AI, Machine Learning and Data Science Roundup: June 2018

A monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements and data applications I’ve noted over the past month or so. Open Source AI, ML & Data Science News Intel open-sources NLP Architect, a Python library for deep learning with natural language. Gym Retro, an open source platform for reinforcement learning research on video games. Facebook open-sources DensePose, a toolkit to transform 2D images of people into a 3-D surface map of the human body. MLflow, an open source machine learning platform from Databricks, has been released. Industry News In a 12-minute documentary video and accompanying Wired article, Facebook describes how it uses Machine Learning to improve quality of the News Feed. In the PYPL language rankings, Python ranks #1 in popularity and R is #7;…
Original Post: AI, Machine Learning and Data Science Roundup: June 2018

Statistics, Causality, and What Claims are Difficult to Swallow: Judea Pearl debates Kevin Gray

[unable to retrieve full-text content]While KDnuggets takes no side, we present the informative and respectful back and forth as we believe it has value for our readers. We hope that you agree.
Original Post: Statistics, Causality, and What Claims are Difficult to Swallow: Judea Pearl debates Kevin Gray

Detecting unconscious bias in models, with R

There’s growing awareness that the data we collect, and in particular the variables we include as factors in our predictive models, can lead to unwanted bias in outcomes: from loan applications, to law enforcement, and in many other areas. In some instances, such bias is even directly regulated by laws like the Fair Housing Act in the US. But even if we explicitly remove “obvious” variables like sex, age or ethnicity from predictive models, unconscious bias might still be a factor in our predictions as a result of highly-correlated proxy variables that are included in our model. As a result, we need to be aware of the biases in our model and take steps to address them. For an excellent general overview of the topic, I highly recommend watching the recent presentation by Rachel Thomas, “Analyzing and Preventing Bias in ML”.…
Original Post: Detecting unconscious bias in models, with R

What's new in Azure for Machine Learning and AI

There were a lot of big announcements at last month’s Build conference, and many of them were related to machine learning and artificial intelligence. With my colleague Tim Heuer, we summarized some of the big announcements — and a few you may have missed — in a recent webinar. The slides are embedded below, and include links to recordings of the Build sessions where you can find in-depth details. You can’t see the videos or demos in the slides, unfortunately — my favorite is a demo of using Microsoft Translator, trained by a hearing-impaired user, to accurately transcribe “deaf voice”. But you can find the videos and discussion from Tim and me in the on-demand recording available at the link below. Azure Webinar Series: Top Azure Takeaways from Microsoft Build
Original Post: What's new in Azure for Machine Learning and AI

When the bubble bursts…

When the bubble bursts… Consider the following facts: NIPS submission are up 50% this year to ~4800 papers. There is significant evidence that the process of reviewing papers in machine learning is creaking under several years of exponentiating growth. Public figures often overclaim the state of AI. Money rains from the sky on ambitious startups with a good story. Apparently, we now even have a fake conference website (https://nips.cc/ is the real one for NIPS). We are clearly not in a steady-state situation. Is this a bubble or a revolution? The answer surely includes a bit of revolution—the fields of vision and speech recognition have been turned over by great empirical successes created by deep neural architectures and more generally machine learning has found plentiful real-world uses. At the same time, I find it hard to believe that we aren’t…
Original Post: When the bubble bursts…

Big Data Toronto Brings Canada to the Centre Stage in Big Data and AI

[unable to retrieve full-text content]The Big Data Toronto conference and expo is back for its 3rd edition on Jun 12-13, 2018 at the Metro Toronto Convention Centre. Big Data focuses on the skills, software and leadership needed to implement data insights & AI Toronto is dedicated to Toronto’s growing AI and deep learning communities.
Original Post: Big Data Toronto Brings Canada to the Centre Stage in Big Data and AI

Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: June 2018 and Beyond

[unable to retrieve full-text content]Coming soon: Mega-PAW Las Vegas, Spark + AI Summit SF, CogX London, Big Data Toronto Big Data Toronto Conference and Expo, ICDM/MLDM NYC, and many more.
Original Post: Upcoming Meetings in AI, Analytics, Big Data, Data Science, Deep Learning, Machine Learning: June 2018 and Beyond