Over 50 million k-12 students will go back to school in the US this month. For many of them using a school computer, every word they type will be tracked. Under the Children's Internet Protection Act (CIPA), any US school that receives federal funding is required to have an internet-safety policy. As school-issued tablets and Chromebook laptops become more commonplace, schools must install technological guardrails to keep their students safe. For some, this simply means blocking inappropriate websites.

Citrine is a company that builds data infrastructure and predictive data analysis software for the materials industry. Machine learning is a key tool in our toolbox. I have had a few professors and students in materials departments ask me (1) how machine learning could help in their research; and (2) how to quickly come up to speed in machine learning without going back to school for a degree in computer science. While a variety of machine learning courses and how-tos exist on the web already (see here, here, or here), none are specific to the field of materials science. I think the best way to master a new concept is by directly applying it, so this tutorial will show you how to build a machine learning-based model of a canonical solid-state materials property: band gap.

You've got a classroom filled with middle school students working out math problems on computers. Which students are knocking them out with ease? In a pilot test of math software at David E. Williams (DEW) Middle School in Coraopolis, Pa., emojis tell the smart-glasses-wearing teacher what she needs to know. A smiling emoji hovers over a student's head: That means the student is progressing nicely. A frown emoji indicates struggle.

On Thursday, Udacity announced a new AI-based Nanodegree. Developed in partnership with WorldQuant, an international asset management firm, "Artificial Intelligence for Trading" will help learners bring machine learning to financial trading. Until recently, most banks have relied on historical data to map out future market trends. Computer modeling and machine learning algorithms, however, allow analysts to test millions of different scenarios to determine which will lead to the best outcomes. The course comprises of two three-month terms.

The space serves as the hub for summer programs in computer science run by the California-based company iD Tech Camps. In one room, a group of children, ages seven to nine, knelt on the carpet next to small white robots, which they were learning to program with handheld tablets. Nearby, other kids worked on laptops, recording YouTube videos or designing video games. While some planned to return the following week, several told me they were squeezing in a few days of programming instruction before heading off to sleepaway camp or on family vacations. Kids don't learn much coding in school, which can leave them unprepared to tackle computer science in college or in a career.

Artificial intelligence promises to make hiring an unbiased utopia. Employee referrals, a process that tends to leave underrepresented groups out, still make up a bulk of companies' hires. Recruiters and hiring managers also bring their own biases to the process, studies have found, often choosing people with the "right-sounding" names and educational background. Across the pipeline, companies lack racial and gender diversity, with the ranks of underrepresented people thinning at the highest levels of the corporate ladder. Fewer than 5 percent of chief executive officers at Fortune 500 companies are women-and there are only three black CEOs.

In March 2018, my collaborator Neus Lorenzo and I had the privilege of hosting a symposium and a workshop at the annual Mobile Learning Week, an event co-sponsored by UNESCO and the International Telecommunication Union (ITU). UNESCO is the educational, scientific, and cultural organization of the United Nations. The ITU, also a UN agency, coordinates telecommunications, spectrum allocations, and policy positions on information and communication services which, it seems, the world no longer knows how to live without. The event made for an amazing week, during which we were able to interact with some of the smartest people from all over the world on subjects connected to all kinds of learning in so many different cultural contexts, but all associated with the major question of mobility. A subtheme that ran through many of the interventions, including our own, was Artificial Intelligence (AI).

Japan's education ministry is planning to place English-speaking artificial intelligence robots in schools to help children improve their English oral communication skills. Japanese students are generally not good at writing in English or speaking the language. Curriculum guidelines that are due to be fully implemented in 2 years will focus on nurturing those skills. In April, the ministry will launch the robot initiative on a trial basis at about 500 schools nationwide. Some schools have already adopted similar robots to enable students to have fun while honing their English pronunciation and conversation skills.

For the past year, I've been working on implementing well known model architectures and building web applications, so I have a fair amount of refreshing to do when coming back to theoretical machine learning. A lot of it has to do with understanding machine learning's underlying mathematics rigorously, to be able to reason with the field and validate radically new architectures. To that end, I've put together a short syllabus that I'll be personally going through to review some Math Keep in mind there are a lot of excellent resources out there. I'll no doubt be updating with a better guide as I work through this material over the next few weeks. Having a fundamental understanding of mathematics is absolutely necessary to being able to reason with ML productively.

How did OpenAI's team of 5 neural networks manage to beat some of the world's best DOTA 2 players? In this video, I'll explain in detail the cutting edge research techniques OpenAI used to create such an incredible AI algorithm, and how it could be used in the real world. These techniques include Long Short Term Memory Recurrent Neural Networks, Proximal Policy Optimization, and a custom rollout system they've dubbed'Rapid'. That's what keeps me going. Sign up for the next course at The School of AI: https://www.theschool.ai