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Nutrients by the Numbers: Food and Nutrition Statistics with Wolfram Language

Statistical analysis is an important tool in food science. It can uncover patterns and relationships in food and nutrition data, leading to advances in food manufacturing, nutrition counseling, food safety and new product development. Wolfram Language offers built-in functions for all standard statistical distributions. Here, we’ll use some of these functions to evaluate relationships between nutrients and visualize the data distributions with informative plots and histograms.
Gay Wilson, Food Data Curator, Wolfram|Alpha Scientific Content Isabel Skidmore, Food Data Consultant
Computation & Analysis

Creamy or Crunchy: Visualizing Food Protein Structures in Wolfram Language

How important is the relationship between protein structure and the food we eat?

  • Protein structure influences food texture. It can make a food smooth and creamy or crisp and crunchy.
  • Protein structure helps determine digestibility. Proteins with looser structures are more readily hydrolyzed into amino acids for easier digestion.
  • Protein structure is a factor in whether foods such as peanuts and shellfish cause an allergic reaction.
  • Protein structure can make our foods elegant and appetizing.
Isabel Skidmore, et al.
Computation & Analysis

Wolfram Function Repository Highlights: From Country Borders to Bird Speech Bubbles

In June 2019, Stephen Wolfram announced the Wolfram Function Repository, a curated repository of functions that can be employed immediately in the Wolfram Language. Since then, the Repository has grown to include more than 1,000 functions in over 20 categories.

Functions included in the Repository range from those that are more general and utilitarian in nature to others with very specific applications. As with all Wolfram Language functions, Repository documentation pages contain examples showing how to use the functions. We’re featuring a few of the functions submitted to the Repository so far that showcase the variety of functions our users have built.

The Wolfram Function Repository Team
Computation & Analysis

Neural Networks: An Introduction

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.

Tuseeta Banerjee, Research Scientist, Machine Learning
Computation & Analysis

Deep Learning and Computer Vision: Converting Models for the Wolfram Neural Net Repository

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.
Tuseeta Banerjee, Research Scientist, Machine Learning
Current Events & History

Citizen Data Science with Civic Hacking: The Safe Drinking Water Data Challenge

Code for America’s National Day of Civic Hacking is coming up on August 11, 2018, which presents a nice opportunity for individuals and teams of all skill levels to participate in the Safe Drinking Water Data Challenge—a program Wolfram is supporting through free access to Wolfram|One and by hosting relevant structured datasets in the Wolfram Data Repository.

According to the state of California, some 200,000 residents of the state have unsafe drinking water coming out of their taps. While the Safe Drinking Water Data Challenge focuses on California, data science solutions could have impacts and applications for providing greater access to potable water in other areas with similar problems.

The goal of this post is to show how Wolfram technologies make it easy to grab data and ask questions of it, so we’ll be taking a multiparadigm approach and allowing our analysis to be driven by those questions in an exploratory analysis, a way to quickly get familiar with the data.
Swede White, Public Relations Manager
Announcements & Events

Learning to Listen: Neural Networks Application for Recognizing Speech

Introduction

Recognizing words is one of the simplest tasks a human can do, yet it has proven extremely difficult for machines to achieve similar levels of performance. Things have changed dramatically with the ubiquity of machine learning and neural networks, though: the performance achieved by modern techniques is dramatically higher compared with the results from just a few years ago. In this post, I'm excited to show a reduced but practical and educational version of the speech recognition problem---the assumption is that we’ll consider only a limited set of words. This has two main advantages: first of all, we have easy access to a dataset through the Wolfram Data Repository (the Spoken Digit Commands dataset), and, maybe most importantly, all of the classifiers/networks I’ll present can be trained in a reasonable time on a laptop.

It’s been about two years since the initial introduction of the Audio object into the Wolfram Language, and we are thrilled to see so many interesting applications of it. One of the main additions to Version 11.3 of the Wolfram Language was tight integration of Audio objects into our machine learning and neural net framework, and this will be a cornerstone in all of the examples I’ll be showing today.

Without further ado, let’s squeeze out as much information as possible from the Spoken Digit Commands dataset!

Carlo Giacometti, Kernel Developer, Algorithms R&D
Current Events & History

Running the Numbers with the Illinois Marathon Viewer

I love to run. A lot. And many of my coworkers do too. You can find us everywhere, and all the time: on roads, in parks, on hills and mountains, and even running up and down parking decks, a flat lander's version of hills. And if there is a marathon to be run, we'll be there as well. With all of the internal interest in running marathons, Wolfram Research created this Marathon Viewer as a sponsorship project for the Christie Clinic Illinois Marathon. Here are four of us, shown as dots, participating in the 2017 Illinois Marathon: How did the above animation and the in-depth look at our performance come about? Read on to find out.
Eila Stiegler, Quality Analysis Manager, Wolfram|Alpha Quality Analysis Jeff Bryant, Research Programmer, Wolfram|Alpha Scientific Content
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