Artificial Intelligence (AI) and Machine Learning (ML) are some of the hottest topics right now. The term "AI" is thrown around casually every day. You hear aspiring developers saying they want to learn AI. You also hear executives saying they want to implement AI in their services. But quite often, many of these people don't understand what AI is.

Schools may be critiqued as "factories," but robots aren't going to replace human teachers any time soon. Still, that doesn't mean that artificially intelligent systems won't transform education just as they are changing a variety of fields and practices, from the way oncologists diagnose cancer to how lawyers analyze cases. Intelligent-tutoring systems like ALEKS (for Assessment and LEarning in Knowledge Spaces), Cognitive Tutor, and a new program in development by IBM's Watson initiative are starting to expand in K-12 education, and experts argue that teachers need new training not only to use intelligent systems in the classroom but also to prepare students for careers in increasingly technology-integrated fields. "Any skill that a computer can teach is going to be done by a computer in the workplace, and that's something people don't think about enough," said Christopher Dede, an education and technology professor at the Harvard Graduate School of Education. For that reason, he said, teachers can use computer programs not simply to replace pieces of their instruction, but to model for students how to work with technology professionally.

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Over the past year, I've spent most of my working time doing deep learning research and internships. And a lot of that year was making very big mistakes that helped me learn not just about ML, but about how to engineer these systems correctly and soundly. One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time. However, there doesn't seem to be a solid tutorial online on how to actually write unit tests for neural network code. Even places like OpenAI only found bugs by staring at every line of their code and try to think why it would cause a bug.

In recent years, MIT scholars have helped develop a whole lexicon of science and math terms for use in Haiti's Kreyòl language. Now a collaboration with Google is making those terms readily available to anyone -- an important step in the expansion of Haitian Kreyòl for education purposes. The new project, centered around the MIT-Haiti Initiative, has been launched as part of an enhancement to the Google Translate program. Now anyone using Google Translate can find an extensive set of Kreyòl terms, including recent coinages, in the science, technology, engineering, and math (STEM) disciplines. "In the past five or six years, we've witnessed quite a paradigm shift in the way people in Haiti talk about and use Kreyòl," says Michel DeGraff, a professor of linguistics at MIT and director of the MIT-Haiti Initiative.

Technology is hard to predict especially beyond the short-term, and so it is difficult to argue one way or another whether robots will "take all of our jobs", as they say. However, I think there is a bit of a paradox buried in the idea that the jobless future will happen: if robots in the future are so great that they will replace most human jobs, then they will also be good at teaching humans how to complement robots. Humans have in the past been displaced by machines in many jobs, and over time we focus on learning skills and picking careers take the new technological landscape into consideration. This means learning how best to complement machines. For example, farmers learned to drive the tractors that replaced their manual labor in the field.

Low-rank tensor regression, a new model class that learns high-order correlation from data, has recently received considerable attention. At the same time, Gaussian processes (GP) are well-studied machine learning models for structure learning. In this paper, we demonstrate interesting connections between the two, especially for multi-way data analysis. We show that low-rank tensor regression is essentially learning a multi-linear kernel in Gaussian processes, and the low-rank assumption translates to the constrained Bayesian inference problem. We prove the oracle inequality and derive the average case learning curve for the equivalent GP model. Our finding implies that low-rank tensor regression, though empirically successful, is highly dependent on the eigenvalues of covariance functions as well as variable correlations.

One of the main challenges of deep learning methods is the choice of an appropriate training strategy. In particular, additional steps, such as unsupervised pre-training, have been shown to greatly improve the performances of deep structures. In this article, we propose an extra training step, called post-training, which only optimizes the last layer of the network. We show that this procedure can be analyzed in the context of kernel theory, with the first layers computing an embedding of the data and the last layer a statistical model to solve the task based on this embedding. This step makes sure that the embedding, or representation, of the data is used in the best possible way for the considered task. This idea is then tested on multiple architectures with various data sets, showing that it consistently provides a boost in performance. One of the main challenges of the deep learning methods is to efficiently solve the highly complex and non-convex optimization problem involved in the training step.

A drone captured video of a boat capsizing in Florida and a daring rescue by a nearby 13-year-old surfer. A drone captured video of a boat capsizing in Jupiter Inlet, Florida, and a daring rescue in the rough water by a nearby 13-year-old surfer. The drone pilot, Kevin Cadby, was flying his drone at Jupiter Inlet to capture video of the water and the boats, as he occasionally does. He saw the boat coming in from far out and decided to follow the boat with his drone, he said. "The wind was blowing in at 20 miles per hour, that inlet can be treacherous," Cadby said.

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