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The Practical Quant's Blog

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Graphs as the front end for machine learning

February 15, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Leo Meyerovich on building large-scale, interactive applications that enable visual investigations.In this episode of the Data Show, I spoke with Leo Meyerovich, co-founder and CEO of Graphistry. Graphs have always been part of the big data revolution (think of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for...

Machine learning needs machine teaching

February 1, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Mark Hammond on applications of reinforcement learning to manufacturing and industrial automation.In this episode of the Data Show, I spoke with Mark Hammond, founder and CEO of Bonsai, a startup at the forefront of developing AI systems in industrial settings. While many articles have been written about developments in computer vision, speech recognition, and autonomous vehicles, I’m particularly excited...

Introducing RLlib: A composable and scalable reinforcement learning library

January 19, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]RISE Lab’s Ray platform adds libraries for reinforcement learning and hyperparameter tuning.In a previous post, I outlined emerging applications of reinforcement learning (RL) in industry. I began by listing a few challenges facing anyone wanting to apply RL, including the need for large amounts of data, and the difficulty of reproducing research results and deriving the error estimates needed for mission-critical applications....

How machine learning can be used to write more secure computer programs

January 18, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Fabian Yamaguchi on the potential of using large-scale analytics on graph representations of code.In this episode of the Data Show, I spoke with Fabian Yamaguchi, chief scientist at ShiftLeft. His 2015 Ph.D. dissertation sketched out how the combination of static analysis, graph mining, and machine learning, can be used to develop tools to augment security analysts. In a recent post, I argued for machine...

Responsible deployment of machine learning

January 11, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]We need to build machine learning tools to augment our machine learning engineers.In this post, I share slides and notes from a talk I gave in December 2017 at the Strata Data Conference in Singapore offering suggestions to companies that are actively deploying products infused with machine learning capabilities. Over the past few years, the data community has focused on infrastructure and platforms for data collection, including robust...

What lies ahead for data in 2018

January 9, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]How new developments in algorithms, machine learning, analytics, infrastructure, data ethics, and culture will shape data in 2018.1. New tools will make graphs and time series easier, leading to new use casesGraphs and time series have been a crucial part of the explosion in big data. 2018 will see the emergence of a new generation of tools for storing and analyzing graphs and time series at large scale. These new analytic and visualization...

5 AI trends to watch in 2018

January 9, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]Expect substantial progress in machine learning methods, understanding, and pedagogyAs in recent years, new deep learning architectures and (distributed) training algorithms will lead to impressive results and applications in a range of domains, including computer vision, speech, and text. Expect to see companies make progress on efficient algorithms for training, inference, and data processing on edge devices. At the same time,...

8 fintech trends for 2018

January 8, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]AI, blockchain, payment regionalization, and other fintech trends to watch.2017 saw big changes, a lot of investment, and some regulatory challenges in fintech. What will 2018 bring? Here’s what we’ll be watching in the coming year.1. AI will be implemented across the stackAI is sweeping across all industry sectors, including financial services. AI touches customer interactions (voice services like Siri and dialog systems), fraud detection,...

Bringing AI into the enterprise

January 4, 2018 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O’Reilly Data Show Podcast: Kris Hammond on business applications of AI technologies and educating future AI specialists.In this episode of the Data Show, I spoke with Kristian Hammond, chief scientist of Narrative Science and professor of EECS at Northwestern University. He has been at the forefront of helping companies understand the power, limitations, and disruptive potential of AI technologies and tools. In a previous post on...

How machine learning will accelerate data management systems

December 21, 2017 Comments (0)

[A version of this post appears on the O'Reilly Radar.]The O'Reilly Data Show Podcast: Tim Kraska on why ML will change how we build core algorithms and data structures.In this episode of the Data Show, I spoke with Tim Kraska, associate professor of computer science at MIT. To take advantage of big data, we need scalable, fast, and efficient data management systems. Database administrators and users often find themselves tasked with building index structures (“indexes” in database parlance),...