[A version of this post appears on the O’Reilly Radar blog.] The O’Reilly Data Show Podcast: Francesca Lazzeri and Jaya Mathew on digital transformation, culture and organization, and the team data science process. In this episode of the Data Show, I spoke with Francesca Lazzeri, an AI and machine learning scientist at Microsoft, and herContinue reading “Lessons learned while helping enterprises adopt machine learning”
Author Archives: Ben Lorica
Machine learning on encrypted data
[A version of this post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Alon Kaufman on the interplay between machine learning, encryption, and security. In this episode of the Data Show, I spoke with Alon Kaufman, CEO and co-founder of Duality Technologies, a startup building tools that will allow companies to apply analyticsContinue reading “Machine learning on encrypted data”
How social science research can inform the design of AI systems
[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 andContinue reading “How social science research can inform the design of AI systems”
Why it’s hard to design fair machine learning models
[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 surveyContinue reading “Why it’s hard to design fair machine learning models”
Using machine learning to improve dialog flow in conversational applications
[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 developersContinue reading “Using machine learning to improve dialog flow in conversational applications”
Building accessible tools for large-scale computation and machine learning
[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 frameworkContinue reading “Building accessible tools for large-scale computation and machine learning”
Simplifying machine learning lifecycle management
[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. AsContinue reading “Simplifying machine learning lifecycle management”
Notes from the first Ray meetup
[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 toContinue reading “Notes from the first Ray meetup”
5 findings from O’Reilly’s machine learning adoption survey companies should know
New survey results highlight the ways organizations are handling machine learning’s move to the mainstream. By Ben Lorica and Paco Nathan. [This post originally appeared on the O’Reilly Radar.] As machine learning has become more widely adopted by businesses, O’Reilly set out to survey our audience to learn more about how companies approach this work.Continue reading “5 findings from O’Reilly’s machine learning adoption survey companies should know”
How privacy-preserving techniques can lead to more robust machine learning models
[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 toolsContinue reading “How privacy-preserving techniques can lead to more robust machine learning models”