Japan aims to make a computer terminal available to every school student by around fiscal 2025, the education ministry said Tuesday. The target is included in the ministry's new plan to improve the educational environment through the use of technology. A ministry survey in March 2018 found that computers were distributed on average at a rate of 1 terminal per 5.6 students at public elementary and high schools across the country. By prefecture, Saga performed best with a rate of 1 terminal per 1.8 students, while Saitama saw the worst rate of 1 terminal per 7.9 students. To further increase the number of computers at schools, the ministry's plan showed examples of how computers can be procured at lower costs.
Beyond the classroom curriculum, many law schools are designing experiential modes of introducing law students to artificial intelligence. At Georgia State University School of Law, for instance, the Legal Analytics and Innovation Initiative gives law students a chance to collaborate closely with computer science and business students at the same university to design complex technologies that solve previously unsolvable legal problems (such as predicting to a high degree of accuracy how a particular judge will rule in cases defined by a large set of parameters). This kind of work not only has the potential to be a flow-through to the legal practitioner space, but could over time become a mechanism for law schools to "spin out" the kinds of revenue-generating start-up businesses that are a common facet of life science departments at research universities. These programs have also been shown (according to the programs' own statistics) to help law students land jobs at higher rates than the overall student body, no doubt because the intersection of technology and law is a rare and valuable skillset in the eyes of employers.
Northside Hospital in Atlanta is adopting machine learning technology to enable the organization to predict when insurance companies will end payments. The new technology it's using is from The SSI Group, which is providing technology that aggregates all remittance data coming through its clearinghouse to make the predictions. The goal is to enable providers that manually build their own spreadsheets to predict payments to use the SSI technology to determine when they can expect to get paid, down to the day and time, according to the vendor. "Without predictive analytics, hospitals and other providers are left guessing when they will receive payments," says Eric Nilsson, chief technology officer at SSI. Using analytics, SSI can give greater visibility on the payment of institutional, professional, in-patient and out-patient claims.
This story was co-published with ProPublica. Ariella Russcol specializes in drama at the Frank Sinatra School of the Arts in Queens, New York, and the senior's performance on this April afternoon didn't disappoint. While the library is normally the quietest room in the school, her ear-piercing screams sounded more like a horror movie than study hall. But they weren't enough to set off a small microphone in the ceiling that was supposed to detect aggression. A few days later, at the Staples Pathways Academy in Westport, Connecticut, junior Sami D'Anna inadvertently triggered the same device with a less spooky sound--a coughing fit from a lingering chest cold.
Online education provider Udacity said today it's launching a nanodegree program to teach product managers how to create AI-powered products. The nontechnical course will also teach product managers how to identify business opportunities with AI or machine learning. Enrollment for the first program begins today and consists of 6 lessons and 3 projects, and lasts about 2 months. "Students will start off by learning the foundations of AI and machine learning, starting with the unsupervised and supervised models that are used in industry today," Udacity founder Sebastian Thrun told VentureBeat in an email. "As a next step, they will learn how to use Figure Eight's data annotation platform to develop a labeled dataset for supervised learning. Finally, students will develop a business proposal for an AI product of their choice, while learning strategies for continuously learning and updating a machine learning model."
This article summarizes the lessons learned after two years of our team engaging with dozens of enterprise clients from different industries including manufacturing, financial services, retail, entertainment, and healthcare, among others. What are the most common ML problems faced by the enterprise? What is beyond training an ML model? How to address data preparation? How to scale to large datasets?
If Bill Gates were to drop out of Harvard University and start a new company today, it would be one that focuses on artificial intelligence, he said in an interview on Monday. The perspective shows that the Microsoft co-founder hasn't lost interest in the technology industry where his company has operated for the past 44 years. "Given my background, I would start an AI company whose goal would be to teach computers how to read, so that they can absorb and understand all the written knowledge of the world. That's an area where AI has yet to make progress, and it will be quite profound when we achieve that goal," Gates told David Rubinstein at an Economic Club of Washington event in the nation's capital on Monday. Gates has invested in Luminous, a start-up developing silicon for AI.
Python is a prevalent programming language in machine learning (ML) community. A lot of Python engineers and data scientists feel the lack of engineering practices like versioning large datasets and ML models, and the lack of reproducibility. This lack is particularly acute for engineers who just moved to ML space. We will discuss the current practices of organizing ML projects using traditional open-source toolset like Git and Git-LFS as well as this toolset limitation. Thereby motivation for developing new ML specific version control systems will be explained.
Microsoft was one of the earliest companies to begin discussing and advocating for an ethical perspective on artificial intelligence. The issue began to take off at the company in 2016, when CEO Satya Nadella spoke at a developer conference about how the company viewed some of the ethical issues around AI, and later that year published an article about these issues. Nadella's primary focus was on Microsoft's orientation toward using AI to augment human capabilities and building trust into intelligent products. The next year, Microsoft's R&D head Eric Horvitz partnered with Microsoft's president and chief legal officer Brad Smith to form Aether, a cross-functional committee addressing AI and ethics in engineering and research. With these foundations laid, in 2018, Microsoft established a full-time position in AI policy and ethics.
A data scientist is a "person who is better at statistics than any software engineer and better at software engineering than any statistician". In Top 10 Coding Mistakes Made by Data Scientists we discussed how statisticians can become a better coders. Here we discuss how coders can become better statisticians. Detailed output and code for each of the examples is available in github and in an interactive notebook. The code uses data workflow management library d6tflow and data is shared with dataset management library d6tpipe.