Watch: High-Performance Data Science with Docker and Digital Ocean
This talk discusses Docker as a tool for data scientists, in particular in conjunction with the popular interactive programming platform, Jupyter, and the cloud computing platform, Amazon Web Services (AWS). Using Docker, Jupyter, and AWS, a data scientist can take control of their environment configuration, prototype scalable data architectures,... Read more
How Nonprofits Use AI to Improve the Workforce
Artificial intelligence has a hand in just about every industry possible, including the nonprofit sector. Though some may not realize it, nonprofit organizations account for nearly $400B of revenue annually worldwide. But some nonprofits still struggle to make ends meet and expand their workforces, causing them to fold and... Read more
The Power of Universal Latent Space In Medical Breakthroughs
The cost to develop new medicines has grown tremendously despite our computing and medical advances. Although we have a greater need than ever for massive breakthroughs in medical science, the industry itself is still a slow, highly regimented field. Mason Victors, CTO and CPO for Recursion Pharmaceuticals believes that... Read more
Problem Solving with Data for a Better Business
When working with large datasets, the smallest anomalies can throw a wrench in predictive analysis. For example, if a company manually enters data into its database, a human error like mistyping or improper timestamps in the training data of a machine learning model may give you reduced accuracy results.... Read more
Why The New Era of Big Data Requires Innovative Privacy Initiatives
“Data can either be useful or perfectly anonymous but never both.” – Paul Ohm Privacy and data collection go together like peanut butter and jelly, but in the world of big data, it’s becoming increasingly difficult to work with anonymous data without crossing a privacy line. So what’s a... Read more
Watch: Project Feels – Deep Text Models for Sentiment Analysis
This video discusses the use of active learning, deep learning, Bayesian inference, and causality in Project Feels. This project, developed by the Data Science Group at the New York Times, sought to predict how likely a given article was to evoke a range of emotions. Thus project crowdsourced data... Read more
Watch: A Breakthrough for Natural Language
Natural language is valuable, but it is complex. With a 1,000 word vocabulary, a 15-word sentence can easily express more than 1e30 (a 1 with 30 zeros) different ideas. Today’s natural language processing is trained to bucket a sentence into one of a few thousand categories–which also means it... Read more
Ensemble Models Demystified
Ensemble models give us excellent performance and work in a wide variety of problems. They’re easier to train than other types of techniques, requiring less data with better results. In machine learning, ensemble models are the norm. Even if you aren’t using them, your competitors are. Kevin Lemagnen is... Read more
Weapons Of Math Destruction: The Power Of Oversight
Weapons of Math Destruction: How algorithms have the power to alter our liberties and what we should be doing instead. Big data is making decisions about your future behind the scenes, and it’s likely you don’t even know it. If you’ve ever applied for a job and had to... Read more
Watch: Applications of Deep Learning in Aerospace
Recent advances in machine learning techniques such as deep learning (DL) have rejuvenated data-driven analysis in aerospace and integrated building systems. DL algorithms have been successful due to the presence of large volumes of data and its ability to learn the features during the learning process. The performance improvement... Read more