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

Deep Learning Summit, Toronto featuring Geoff Hinton – save with KDnuggets

[unable to retrieve full-text content]Geoffrey Hinton, one of the fathers of Deep Learning, will be back to share his most recent and cutting-edge research progressions, and will be joined by other top researchers. Save 20% on Early Bird passes when you sign up before 15 June w. code KDNUGGETS. Also check Women in AI dinner series and get new white paper on Ethical implications of AI.
Original Post: Deep Learning Summit, Toronto featuring Geoff Hinton – save with KDnuggets