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How Can We Trust Machine Learning?

How Can We Trust Machine Learning? In this talk, Carlos Guestrin, CEO of Dato, Inc. and Amazon Professor of Machine Learning at the University of Washington, describes recent research and new tools with which companies can start to have the means to gain trust and confidence in the models and predictions behind their core business applications.

New Theory Unveils the Black Box of Deep Learning

In the video presentation below (courtesy of Yandex) – “Deep Learning: Theory, Algorithms, and Applications” – Naftali Tishby, a computer scientist and neuroscientist from the Hebrew University of Jerusalem, provides evidence in support of a new theory explaining how deep learning works. Tishby argues that deep neural networks learn according to a procedure called the “information bottleneck.”

Making Computers Smarter with Google’s AI chief John Giannandrea

In the video below from the recent TechCrunch Disrupt SF 2017 event, Google’s John Giannandrea sits down with Frederic Lardinois to discuss the AI hype/worry cycle and the importance, limitations, and acceleration of machine learning. Giannandrea addresses the huge amount of hype surrounding AI right now, specifically the fear-mongering by some of Silicon Valley’s elite.

RMSprop Optimization Algorithm for Gradient Descent with Neural Networks

The video lecture below on the RMSprop optimization method is from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012. For all you AI practitioners out there, this technique should supplement your toolbox in a very useful way.

Intel AI Lounge – Bryce Olson, Global Marketing Director, Health and Life Sciences at Intel

In this Silicon Angle video interview, Bryce Olson, Intel Global Marketing Director, Health and Life Sciences and stage 4 cancer survivor, set out to find better treatment for his illness through technology. By using AI to sift through his DNA data, he is now in remission.

Machine Learning Interpretability with Driverless AI

In this presentation, our friends Andy Steinbach, Head of AI in Financial Services at NVIDIA, and Patrick Hall, Senior Director of Product at H2O.ai discuss Machine Learning Interpretability with Driverless AI. Interpretability is a hugely popular topic in machine learning. Wherever possible, interpretability approaches are deconstructed into more basic components suitable for human storytelling: complexity, scope, understanding, and trust.

Text Analytics – Unstructured Data Analysis

Presented by noted data scientist Derek Kane, this video provides an introduction to text analytics for advanced business users and IT professionals with limited programming expertise. The topics covered include search engine building, categorization (supervised and unsupervised), clustering, NLP, and social media analysis.

Virtualitics: Caltech & NASA Scientists Build VR/AR Analytics Platform using AI & Machine Learning

Virtualitics is a transformative start-up company that merges artificial intelligence (AI), big data and virtual reality (VR), and augmented reality (AR) to gain insights from big and complex data sets. Furthermore, Virtualitics leverages AI and easy-to-use machine learning tools so even non-expert users can uncover multidimensional relationships present in complex data sets with the click of a button.

Impetus Workload Migration Solution, StreamAnalytix and OLAP on Big Data solution

In this video from the DataWorks Summit / Hadoop Summit 2017 conference in San Jose (June 13-15, 2017), insideBIGDATA’s Managing Editor and resident data scientist Daniel D. Gutierrez chats with Larry Pearson, VP Marketing of Impetus Technologies, Vineet Tyagi, CTO of Impetus Technologies, and Ajay Anand, VP Products of Kyvos Insights.

AI Suggests Recipes Based on Food Photos

There are few things social media users love more than flooding their feeds with photos of food. Yet we seldom use these images for much more than a quick scroll on our cellphones. Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) believe that analyzing photos like these could help us learn recipes and better understand people’s eating habits.