Hong Kong 2017

We just spent the past week in Hong Kong and below are some tweets from our trip. The weather was perfect – the temperature was just right. Their metro/train system is world class and easy to use and navigate. I’m looking to returning soon!

Introducing model-based thinking into AI systems

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The O’Reilly Data Show Podcast: Vikash Mansinghka on recent developments in probabilistic programming.

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In this episode I spoke with Vikash Mansinghka, research scientist at MIT, where he leads the Probabilistic Computing Project, and co-founder of Empirical Systems. I’ve long wanted to introduce listeners to recent developments in probabilistic programming, and I found the perfect guide in Mansinghka.

Probability is the mathematical language to represent, model, and manipulate uncertainty, and probabilistic programming provides frameworks for representing probabilistic models as computer programs. This family of tools and techniques distinguishes between models and the inference procedures, and in the process, encourages the kind of model-based thinking that may inform the design of future artificial intelligence systems and supplement current data and compute-intensive systems that rely primarily on large-scale pattern recognition.

Below are highlights from my conversation with Mansinghka:
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Building the next-generation big data analytics stack

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The O’Reilly Data Show Podcast: Michael Franklin on the lasting legacy of AMPLab.

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In this episode I spoke with Michael Franklin, co-director of UC Berkeley’s AMPLab and chair of the Department of Computer Science at the University of Chicago. AMPLab is well-known in the data community for having originated Apache Spark, Alluxio (formerly Tachyon) and many other open source tools. Today marks the start of a two-day symposium commemorating the end of AMPLab, and we took the opportunity to reflect on its impressive accomplishments.

AMPLab is the latest in a series of UC Berkeley research labs each designed with clear goals, a multidisciplinary faculty, and a fixed timeline (for more details, see David Patterson’s interesting design document for research labs). Many of AMPLab’s principals were involved in its precursor, the RAD Lab. As Franklin describes in our podcast episode:

The insight that Dave Patterson and the other folks who founded the RAD Lab had was that modern systems were so complex that you needed serious machine learning—cutting-edge machine learning—to be able to do that [to basically allow the systems to manage themselves]. You couldn’t take a computer systems person, give them an intro to machine learning book, and hope to solve that problem. They actually built this team that included computer systems people sitting next to machine learning people. … Traditionally, these two groups had very little to do with each other. That was a five-year project. The way I like to say it is—they spent at least four of those years learning how to talk to each other.

Toward of the end of the RAD Lab, we had probably the best group in the world of combined systems and machine learning people, who actually could speak to each other. In fact, Spark grew out of that relationship, because there were machine learning people in the RAD Lab who were trying to run iterative algorithms on Hadoop and were just getting terrible performance.

… AMPLab in some sense was a flip of that relationship. If you considered RAD Lab as basically a setting where “machine learning people were consulting for the systems people”, in AMPLab, we did the opposite—machine learning people got help from the systems people in how to make these things scale. That’s one part of the story.

In the rest of this post, I’ll describe some of my interactions with the AMPLab team. These recollections are based on early meetups, retreats, and conferences.

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Visual tools for overcoming information overload

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The O’Reilly Data Show Podcast: Dafna Shahaf on information cartography and AI, and Sam Wang on probabilistic methods for forecasting political elections.

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In this special two-segment episode of the Data Show, I spoke with Dafna Shahaf, assistant professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem. Her area of research is focused on tools and techniques for overcoming information overload, an area of increasing importance in an attention economy. With the upcoming U.S. Presidential Elections right around the corner, I included a conversation between Jenn Webb, host of the O’Reilly Radar Podcast, and Sam Wang, co-founder of the Princeton Election Consortium and professor of neuroscience and molecular biology at Princeton University.

Below are highlights from my conversation with Dafna Shahaf:
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Why businesses should pay attention to deep learning

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The O’Reilly Data Show Podcast: Christopher Nguyen on the early days of Apache Spark, deep learning for time-series and transactional data, innovation in China, and AI.

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In this episode of the O’Reilly Data Show, I spoke with Christopher Nguyen, CEO and co-founder of Arimo. Nguyen and Arimo were among the first adopters and proponents of Apache Spark, Alluxio, and other open source technologies. Most recently, Arimo’s suite of analytic products has relied on deep learning to address a range of business problems.

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The technology behind self-driving vehicles

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Shaoshan Liu on perception, knowledge, reasoning, and planning for autonomous cars.

Shaoshan Liu takes a deep dive into this topic in his recent post “Creating autonomous vehicle systems.”

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to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS.

Ask a random person for an example of an AI system and chances are he or she will name self-driving vehicles. In this episode of the O’Reilly Data Show, I sat down with Shaoshan Liu, co-founder of PerceptIn and previously the senior architect (autonomous driving) at Baidu USA. We talked about the technology behind self-driving vehicles, their reliance on rule-based decision engines, and deploying large-scale deep learning systems.

Here are some highlights from our conversation:
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