News, Views & Insights
The Drumbeat of Releases Continues… Just under six months ago (176 days ago, to be precise) we released Version 14.1. Today I’m pleased to announce that we’re releasing Version 14.2, delivering the latest from our R&D pipeline. This is an exciting time for our technology, both in terms of what we’re now able to implement, […]
Nearly a year and a half ago—just a few months after ChatGPT burst on the scene—we introduced the first version of our Chat Notebook technology to integrate LLM-based chat into Wolfram Notebooks. For the past year and a half we’ve been building on those foundations. And today I’m excited to be able to announce that we’re releasing the fruits of those efforts: the first version of our Wolfram Notebook Assistant.
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.
On December 21, 2020, a visual astronomical spectacle will occur. The planets Jupiter and Saturn will pass so close to each other in the sky that, to the unaided eye, they will be difficult to separate. This is the closest the two planets have come in 397 years; the last time they were this close was July 16, 1623. When Jupiter and Saturn come close to each other in the sky as seen from Earth, this is known as a “great conjunction” and happens about every 20 years or so. But not all great conjunctions are as close as this one. The next great conjunction will be on November 5, 2040, and again on April 10, 2060, but the planets will be a bit over a degree apart, so not as close as the 2020 event. The next comparable event will be on March 15, 2080.
Yet Bigger than Ever Before When we released Version 12.1 in March of this year, I was pleased to be able to say that with its 182 new functions it was the biggest .1 release we’d ever had. But just nine months later, we’ve got an even bigger .1 release! Version 12.2, launching today, has […]