November 20, 2020 — Koji Maruyama, Sales Engineer

Distinguishing Risks of Modes of Cardiac Death in Heart Failure with Machine Learning

In medical fields like cardiology, the Wolfram Language continues to help researchers make discoveries and predictions. I recently coauthored a study that uses the machine learning functionality of the Wolfram Language to predict risks of deaths due to heart failure. In it, we aimed to build a classifier that is capable of distinguishing the probabilities of cardiac death caused by end-stage heart failure (HFD) and severe arrhythmic events/sudden death (ArE). What follows is a summary of the paper we published earlier this year.

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May 26, 2020 — Sjoerd Smit, Technical Consultant, Wolfram Europe

AI and the Wolfram Language Work toward Partial Automation in the Search for Cancer

NOTE: The following post contains real medical images.

As more technology is folded into medical environments all over the world, Wolfram’s European branch has taken on work with the United Kingdom’s National Health Service (NHS) in an effort to partially automate the process of cancer diagnosis. The task is to use machine learning to avoid checking thousands of similar-looking images of people’s insides by hand for signs of cancer.

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May 19, 2020 — Shadi Ashnai, Manager of Sound & Vision, Algorithms R&D

Version 12.1 of the Wolfram Language introduces the long-awaited Video object. The Video object is completely (and only) out-of-core; it can link to an extensive list of video containers with almost any codec. Most importantly, it is bundled with complete stacks for image and audio processing, machine learning and neural nets, statistics and visualization and many more capabilities. This already makes the Wolfram Language a powerful video computation platform, but there are still more features to explore.

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May 28, 2019 — Daniel Lichtblau, Symbolic Algorithms Developer, Algorithms R&D

Did We Really Write What We Said We Wrote?

Several Months Ago…

I wrote a blog post about the disputed Federalist Papers. These were the 12 essays (out of a total of 85) with authorship claimed by both Alexander Hamilton and James Madison. Ever since the landmark statistical study by Mosteller and Wallace published in 1963, the consensus opinion has been that all 12 were written by Madison (the Adair article of 1944, which also takes this position, discusses the long history of competing authorship claims for these essays). The field of work that gave rise to the methods used often goes by the name of “stylometry,” and it lies behind most methods for determining authorship from text alone (that is to say, in the absence of other information such as a physical typewritten or handwritten note). In the case of the disputed essays, the pool size, at just two, is as small as can be. Even so, these essays have been regarded as difficult for authorship attribution due to many statistical similarities in style shared by Hamilton and Madison.

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May 2, 2019 — Tuseeta Banerjee, Research Scientist, Machine Learning

If you haven’t used machine learning, deep learning and neural networks yourself, you’ve almost certainly heard of them. You may be familiar with their commercial use in self-driving cars, image recognition, automatic text completion, text translation and other complex data analysis, but you can also train your own neural nets to accomplish tasks like identifying objects in images, generating sequences of text or segmenting pixels of an image. With the Wolfram Language, you can get started with machine learning and neural nets faster than you think. Since deep learning and neural networks are everywhere, let’s go ahead and explore what exactly they are and how you can start using them.

Explaining neural networks

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April 11, 2019 — Swede White, Public Relations Manager

Fishackathon

Every year, the U.S. Department of State sponsors a worldwide competition called Fishackathon. Its goal is to protect life in our waters by creating technological solutions to help solve problems related to fishing.

The first global competition was held in 2014 and has been growing massively every year. In 2018 the winning entry came from a five-person team from Boston, after competing against 45,000 people in 65 other cities spread across 5 continents. The participants comprised programmers, web and graphic designers, oceanographers and biologists, mathematicians, engineers and students who all worked tirelessly over the course of two days.

To find out more about the winning entry for Fishackathon in 2018 and how the Wolfram Language has helped make the seas safer, we sat down with Michael Sollami to learn more about him and his team’s solution to that year’s challenge.

Wolfram Language at Fishackathon

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December 6, 2018 — Tuseeta Banerjee, Research Scientist, Machine Learning

Julian Francis, a longtime user of the Wolfram Language, contacted us with a potential submission for the Wolfram Neural Net Repository. The Wolfram Neural Net Repository consists of models that researchers at Wolfram have either trained in house or converted from the original code source, curated, thoroughly tested and finally have rendered the output in a very rich computable knowledge format. Julian was our very first user to go through the process of converting and testing the nets.

We thought it would be interesting to interview him on the entire process of converting the models for the repository so that he could share his experiences and future plans to inspire others.

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August 2, 2018
Aaron Enright, Senior Data Scientist, Wolfram|Alpha Socioeconomic Content
Eric Weisstein, Senior Researcher, Wolfram|Alpha Scientific Content

The Mathematics Genealogy Project (MGP) is a project dedicated to the compilation of information about all mathematicians of the world, storing this information in a database and exposing it via a web-based search interface. The MGP database contains more than 230,000 mathematicians as of July 2018, and has continued to grow roughly linearly in size since its inception in 1997.

In order to make this data more accessible and easily computable, we created an internal version of the MGP data using the Wolfram Language’s entity framework. Using this dataset within the Wolfram Language allows one to easily make computations and visualizations that provide interesting and sometimes unexpected insights into mathematicians and their works. Note that for the time being, these entities are defined only in our private dataset and so are not (yet) available for general use.

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June 14, 2018
Sebastian Bodenstein, Machine Learning
Matteo Salvarezza, Machine Learning
Meghan Rieu-Werden, Data Manager, Machine Learning
Taliesin Beynon, Machine Learning

Hero

Today, we are excited to announce the official launch of the Wolfram Neural Net Repository! A huge amount of work has gone into training or converting around 70 neural net models that now live in the repository, and can be accessed programmatically in the Wolfram Language via NetModel:

net = NetModel

net = NetModel["ResNet-101 Trained on ImageNet Competition Data"]

Peacock Input

net[]

Peacock Output

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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May 31, 2018 — Sjoerd Smit, Technical Consultant, Wolfram Europe

Neural networks are very well known for their uses in machine learning, but can be used as well in other, more specialized topics, like regression. Many people would probably first associate regression with statistics, but let me show you the ways in which neural networks can be helpful in this field. They are especially useful if the data you’re interested in doesn’t follow an obvious underlying trend you can exploit, like in polynomial regression.

In a sense, you can view neural network regression as a kind of intermediary solution between true regression (where you have a fixed probabilistic model with some underlying parameters you need to find) and interpolation (where your goal is mostly to draw an eye-pleasing line between your data points). Neural networks can get you something from both worlds: the flexibility of interpolation and the ability to produce predictions with error bars like when you do regression.

Bayesian Neural Nets

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