale up.1 But capturing these opportunities can be a very complicated process.

As data collections become larger, exploratory regression analysis becomes more important but more challenging. When observations are hierarchically clustered the problem is even more challenging because model selection with mixed effect models can produce misleading results when nonlinear effects are not included into the model (Bauer and Cai, 2009). A machine learning method called boosted decision trees (Friedman, 2001) is a good approach for exploratory regression analysis in real data sets because it can detect predictors with nonlinear and interaction effects while also accounting for missing data. We propose an extension to boosted decision decision trees called metboost for hierarchically clustered data. It works by constraining the structure of each tree to be the same across groups, but allowing the terminal node means to differ. This allows predictors and split points to lead to different predictions within each group, and approximates nonlinear group specific effects. Importantly, metboost remains computationally feasible for thousands of observations and hundreds of predictors that may contain missing values. We apply the method to predict math performance for 15,240 students from 751 schools in data collected in the Educational Longitudinal Study 2002 (Ingels et al., 2007), allowing 76 predictors to have unique effects for each school. When comparing results to boosted decision trees, metboost has 15% improved prediction performance. Results of a large simulation study show that metboost has up to 70% improved variable selection performance and up to 30% improved prediction performance compared to boosted decision trees when group sizes are small

Our learning task: Given a training set S {(x1 , y1), (x2 , y2), . . . The simplest case: y { 1, 1} called binary classification problem If y is a real number it becomes a regression problem More general case, y can be a vector and each element is drawn from a finite set.

Children learn to speak, as well as recognize objects, people, and places, long before they learn to read or write. They can learn from hearing, seeing, and interacting without being given any instructions. So why shouldn't artificial intelligence systems be able to work the same way? That's the key insight driving a research project under way at MIT that takes a novel approach to speech and image recognition: Teaching a computer to successfully associate specific elements of images with corresponding sound files in order to identify imagery (say, a lighthouse in a photographic landscape) when someone in an audio clip says the word "lighthouse." Though in the very early stages of what could be a years-long process of research and development, the implications of the MIT project, led by PhD student David Harwath and senior research scientist Jim Glass, are substantial. Along with being able to automatically surface images based on corresponding audio clips and vice versa, the research opens a path to creating language-to-language translation without needing to go through the laborious steps of training AI systems on the correlation between two languages' words.

Andrew Moore, the Dean of the Carnegie Mellon School of Computer Science and a former Vice President at Google, just told me something exciting. Moore predicts that 2016 will see a rapid proliferation of research on machine emotional understanding in machines. Robots, smart phones, and computers will very quickly start to understand how we're feeling and will be able to respond accordingly. AI might be a hot topic but you'll still need to justify those projects. "There will be immediate positive uses," he explains over the phone.

Mobile robots can help chronically ill children regain part of the normal school experience. Some kids are unable to attend school for months or even years due to symptoms, treatments, or recovery from serious illness. These homebound children typically continue their education by having make-up work sent home and (depending on resources) studying with tutors for a few hours each week. But they miss out on a key aspect of school: socialization. With today's technology, the definition of face time has changed.

When you think about it, not a lot has changed in the legal world from the days of To Kill A Mockingbird to the latest John Grisham thriller. Sure, literature snobs may insist that Atticus Finch's flawless moral heroism should never be compared to the conflicted protagonists of contemporary legal page-turners, but in terms of the substance of how lawyers do their lawyering, the fundamentals have barely changed in 80 years, from the career track of a young lawyer to the set-up of a law firm. The same cannot be said of virtually any other profession. Indeed, the legal industry seems more dusty than dynamic; the robes and wrinkles that mark those at the top of the field hardly scream modernity. But change is afoot, as a couple of powerful market forces are driving law firms to adopt modern corporate efficiency.

I teach one of the world's most popular MOOCs (massive online open courses), "Learning How to Learn," with neuroscientist Terrence J. Sejnowski, the Francis Crick Professor at the Salk Institute for Biological Studies. The course draws on neuroscience, cognitive psychology, and education to explain how our brains absorb and process information, so we can all be better students. Since it launched on the website Coursera in August of 2014, nearly 1 million students from over 200 countries have enrolled in our class. We've had cardiologists, engineers, lawyers, linguists, 12-year-olds, and war refugees in Sudan take the course. We get emails like this one that recently arrived: "I'll keep it short. I've recently completed your MOOC and it has already changed my life in ways you cannot imagine. I just turned 29, am in the middle of a career change to computer science, and I've never been more excited to learn."

When Pyotr Stolyarsky died in 1944, he was considered Russia' s greatest violin teacher. He counted among his pupils a coterie of stars, including David Oistrakh and Nathan Milstein, and a school for gifted musicians in his native Odessa was named after him in 1933. But Stolyarsky couldn't play the violin anywhere near as well as his best students. What he could do was whisper metaphors into their ears. He might lean over and explain how his mother cooked Sabbath dinner.

Walter Pitts was used to being bullied. He'd been born into a tough family in Prohibition-era Detroit, where his father, a boiler-maker, had no trouble raising his fists to get his way. One afternoon in 1935, they chased him through the streets until he ducked into the local library to hide. The library was familiar ground, where he had taught himself Greek, Latin, logic, and mathematics--better than home, where his father insisted he drop out of school and go to work. Outside, the world was messy. Inside, it all made sense. Not wanting to risk another run-in that night, Pitts stayed hidden until the library closed for the evening. Alone, he wandered through the stacks of books until he came across Principia Mathematica, a three-volume tome written by Bertrand Russell and Alfred Whitehead between 1910 and 1913, which attempted to reduce all of mathematics to pure logic. Pitts sat down and began to read. For three days he remained in the library until he had read each volume cover to cover--nearly 2,000 pages in all--and had identified several mistakes. Deciding that Bertrand Russell himself needed to know about these, the boy drafted a letter to Russell detailing the errors.