How Ray makes continuous learning accessible and easy to scale

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The O’Reilly Data Show Podcast: Robert Nishihara and Philipp Moritz on a new framework for reinforcement learning and AI applications.

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In this episode of the Data Show, I spoke with Robert Nishihara and Philipp Moritz, graduate students at UC Berkeley and members of RISE Lab. I wanted to get an update on Ray, an open source distributed execution framework that makes it easy for machine learning engineers and data scientists to scale reinforcement learning and other related continuous learning algorithms. Many AI applications involve an agent (for example a robot or a self-driving car) interacting with an environment. In such a scenario, an agent will need to continuously learn the right course of action to take for a specific state of the environment.

What do you need in order to build large-scale continuous learning applications? You need a framework with low-latency response times, one that is able to run massive numbers of simulations quickly (agents need to be able explore states within an environment), and supports heterogeneous computation graphs. Ray is a new execution framework written in C++ that contains these key ingredients. In addition, Ray is accessible via Python (and Jupyter Notebooks), and comes with many of the standard reinforcement learning and related continuous learning algorithms that users can easily call.

As Nishihara and Moritz point out, frameworks like Ray are also useful for common applications such as dialog systems, text mining, and machine translation. Here are some highlights from our conversation:

Tools for reinforcement learning

Ray is something we’ve been building that’s motivated by our own research in machine learning and reinforcement learning. If you look at what researchers who are interested in reinforcement learning are doing, they’re largely ignoring the existing systems out there and building their own custom frameworks or custom systems for every new application that they work on.

… For reinforcement learning, you need to be able to share data very efficiently, without copying it between multiple processes on the same machine, you need to be able to avoid expensive serialization and deserialization, and you need to be able to create a task and get the result back in milliseconds instead of hundreds of milliseconds. So, there are a lot of little details that come up.
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Why continuous learning is key to AI

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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 to gather data, learn some underlying structure, and deploy an algorithm that systematically captures what you’ve learned. Gathering, preparing, and enriching the right data—particularly training data—is essential and remains a key bottleneck among companies wanting to use machine learning.

I take for granted that future AI systems will rely on continuous learning as opposed to algorithms that are trained offline. Humans learn this way, and AI systems will increasingly have the capacity to do the same. Imagine visiting an office for the first time and tripping over an obstacle. The very next time you visit that scene—perhaps just a few minutes later—you’ll most likely know to look out for the object that tripped you.

There are many applications and scenarios where learning takes on a similar exploratory nature. Think of an agent interacting with an environment while trying to learn what actions to take and which ones to avoid in order to complete some preassigned task. We’ve already seen glimpses of this with recent applications of reinforcement learning (RL). In RL, the goal is to learn how to map observations and measurements to a set of actions, while trying to maximize some long-term reward. (The term RL is frequently used to describe both a class of problems and a set of algorithms.) While deep learning gets more media attention, there are many interesting recent developments in RL that are well known within AI circles. Researchers have recently applied RL to game play, robotics, autonomous vehicles, dialog systems, text summarization, education and training, and energy utilization.
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Why AI and machine learning researchers are beginning to embrace PyTorch

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The O’Reilly Data Show Podcast: Soumith Chintala on building a worthy successor to Torch and deep learning within Facebook.

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In this episode of the Data Show, I spoke with Soumith Chintala, AI research engineer at Facebook. Among his many research projects, Chintala was part of the team behind DCGAN (Deep Convolutional Generative Adversarial Networks), a widely cited paper that introduced a set of neural network architectures for unsupervised learning. Our conversation centered around PyTorch, the successor to the popular Torch scientific computing framework. PyTorch is a relatively new deep learning framework that is fast becoming popular among researchers. Like Chainer, PyTorch supports dynamic computation graphs, a feature that makes it attractive to researchers and engineers who work with text and time-series.

