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Not Always a Black Box: Machine Learning Approaches For Model Explainability
Editor’s Note: Violeta is speaking at ODSC Europe 2019, see her talk “Not Always a Black Box: Explainability Applications for a Real Estate Problem“ What is model explainability? Imagine that you have built a very precise machine learning model by using clever tricks and non-standard features. You are beyond... Read more
Generating Neural Networks to Detect Alzheimer’s
AI is showing so much promise in the medical field. It’s an excellent example of how AI combines with human intelligence to create a “super brain” capable of predicting disease, uncovering patterns, and testing solutions for persistent problems. Precision Medicine is a medical nonprofit using this super combination of... Read more
Watch: No Black Boxes: Understandability, Transparency, and Governance in Machine Learning
In this talk, presented at Accelerate AI East 2019, Ingo Mierswa presents the ideas of understandability, transparency, and governance in machine learning, and how those pieces all work together. Ingo Mierswa is an industry-veteran data scientist... Read more
10 Things Learned From Deploying AI in Human Environments
Deploying AI in human environments requires some finesse, unlike the pure environments you often encounter in school. Cameron Turner of Datorium is an expert at blending the world of business with AI deployment and is here to tell us ten things he’s learned so far.  [Related Article: How to... Read more
Scaling Humans With AI
One of the most persistent problems within many fields is the lack of communication. We have access to vast amounts of data, but all that information is siloed. You have so many different systems and hundreds of ways to access various pieces of data, but nothing really communicates. AI... Read more
Most Data-Driven Cultures… Aren’t
For Cassie Kozyrkov, Chief Decision Scientist at Google, reducing the instances of errors in statistics is the top priority. Many organizations think of themselves as data-driven, but in reality, it’s at the mercy of good leadership at your organization. If your organization isn’t good at getting and using data,... Read more
Taking Your Machine Learning from 0 to 10
Madhura Dudhgaonkar is the senior director of Machine Learning at Workday Inc. She believes that it’s possible to deploy machine learning within your enterprise, but it takes a few steps to get exactly right. She loves to get into unknowns and things we haven’t tried yet, but let’s look... Read more
Causal Inference: An Indispensable Set of Techniques for Your Data Science Toolkit
Editor’s Note: Want to learn more about key causal inference techniques, including those at the intersection of machine learning and causal inference? Attend ODSC West 2019 and join Vinod’s talk, “An Introduction to Causal Inference in Data Science.” Data scientists often get asked questions of the form “Does X... Read more
Deep Learning is Not Always the Best Solution in Education
Building AI models in education seems like a slam dunk. Educators and Edtech companies are so eager to make use of this new technology that sometimes the resulting product misses the mark. Deep learning has a place in education, but to make the best use of these types of... Read more
Why Effective and Ethical AI Needs Human-Centered Design
Data science is about a half-century old in the way we think of it now, but with the advent of AI, we’re reaching a precipice of how we want to model our AI initiatives. Moving forward with not just effective but ethical AI, we need a human-centered design principle.... Read more