One Simple Chart: which sectors are using reinforcement learning

Interest in reinforcement learning has grown steadily over the last decade. In a recent post, I described emerging applications of RL in recommendation and personalization systems, and in business simulation and optimization. In this post, I wanted to examine which industry sectors have been mentioning reinforcement learning in their job postings. Let’s place demand forContinue reading “One Simple Chart: which sectors are using reinforcement learning”

Understanding the Ray ecosystem and community

Ray is both a general purpose distributed computing platform and a collection of libraries targeted at machine learning and other workloads. By Ben Lorica and Ion Stoica. [This post originally appeared on the Anyscale blog.] Ray is usually described as a distributed computing platform that can be used to scale Python applications with minimal effort.Continue reading “Understanding the Ray ecosystem and community”

Building accessible tools for large-scale computation and machine learning

[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 frameworkContinue reading “Building accessible tools for large-scale computation and machine learning”

Notes from the first Ray meetup

[A version of this post appears on the O’Reilly Radar.] Ray is beginning to be used to power large-scale, real-time AI applications. Machine learning adoption is accelerating due to the growing number of large labeled data sets, languages aimed at data scientists (R, Julia, Python), frameworks (scikit-learn, PyTorch, TensorFlow, etc.), and tools for building infrastructure toContinue reading “Notes from the first Ray meetup”

Unleashing the potential of reinforcement learning

[A version of they post appears on the O’Reilly Radar.] The O’Reilly Data Show Podcast: Danny Lange on how reinforcement learning can accelerate software development and how it can be democratized. In this episode of the Data Show, I spoke with Danny Lange, VP of AI and machine learning at Unity Technologies. Lange previously ledContinue reading “Unleashing the potential of reinforcement learning”

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”

Practical applications of reinforcement learning in industry

[A version of this post appears on the O’Reilly Radar.] An overview of commercial and industrial applications of reinforcement learning. The flurry of headlines surrounding AlphaGo Zero (the most recent version of DeepMind’s AI system for playing Go) means interest in reinforcement learning (RL) is bound to increase. Next to deep learning, RL is amongContinue reading “Practical applications of reinforcement learning in industry”

Why continuous learning is key to AI

[A version of this post appears on the O’Reilly Radar.] A look ahead at the tools and methods for learning from sparse feedback. As more companies begin to experiment with and deploy machine learning in different settings, it’s good to look ahead at what future systems might look like. Today, the typical sequence is toContinue reading “Why continuous learning is key to AI”