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Data Science Community News | Volume 1, Issue 3

By Christina Howell posted Tue October 08, 2019 08:55 PM

  
IBM Data Science Community Newsletter
October 2019 | Volume 1, Issue 3
Spotlight
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AI Coming to the US Military
Using AI on the battlefield is the stuff of 80s movie fantasies. "There will be times where the machines make the mistake.  We're not looking for the omniscient machine that is never wrong. What we're looking for are machines that have been tested to the point where we have the trust that the AI will do what it is designed to do, and hopefully be able to explain why it made the decision it did." –Robert Work, US Deputy Secretary of Defense. Read commentary about the expected real world impact and US policy. 

AI Skills
Data Science for All: An Open Source Approach to Education
We are announcing a new Open Source framework for building AI/ML education across academia. Read more to understand how this approach will help close the skills gap around the world. Read more

Tools & Libraries
Building Trust in AI, the IBM Way
Interview with Saska Mojsilovic head of IBM's AI foundation dives into issues of AI Fairness and bias in our data sets. How big of a problem is biased data and what can we do to combat this problem? Watch the interview
Trustworthy Machine Learning and Artificial Intelligence
Modern AI/ML can solve many problems in new and exciting ways, but, what happens when things go wrong? This is a must read for both experts and new Data Scientists who want to understand the risks of problems—from distribution shifts to data poisoning. Read more

Solutions & Products
ML Ops Day, OSCON 2019
Machine Learning Operations is the practice of putting predictive models into production. How can an ML engineer/scientist approach this practice most efficiently? We heard from experts at ML Ops Day who had been asked this question. Watch videos and learn more
Operationalizing Data Science
Why do most ML/Data Science projects never make it to production? These fundamentals of DS Ops will help you avoid the common pitfalls of the field. Read more

Research
Optimized Score Transformation for Fair Classification in AI
Read the latest research paper on fair classification, which focuses on disparities in classification output/performance when conditioned on a protected attribute (race, gender, or ethnicity). This is an important study on the professional responsibility of using these attributes correctly. Read more

See all upcoming community events, here.

PyCon DE & PyData Berlin
October 9–13, 2019 | Live Event | Berlin, Germany

Data & AI Forum Miami Community Day
October 21, 2019 | Live Event | Miami, Florida

TensorFlow World
October 28–31, 2019 | Live Event | Santa Clara, California



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