Here are some highlights from our conversation:

The origins of PyTorch

TensorFlow addressed one part of the problem, which is quality control and packaging. It offered a Theano style programming model, so it was a very low-level deep learning framework. … There are a multitude of front ends that are trying to cope with the fact that TensorFlow is a very low-level framework—there’s TF-slim, there’s Keras. I think there’s like 10 or 15, and just from Google there’s probably like four or five of those.
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How big data and AI will reshape the automotive industry

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The O’Reilly Data Show Podcast: Evangelos Simoudis on next-generation mobility services.

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In this episode of the Data Show, I spoke with Evangelos Simoudis, co-founder of Synapse Partners and a frequent contributor to O’Reilly. He recently published a book entitled The Big Data Opportunity in Our Driverless Future, and I wanted get his thoughts on the transportation industry and the role of big data and analytics in its future. Simoudis is an entrepreneur, and he also advises and invests in many technology startups. He became interested in the automotive industry long before the current wave of autonomous vehicle startups was in the planning stages.


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Building a next-generation platform for deep learning

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The O’Reilly Data Show Podcast: Naveen Rao on emerging hardware and software infrastructure for AI.

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In this episode of the Data Show, I speak with Naveen Rao, VP and GM of the Artificial Intelligence Products Group at Intel. In an earlier episode, we learned that scaling current deep learning models requires innovations in both software and hardware. Through his startup Nervana (since acquired by Intel), Rao has been at the forefront of building a next generation platform for deep learning and AI.

I wanted to get his thoughts on what the future infrastructure for machine learning would look like. At least for now, we’re seeing a variety of approaches, and many companies are using heterogeneous processors (even specialized ones) and proprietary interconnects for deep learning. Nvidia and Intel Nervana are set to release processors that excel at both training and inference, but as Rao pointed out, at large-scale there are many considerations—including utilization, power consumption, and convenience—that come into play.

Here is a partial list of the items we discussed:

  • Deep learning in comparison to other machine learning algorithms
  • Key features and the current status of Intel Nervana’s Lake Cresttechnology
  • Deep learning frameworks and related software tools including Nervana Graph.
  • Building next-generation hardware and software components for deep learning
  • An overview of the major AI initiatives within Intel (including the establishment of a new AI Research Lab that Rao is leading)

Related resources:

Programming collective intelligence for financial trading

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The O’Reilly Data Show Podcast: Geoffrey Bradway on building a trading system that synthesizes many different models.

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In this episode of the Data Show, I spoke with Geoffrey Bradway, VP of engineering at Numerai, a new hedge fund that relies on contributions of external data scientists. The company hosts regular competitions where data scientists submit machine learning models for classification tasks. The most promising submissions are then added to an ensemble of models that the company uses to trade in real-world financial markets.

To minimize model redundancy, Numerai filters out entries that produce signals that are already well-covered by existing models in their ensemble. The company also plans to use (Ethereum) blockchain technology to develop an incentive system to reward models that do well on live data (not ones that overfit and do well on historical data).

Here are some highlights from our conversation:
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Creating large training data sets quickly

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The O’Reilly Data Show Podcast: Alex Ratner on why weak supervision is the key to unlocking dark data.

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In this episode of the Data Show, I spoke with Alex Ratner, a graduate student at Stanford and a member of Christopher Ré’s Hazy research group. Training data has always been important in building machine learning algorithms, and the rise of data-hungry deep learning models has heightened the need for labeled data sets. In fact, the challenge of creating training data is ongoing for many companies; specific applications change over time, and what were gold standard data sets may no longer apply to changing situations.

Ré and his collaborators proposed a framework for quickly building large training data sets. In essence, they observed that high-quality models can be constructed from noisy training data. Some of these ideas were discussed in a previous episode featuring Mike Cafarella (jump to minute 24:16 for a description of an earlier project called DeepDive).

By developing a framework for mining low-quality sources in order to build high-quality machine learning models, Ré and his collaborators help researchers extract information previously hidden in unstructured data sources (so-called “dark data” buried in text, images, charts, and so on).

Here are some highlights from my conversation with Ratner:
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