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The Practical Quant's Blog

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Assessing progress in automation technologies

December 6, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]When it comes to automation of existing tasks and workflows, you need not adopt an “all or nothing” attitude.In this post, I share slides and notes from a keynote Roger Chen and I gave at the Artificial Intelligence conference in London in October 2018. We presented an overview of the state of automation technologies: we tried to highlight the state of the key building block technologies and we described how these tools might evolve in the...

Tools for generating deep neural networks with efficient network architectures

December 6, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Alex Wong on building human-in-the-loop automation solutions for enterprise machine learning.In this episode of the Data Show, I spoke with Alex Wong, associate professor at the University of Waterloo, and co-founder of DarwinAI, a startup that uses AI to address foundational challenges with deep learning in the enterprise. As the use of machine learning and analytics become more widespread, we’re beginning...

Building tools for enterprise data science

November 21, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Vitaly Gordon on the rise of automation tools in data science.In this episode of the Data Show, I spoke with Vitaly Gordon, VP of data science and engineering at Salesforce. As the use of machine learning becomes more widespread, we need tools that will allow data scientists to scale so they can tackle many more problems and help many more people. We need automation tools for the many stages involved in data...

Managing risk in machine learning

November 13, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]Considerations for a world where ML models are becoming mission critical.In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.Let’s begin by looking at the state of adoption. We recently conducted a surveywhich garnered more than 11,000 respondents—our...

Lessons learned while helping enterprises adopt machine learning

November 8, 2018 Comments (0)

[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 her colleague Jaya Mathew, a senior data scientist at Microsoft. We conducted a couple of surveys this year—“How Companies Are Putting AI to Work Through Deep...

Machine learning on encrypted data

October 25, 2018 Comments (0)

[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 analytics and machine learning to encrypted data. In a recent talk, I described the importance of data, various methods for estimating the value of data, and emerging...

How social science research can inform the design of AI systems

October 12, 2018 Comments (0)

[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...

Why it's hard to design fair machine learning models

September 27, 2018 Comments (0)

[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...

Using machine learning to improve dialog flow in conversational applications

September 14, 2018 Comments (0)

[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...

Building accessible tools for large-scale computation and machine learning

August 30, 2018 Comments (0)

[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...