Smartphone and tablet computer apps are seeing increasing use in Japanese schools as teachers look to capitalize on what has become many young people's preferred window to the world. Artificial intelligence-assisted apps have become prevalent in education, particularly in subjects many Japanese teachers struggle to teach well. One subject educators need help with is teaching English, a task that will become all the more important when speaking ability enters the joint achievement test in 2020, part of Japan's high-pressure university entrance exams. Nippon Sports Science University Kashiwa High School in Chiba Prefecture uses an app called TerraTalk to help students improve their English conversation skills. The school introduced the app last summer for use by students planning to study abroad.

Regardless of where you stand on the matter of Data Science sexiness, it's simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it. Drawing on their vast stores of employment data and employee feedback, Glassdoor ranked Data Scientist #1 in their 25 Best Jobs in America list. So the role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers -- and the companies that hire them -- Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress. While having a strong coding ability is important, data science isn't all about software engineering (in fact, have a good familiarity with Python and you're good to go). Data scientists live at the intersection of coding, statistics, and critical thinking.

Machine learning is needed for tasks that are too complex for humans to code directly. Some tasks are so complex that it is impractical, if not impossible, for humans to work out all of the nuances and code for them explicitly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve. Let's look at these 2 examples: Then comes the Machine Learning Approach: Instead of writing a program by hand for each specific task, we collect lots of examples that specify the correct output for a given input. A machine learning algorithm then takes these examples and produces a program that does the job. The program produced by the learning algorithm may look very different from a typical hand-written program.

Student radicalization on college campuses is a growing concern and the same process is ongoing even in high schools now. Educational institutions often are wonderful places for lively discussion about the world, however at times students can become ill informed via online sources. While computers are supposed to be used in such institutions for educational purposes, students often surf the web there. There are also cases where personal devices (laptops and mobile devices) are used at school for browsing activity. What would seem like a harmless activity may be isolating, polarizing, and radicalizing students.

As advanced machines and computers become more and more proficient at picking investments, diagnosing disease symptoms, and conversing in natural English, it is difficult not to wonder what the limits to their capabilities are. This is why many observers believe in technology's potential to disrupt our economy--and our civilization--is unprecedented. Over the past few years, my conversations with students entering the workforce and the business leaders who hire them have revealed something important: to stay relevant in this new economic reality, higher education needs a dramatic realignment. Instead of educating college students for jobs that are about to disappear under the rising tide of technology, twenty-first-century universities should liberate them from outdated career models and give them ownership of their own futures. They should equip them with the literacies and skills they need to thrive in this new economy defined by technology, as well as continue providing them with access to the learning they need to face the challenges of life in a diverse, global environment. Higher education needs a new model and a new orientation away from its dual focus on undergraduate and graduate students.

Today's debate about artificial intelligence and the future of the workforce often centers on dire warnings about how robots are going to steal people's jobs. Albeit anxiety-inducing, these conversations raise critical questions about the role of artificial intelligence and advanced robotics within the labor market -- and the number and quality of jobs for people in the future. But as it turns out, it's unlikely that most jobs will be taken over by robots or AI agents in their entirety. The McKinsey Global Institute estimates, for example, that about ⅓ of activities can be fully automated for some sixty percent of jobs. So, while some portion of the necessary skills may be automated, the remainder of the tasks required to perform the job may very well stay the same.

IN A POST entitled "Machine Learning: Bane or Blessing for Mankind?", I noted that the renowned theoretical physicist Stephen Hawking along with his colleagues Stuart Russell, Max Tegmark, and Frank Wilczek recommend moving cautiously in the development of artificial intelligence (AI), especially in the area of autonomous weapon systems. Hawking and his colleagues understand, however, that the AI genie has already been released from the bottle and there is no way to get it back in. After noting Hawking's concerns, Ron Neale comments, "Such a warning about the application of AI and its derivative intelligent machines (IMs), especially in the area of military application, might be appropriate. But what if IMs are really just a new branch on the tree of evolution that has led us from the original Protists to where we are today?"

Artificial Intelligence (AI) has stopped being just a thing of Sci-Fi novels and movies. From self-driving cars and grocery shopping without cash registers (Amazon Go), to algorithms that detect diseases and speech recognition that allows us to have conversations with robots (Apple's Siri, for example) artificial intelligence is everywhere. And the near future will have more and more of it. Perhaps AI is not spread into education as much as it is in other fields, but this doesn't mean the future's not bright. A flower that blooms later can become as beautiful -- if not even more beautiful -- than the others.

At least 15 trusts in England are said to be interested in new machine learning software designed to support the diagnosis of heart disease, which its developer is planning to offer for free to the NHS. The machine learning algorithm, developed by Oxford-based start-up Ultromics, analyses echocardiogram images for signs of disease. The system is said to be capable of spotting warning signs that might be missed by a clinician, so reducing the risk of a patient suffering a heart attack or other complications. Ultromics hit the headlines over the festive period after it was reported its machine learning software could be rolled out to NHS trusts for free starting this summer. CEO Ross Upton suggested the technology could save the NHS £300 million a year by reducing the number of people who are incorrectly sent for heart surgery, or are otherwise given the all-clear and later suffer a heart attack that requires treatment.

Let's make one thing clear: one year isn't going to fix decades of gender discrimination in computer science and all the problems associated with it. Recent diversity reports show that women still make up only 20 percent of engineers at Google and Facebook, and an even lower proportion at Uber. But after the parade of awful news about the treatment of female engineers in 2017--sexual harassment in Silicon Valley and a Google engineer sending out a memo to his coworkers arguing that women are biologically less adept at programming, just to name a couple--there is actually reason to believe that things are looking up for 2018, especially when it comes to AI. At first glance, AI would seem among least likely areas of programming to be friendly to women. Writing in Fast Company recently, Hanna Wallach, an AI researcher and cofounder of the Women in Machine Learning Conference, said that only 13.5 percent of those working in machine learning are female. In the midst of the #MeToo movement, researchers in artificial intelligence also dealt with sexual harassment allegations, as well as complaints that inappropriate jokes were made at a parties around NIPS, a major industry conference.