Our first big application area was math (hence the name “Mathematica”). And we’ve kept pushing the frontiers of what’s possible with math. But over the past 30 years, we’ve been able to build on the framework that we defined in Mathematica 1.0 to create the whole edifice of computational capabilities that we now call the Wolfram Language—and that corresponds to Mathematica as it is today.

From when I first began to design Mathematica, my goal was to create a system that would stand the test of time, and would provide the foundation to fill out my vision for the future of computation. It’s exciting to see how well it’s all worked out. My original core concepts of language design continue to infuse everything we do. And over the years we’ve been able to just keep building and building on what’s already there, to create a taller and taller tower of carefully integrated capabilities.

It’s fun today to launch Mathematica 1.0 on an old computer, and compare it with today.

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Neural nets have generated a lot of interest recently, and rightly so: they form the basis for state-of-the-art solutions to a dizzying array of problems, from speech recognition to machine translation, from autonomous driving to playing Go. Fortunately, the Wolfram Language now has a state-of-the-art neural net framework (and a growing tutorial collection). This has made possible a whole new set of Wolfram Language functions, such as FindTextualAnswer, ImageIdentify, ImageRestyle and FacialFeatures. And deep learning will no doubt play an important role in our continuing mission to make human knowledge computable.

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Researchers from the University of Illinois at Urbana-Champaign and the National Center for Supercomputing Applications Gravity Group expand their novel Deep Filtering method that uses GPU-powered neural networks for anomaly detection and classification of gravitational waves in LIGO data.

“Classification and Clustering of LIGO Data with Deep Transfer Learning”: Accepted for publication in Physical Review D in April 2018

This article shows that deep learning methods can automatically detect and group together anomalies in data from LIGO detectors by using artificial intelligence algorithms based on neural networks that were already pre-trained to classify photographs of real-world objects.

Furthermore, this research shows pre-trained neural networks can be used as feature extractors for unsupervised clustering algorithms to facilitate finding entirely new and unknown classes of glitches and anomalies in gravitational waves without human supervision.

Pre-trained computer vision neural networks were obtained from the Wolfram Neural Net Repository and trained on NVIDIA DGX-1 with Tesla V100 and NVIDIA Tesla P100 GPUs.

“Deep Learning for Real-Time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data”: Published in Physics Letters B in March 2018

This article shows for the first time that deep learning can detect true gravitational wave signals in real LIGO data. It is shown that neural networks can be used in realistic detection scenarios and can learn to adapt to the non-Gaussian and non-stationary behavior of real LIGO data. To demonstrate this novel detection method, the researchers showed their method can correctly identify and estimate the properties of gravitational wave detection.

“Deep Neural Networks to Enable Real-Time Multimessenger Astronomy”: Published in Physical Review D in February 2018

The foundation article on deep learning for gravitational wave detection, this paper can be rightly regarded as the textbook reference that established for the first time the power of deep learning to outperform other gravitational wave analysis methods in terms of both accuracy and speed.

This article shows for the very first time that deep convolutional neural networks can match the sensitivity of matched-filtering searches for detecting signals in noisy time series data.

The Challenges typically have the form: “Write a function to do X”. But because we’re using the Wolfram Language—with all its built-in computational intelligence—it’s easy to make the X be remarkably sophisticated.

The site has a range of levels of Challenges. Some are good for beginners, while others will require serious effort even for experienced programmers and computational thinkers. Typically each Challenge has at least some known solution that’s at most a few lines of Wolfram Language code. But what are those lines of code?

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Additional details about this release are available here.

Read through Stephen Wolfram’s launch-day blog post to learn more, or see the Wolfram Language Quick Revision History for a complete list of new features and enhancements.

Since 2012, SystemModeler has been used in a wide variety of fields with an even larger number of goals—such as optimizing the fuel consumption of a car, finding the optimal dosage of a drug for liver disease and maximizing the lifetime of a battery system. The Version 5.1 update expands SystemModeler beyond its previous usage horizons to include a whole host of options, such as:

• Exporting models in a form that includes a full simulation engine, which makes them usable in a wide variety of tools
• Providing the right interface for your models so that they are easy for others to explore and analyze
• Sharing models with millions of users with the simulation core now included in the Wolfram Language

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Additional details about this release are available here.