[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Jacob Ward on the interplay between psychology, decision-making, and AI systems.In this episode of the Data Show, I spoke with Jacob Ward, a Berggruen Fellow at Stanford University. Ward has an extensive background in journalism, mainly covering topics in science and technology, at National Geographic, Al Jazeera, Discovery Channel, BBC, Popular Science, and many other outlets. Most recently, he’s become...

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Sharad Goel and Sam Corbett-Davies on the limitations of popular mathematical formalizations of fairness.In this episode of the Data Show, I spoke with Sharad Goel, assistant professor at Stanford, and his student Sam Corbett-Davies. They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in...

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Alan Nichol on building a suite of open source tools for chatbot developers.In this episode of the Data Show, I spoke with Alan Nichol, co-founder and CTO of Rasa, a startup that builds open source tools to help developers and product teams build conversational applications. About 18 months ago, there was tremendous excitement and hype surrounding chatbots, and while things have quieted lately, companies and...

[A version of this post appears on the O'Reilly Radar.]In this episode of the Data Show, I spoke with Eric Jonas, a postdoc in the new Berkeley Center for Computational Imaging. Jonas is also affiliated with UC Berkeley’s RISE Lab. It was at a RISE Lab event that he first announced Pywren, a framework that lets data enthusiasts proficient with Python run existing code at massive scale on Amazon Web Services. Jonas and his collaborators are working on a related project, NumPyWren, a system for...

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Harish Doddi on accelerating the path from prototype to production.In this episode of the Data Show, I spoke with Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge....

[A version of this post appears on the O'Reilly Radar.]Ray is beginning to be used to power large-scale, real-time AI applications.Machine learning adoption is accelerating due to the growing number of large labeled data sets, languages aimed at data scientists (R, Julia, Python), frameworks (scikit-learn, PyTorch, TensorFlow, etc.), and tools for building infrastructure to support end-to-end applications. While some interesting applications of unsupervised learning are beginning to emerge,...

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Chang Liu on operations research, and the interplay between differential privacy and machine learning.In this episode of the Data Show, I spoke with Chang Liu, applied research scientist at Georgian Partners. In a previous post, I highlighted early tools for privacy-preserving analytics, both for improving decision-making (business intelligence and analytics) and for enabling automation (machine learning)....

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Andrew Feldman on why deep learning is ushering a golden age for compute architecture.In this episode of the Data Show, I spoke with Andrew Feldman, founder and CEO of Cerebras Systems, a startup in the blossoming area of specialized hardware for machine learning. Since the release of AlexNet in 2012, we have seen an explosion in activity in machine learning, particularly in deep learning. A lot of the work...

[A version of this post appears on the O'Reilly Radar.]While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data.In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. My goal was to remind the data community about the many interesting opportunities and challenges in data itself. Much of the focus of recent press coverage has been on algorithms and models,...

[A version of this post appears on the O'Reilly Radar.]"Human in the loop" software development will be a big part of the future.Machine learning is poised to change the nature of software development in fundamental ways, perhaps for the first time since the invention of FORTRAN and LISP. It presents the first real challenge to our decades-old paradigms for programming. What will these changes mean for the millions of people who are now practicing software development? Will we see job losses...