What Statistical Test Should I Do?
Being a teaching assistant in statistics for students with diverse backgrounds, I have the chance to see what is globally not well understood by students. I have realized that it is usually not a problem for students to do a specific statistical test when they are told which... Read more
Answering Causal Questions in AI
Two of the main techniques used in order to try to discover causal relationships are Graphical Methods (such as Knowledge Graphs and Bayesian Belief Networks) and Explainable AI. These two methods form in fact the basis of the Association level in the Causality Hierarchy (Figure 1),... Read more
Making Deep Learning Climate-Friendly
Deep learning is bringing many benefits to the world: solving the 50-year-old protein folding problem, detecting cancer, and improving the power grid. While there is so much that deep learning is powering, we also need to consider the costs. In the quest for more accurate and... Read more
Exploring Natural Language Processing: Two Ways You Can Leverage Corpus Analysis
Corpus analysis is a technique widely used by data scientists because it provides understanding of a document collection and provides insights about the text.  It’s an apt methodology to consider as we came upon Charles Dickens’ 210th birthday earlier this year because of how frequently passages... Read more
Overcoming ImageNet dataset biases with PASS
ImageNet is one of the most widely used datasets in Computer Vision applications. However, studies have shown biases prevalent in this dataset based on the collection methodology and the types of images present. In this respect, a team of researchers at the Visual Geometry Group, the University of... Read more
Using Distillation to Protect Your Neural Networks
Distillation is a hot research area. For distillation, you first train a deep learning model, the teacher network, to solve your task. Then, you train a student network, which can be any model. While the teacher is trained on real data, the student is trained on the teacher’s outputs. It... Read more
AI Trained to Generate Novel Molecular Materials with a Generative Graph Grammar Model
Emerging technologies in the scientific community are helping researchers achieve more goals and make discoveries. Revolutionary tech such as artificial intelligence (AI) and machine learning (ML) have already disrupted various industries, from manufacturing to retail and beyond. ML has expedited the discovery process, especially for grad... Read more
Extracting Information From Unstructured Documents with Azure Form Recognizer
Extracting information from unstructured documents such as contracts is usually manual and involves tediously reading and understanding substantial amounts of documents to find specific information and manually extracting the information to digitize it. The process consumes a significant amount of a lawyer’s billable hours and is... Read more
Open-Source Assessment is Critical for the Future of Responsible AI
Artificial intelligence’s technological sophistication and industry reach continues to evolve rapidly, which has prompted a movement to ensure that these systems are used responsibly. The broadest goal of Responsible AI (RAI) is to recognize the expansive consequences of AI systems, and adapt the development and management... Read more
Recurrent Neural Networks for Financial Time Series Prediction
Editor’s note: Nicole Königstein is a speaker for ODSC Europe 2022. Be sure to check out her talk, Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment, there to learn more about financial time series prediction! The use of neural networks is relatively... Read more