"Human plus machine isn't the future, it's the present," Garry Kasparov said in a recent TED talk. And this "present" is transforming the world of education at a rapid pace. With children increasingly using tablets and coding becoming part of national curricula around the world, technology is becoming an integral part of classrooms, just like chalk and blackboards. We have already witnessed the rise and impact of education technology especially through a multitude of adaptive learning platforms such as Khan Academy and Coursera that allow learners to strengthen their skills and knowledge. And now virtual reality (VR) and artificial intelligence (AI) are gaining traction.

There have been a lot of surveys over the past few years on the educational background of data scientists. As a result, there have also been many different results. In the O'Reilly Data Science Salary Survey of 2014, about 28% of the respondents had a Bachelor's degree, while 44% had a Master's degree and 20% had a Ph.D. Common fields that data scientists have as backgrounds are mathematics/Statistics, Computer Sciences, and Engineering. The results that are represented in the infographic are from 2016. They are very similar to the ones of the O'Reilly survey.

Pamela Fox, Woebot's chief technology officer, center, talks to Alison Darcy, Woebot's founder and chief executive, at the offices of the start-up in San Francisco. Pamela Fox, Woebot's chief technology officer, center, talks to Alison Darcy, Woebot's founder and chief executive, at the offices of the start-up in San Francisco. Fifty years ago, an MIT professor created a chatbot that simulated a psychotherapist. Named Eliza, it was able to trick some people into believing it was human. But it didn't understand what it was told, nor did it have the capacity to learn on its own.

If you're plugged into the tech industry, you'll know that two things have been making consistent waves in many areas over the past few years; machine learning and Python. What happens when you combine the new gold standard programming language with the most significant tech development in areas such as financial trading, online search, digital marketing and even data and personal security (among others)? This course will show you what's what, and get you started on becoming a machine learning guru. If you have a desire to learn machine learning concepts and have some previous programming or Python experience, this course is perfect for you. If you're more of a beginner than an intermediate, don't worry; each module starts with theory to explain upcoming concepts.

Funding for artificial intelligence startups has surged more than eightfold since 2012, and entrepreneurs are rushing to capitalize on the cash that's still sitting on the sidelines. Money is gravitating toward the area as tech giants such as Alphabet Inc. and Facebook Inc. acquire companies and push advancements. Venture capital firms including Andreessen Horowitz, New Enterprise Associates Inc., and Battery Ventures LP announced Tuesday they have injected $140 million into artificial intelligence startup Databricks Inc., raising the company's funding to $247 million. While the sector saw just $559 million in funding in 2012, it surged to almost $5 billion in 2016, according to data compiled by research firm CB Insights. The number of deals has also seen a substantial increase, rising from 150 to 698 in that same time frame.

The acquisition of Apprento by M-Files, along with its recently-announced partnership with ABBYY, a global provider of innovative language-based and artificial intelligence technologies, illustrates the company's focus on automating and simplifying the way business professionals manage information and related processes. "Business leaders, industry analysts and others who follow our industry all agree that traditional approaches and solutions for managing information are inadequate and that a new and more intelligent approach is required," said Miika Mäkitalo, CEO at M-Files. "Our acquisition of Apprento coupled with our recent partnership with ABBYY reinforces our commitment to deliver human-like intelligence to the massive volume of unstructured content that resides within disconnected systems and repositories in the typical enterprise." "By incorporating the Apprento Business Context Engine into the M-Files platform, we're delivering powerful artificial intelligence capabilities to current M-Files users as well as setting the foundation for the introduction of many more AI enhancements and tools in the near future," said Trevor Cookson, CEO at Apprento. "I'm honored to join the forward-thinking M-Files team and I'm looking forward to working with them to simplify and improve how people manage and interact with information."

Last week was the high point of the Dota 2 competitive year: it was the week of The International, Valve's biggest tournament. On Saturday, Team Liquid walked away with more than $10 million after defeating Newbee 3-0 in the grand final. Right now, one of the requirements to be a good Dota 2 player is that you've got to be a living, breathing human. The game does include some basic computer-controlled bots to practice against, but any seasoned player of the game should have no trouble prevailing over these bots, even on their hardest "Unfair" difficulty (though the Unfair Viper bot is a legendary jerk that's utterly miserable to play against). Last Friday, however, we got a hint of a new, altogether more threatening kind of computer-controlled player: an AI-controlled bot built by Elon Musk's OpenAI.

Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor. Also there is often a gap between statistical guarantee and computational efficiency for existing tensor clustering solutions. In this article, we aim to bridge this gap by proposing a new dynamic tensor clustering method, which takes into account both sparsity and fusion structures, and enjoys strong statistical guarantees as well as high computational efficiency. Our proposal is based upon a new structured tensor factorization that encourages both sparsity and smoothness in parameters along the specified tensor modes. Computationally, we develop a highly efficient optimization algorithm that benefits from substantial dimension reduction. In theory, we first establish a non-asymptotic error bound for the estimator from the structured tensor factorization. Built upon this error bound, we then derive the rate of convergence of the estimated cluster centers, and show that the estimated clusters recover the true cluster structures with a high probability. Moreover, our proposed method can be naturally extended to co-clustering of multiple modes of the tensor data. The efficacy of our approach is illustrated via simulations and a brain dynamic functional connectivity analysis from an Autism spectrum disorder study.

We present a new method of generating mixture models for data with categorical attributes. The keys to this approach are an entropy-based density metric in categorical space and annealing of high-entropy/low-density components from an initial state with many components. Pruning of low-density components using the entropy-based density allows GALILEO to consistently find high-quality clusters and the same optimal number of clusters. GALILEO has shown promising results on a range of test datasets commonly used for categorical clustering benchmarks. We demonstrate that the scaling of GALILEO is linear in the number of records in the dataset, making this method suitable for very large categorical datasets.

This paper considers applications of trajectory data in transportation, and makes two primary contributions. First, it provides a comprehensive literature review detailing ways in which trajectory data has been used for transportation systems analysis, distilling existing research into the following six areas: demand estimation, modeling human behavior, designing public transit, measuring and predicting traffic performance, quantifying environmental impact, and safety analysis. Additionally, it presents innovative applications of trajectory data for the state of Maryland, employing visualization and machine learning techniques to extract value from 20 million GPS traces. These visual analytics will be implemented in the Regional Integrated Transportation Information System (RITIS), which provides free data sharing and visual analytics tools to help transportation agencies attain situational awareness, evaluate performance, and share insights with the public.