News, Views & Insights
Today we celebrate the arrival of the 36th (x.x) version of the Wolfram Language and Mathematica: Version 14.1. We’ve been doing this since 1986: continually inventing new ideas and implementing them in our larger and larger tower of technology. And it’s always very satisfying to be able to deliver our latest achievements to the world.
Today we celebrate a new waypoint on our journey of nearly four decades with the release of Version 14.0 of Wolfram Language and Mathematica. Over the two years since we released Version 13.0 we’ve been steadily delivering the fruits of our research and development in .1 releases every six months. Today we’re aggregating these—and more—into Version 14.0.
Sound classification can be a hard task, especially when sound samples have small variations that can be imperceptible to the human ear. The use of machines, and recently machine learning models, has been shown to be an effective approach to solving the problem of classifying sounds. These applications can help improve diagnoses and have been a topic of research in areas such as cardiology and pulmonology. Recent innovations such as a convolutional neural network identifying COVID-19 coughs and the MIT AI model detecting asymptomatic COVID-19 infections using cough recordings show some promising results for identifying COVID-19 patients just by the sound of their coughs. Looking at these references, this task may look quite challenging and like something that can be done only by top-notch researchers. In this post, we will discuss how you can get very promising results using the machine learning and audio functionalities in the Wolfram Language.
Explore the contents of this article with a free Wolfram System Modeler trial. System dynamics (SD) is a very powerful and flexible modeling paradigm, ideally suited to tackle strategic business, economics and public policy issues. Some years ago, Guido Wolf Reichert, management consultant and developer for BSL Management Support, Germany, stumbled into problems while modeling the public transport system in a German metropolis. He had reached the technical limits of the established SD software. While looking for alternatives, he discovered Wolfram System Modeler.
In the days of online learning and remote work, students are gaining valuable skills to help them navigate their education. With new needs for greater independence in their learning journeys, better time management and finding communities in unexpected places, students are already encountering important lessons. Student-centered events like the Wolfram Emerging Leaders Program help foster success in the workplace and in life, no matter what happens in the world.
Editor’s note: The following post is based on the 2020 Advent of Code challenge, “an Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like.” Guest author Philip Maymin delves into why he chose to use the Wolfram Language to solve these queries with a how-to on learning the language.
心臓病学のような医療分野でも，Wolfram言語は新たな知見獲得や臨床現場における予測に役立っています．最近，金沢大学先進予防医学研究科の中嶋憲一教授がリードする，Wolfram言語の機械学習機能を用いた心不全による死亡リスク予測の研究に参加し，論文が出版されましたのでその概略を紹介します．そこでは，末期心不全(HFD)と重度の不整脈/突然死(ArE)によって引き起こされる心臓死の確率を区別できる分類器を構築することを目指しました.
The past year of learning ushered in a variety of new experiences for instructors and students alike, and the United States Military Academy at West Point was no exception. In addition to masks in the classroom, reduced class sizes to allow for social distancing, rigorous testing and tracing efforts, and precautionary remote video classes, we have also needed to adjust aspects of our teaching styles. While such adjustments were voluntary, to enhance the discussion I chose to teach several lessons outside under large white tents and even in stadium bleachers to safely enable larger conversations with my cadets. Sometimes this meant carrying a large whiteboard with a tripod out to the stadium. At other times it meant putting quiz-style questions on a website so that students could submit answers via forms that were easier to grade while allowing everyone to work at a safe distance on individual devices.
Today’s handheld devices are powerful enough to run neural networks locally without the need for a cloud server connection, which can be a great convenience when you’re on the go. Deploying and running a custom neural network on your phone or tablet is not straightforward, though, and the process depends on the operating system of the machine. In this post, I will focus on iOS devices and walk you through all the necessary steps to train a custom image classifier neural network model using the Wolfram Language, export it through ONNX (new in Version 12.2), convert it to Core ML (Apple’s machine learning framework for iOS apps) and finally deploy it to your iPhone or iPad.
Cars are getting smarter and more connected, yet how much have you explored the technology that helps run our vehicles? I was curious to see how I could connect to my vehicle’s communication center and what kind of interface I could create in Wolfram Notebooks to report on the data gathered.
I’ll begin this blog post by admitting that I personally have forgotten many passwords in my lifetime. If you’re like me (which I’m sure you are in this regard), you use many online tools and websites that require a login and password. We also know it’s wise practice to use a variety of passwords and to change them frequently. We hope a new feature of Mathematica Online has made this a little less daunting for you in your educational settings.