[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 ofContinue reading “Graphs as the front end for machine learning”
Author Archives: Ben Lorica
Machine learning needs machine teaching
[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 industrialContinue reading “Machine learning needs machine teaching”
Introducing RLlib: A composable and scalable reinforcement learning library
[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 amountsContinue reading “Introducing RLlib: A composable and scalable reinforcement learning library”
How machine learning can be used to write more secure computer programs
[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 ofContinue reading “How machine learning can be used to write more secure computer programs”
We need to build machine learning tools to augment machine learning engineers
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,Continue reading “We need to build machine learning tools to augment machine learning engineers”
What lies ahead for data in 2018
[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 cases Graphs and time series have been a crucial part of the explosion in bigContinue reading “What lies ahead for data in 2018”
5 AI trends to watch in 2018
[A version of this post appears on the O’Reilly Radar.] Expect substantial progress in machine learning methods, understanding, and pedagogy As 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 onContinue reading “5 AI trends to watch in 2018”
8 fintech trends for 2018
[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 stackContinue reading “8 fintech trends for 2018”
Bringing AI into the enterprise
[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 beenContinue reading “Bringing AI into the enterprise”
How machine learning will accelerate data management systems
[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,Continue reading “How machine learning will accelerate data management systems”