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The Practical Quant's Blog

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The evolution and expanding utility of Ray

February 21, 2019 Comments (0)

[A version of this post appears on the O'Reilly Radar.]There are growing numbers of users and contributors to the framework, as well as libraries for reinforcement learning, AutoML, and data science.In a recent post, I listed some of the early use cases described in the first meetup dedicated to Ray—a distributed programming framework from UC Berkeley’s RISE Lab. A second meetup took place a few months later, and both events featured some of the first applications built with Ray. On the...

Using machine learning and analytics to attract and retain employees

February 21, 2019 Comments (0)

[A version of this post appears on the O'Reilly Radar blog.]The O'Reilly Data Show Podcast: Maryam Jahanshahi on building tools to help improve efficiency and fairness in how companies recruit.In this episode of the Data Show, I spoke with Maryam Jahanshahi, research scientist at TapRecruit, a startup that uses machine learning and analytics to help companies recruit more effectively. In an upcoming survey, we found that a “skills gap” or “lack of skilled people” was one of the main bottlenecks...

The technical, societal, and cultural challenges that come with the rise of fake media

February 21, 2019 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Siwei Lyu on machine learning for digital media forensics and image synthesis.In this episode of the Data Show, I spoke with Siwei Lyu, associate professor of computer science at the University at Albany, State University of New York. Lyu is a leading expert in digital media forensics, a field of research into tools and techniques for analyzing the authenticity of media files. Over the past year, there have...

9 AI trends on our radar

February 21, 2019 Comments (0)

[A version of this post appears on the O'Reilly Radar.]How new developments in automation, machine deception, hardware, and more will shape AI.Here are key AI trends business leaders and practitioners should watch in the months ahead.We will start to see technologies enable partial automation of a variety of tasks.Automation occurs in stages. While full automation might still be a ways off, there are many workflows and tasks that lend themselves to partial automation. In fact, McKinsey...

How machine learning impacts information security

February 21, 2019 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Andrew Burt on the need to modernize data protection tools and strategies.In this episode of the Data Show, I spoke with Andrew Burt, chief privacy officer and legal engineer at Immuta, a company building data management tools tuned for data science. Burt and cybersecurity pioneer Daniel Geer recently released a must-read white paper (“Flat Light”) that provides a great framework for how to think about...

Books I enjoyed in 2018

February 21, 2019 Comments (0)

Here are nonfiction books I enjoyed reading in 2018 (more precisely, these are books I read during the second part of 2017 through 2018). Not all of these books were released this year, although most them are 2018 releases. I read many of these using Apple Books on my iPad but I've also gotten back to scouring thrift stores and used bookstores (where I've gotten some amazing bargains this year). For more, head over to the Gradient Flow.

In the age of AI, fundamental value resides in data

February 21, 2019 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Haoyuan Li on accelerating analytic workloads, and innovation in data and AI in China.In this episode of the Data Show, I spoke with Haoyuan Li, CEO and founder of Alluxio, a startup commercializing the open source project with the same name (full disclosure: I’m an advisor to Alluxio). Our discussion focuses on the state of Alluxio (the open source project that has roots in UC Berkeley’s AMPLab),...

7 data trends on our radar

February 21, 2019 Comments (0)

[A version of this post appears on the O'Reilly Radar.]From infrastructure to tools to training, here's what’s ahead for data.Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead.Increasing focus on building data culture, organization, and trainingIn a recent O’Reilly survey, we found that the skills gap remains one of the key challenges holding back the adoption of machine learning. The demand for data skills (“the sexiest job of...

Deep automation in machine learning

February 21, 2019 Comments (0)

[A version of this post appears on the O'Reilly Radar.]We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline.By Ben Lorica and Mike LoukidesIn a previous post, we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure. Since that time, Andrej Karpathy has made some more predictions about the fate of software...

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...