Language understanding remains one of AI’s grand challenges

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The O’Reilly Data Show Podcast: David Ferrucci on the evolution of AI systems for language understanding.

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In this episode of the Data Show, I spoke with David Ferrucci, founder of Elemental Cognition and senior technologist at Bridgewater Associates. Ferrucci served as principal investigator of IBM’s DeepQA project and led the Watson team that became champion of the Jeopardy! quiz show. Elemental Cognition (EC) is a research group focused on building an AI system that will be equipped with state-of-the-art natural language understanding technologies. Ferrucci envisions that EC will ship with foundational knowledge in many subject areas, but will be able to very quickly acquire knowledge in other (specialized) domains with the help of “human mentors.”

Having built and deployed several prominent AI systems through the years, I also wanted to get Ferrucci’s perspective on the evolution of AI technologies, and how enterprises can take advantage of all the exciting recent developments.

Here are some highlights from our conversation:
Continue reading “Language understanding remains one of AI’s grand challenges”

Data preparation in the age of deep learning

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The O’Reilly Data Show Podcast: Lukas Biewald on why companies are spending millions of dollars on labeled data sets.

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In this episode of the Data Show, I spoke with Lukas Biewald, co-founder and chief data scientist at CrowdFlower. In a previous episode we covered how the rise of deep learning is fueling the need for large labeled data sets and high-performance computing systems. CrowdFlower has a service that many leading companies have come to rely on to provide them with labeled data sets to train machine learning models. As deep learning models get larger and more complex, they require training data sets that are bigger than those required by other machine learning techniques.

The CrowdFlower platform combines the contributions of human workers and algorithms. Through a process called active learning, they send difficult tasks or edge cases to humans, and they let the algorithms handle the more routine examples. But, how do you decide when to use human workers? In a simple example involving building an automatic classifier, you will probably want to send human workers cases when your machine learning algorithms signal uncertainty (probability scores are on the low side) or when your ensemble of machine learning algorithms signals disagreement. As Biewald describes in our conversation, active learning is much more subtle, and the CrowdFlower platform, in particular, is able to combine humans and algorithms to handle more sophisticated tasks.

Here are some highlights from our conversation:
Continue reading “Data preparation in the age of deep learning”

Scaling machine learning

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The O’Reilly Data Show Podcast: Reza Zadeh on deep learning, hardware/software interfaces, and why computer vision is so exciting.

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In this episode of the Data Show, I spoke with Reza Zadeh, adjunct professor at Stanford University, co-organizer of ScaledML, and co-founder of Matroid, a startup focused on commercial applications of deep learning and computer vision. Zadeh also is the co-author of the forthcoming book TensorFlow for Deep Learning (now in early release). Our conversation took place on the eve of the recent ScaledML conference, and much of our conversation was focused on practical and real-world strategies for scaling machine learning. In particular, we spoke about the rise of deep learning, hardware/software interfaces for machine learning, and the many commercial applications of computer vision.

Prior to starting Matroid, Zadeh was immersed in the Apache Spark community as a core member of the MLlib team. As such, he has firsthand experience trying to scale algorithms from within the big data ecosystem. Most recently, he’s been building computer vision applications with TensorFlow and other tools. While most of the open source big data tools of the past decade were written in JVM languages, many emerging AI tools and applications are not. Having spent time in both the big data and AI communities, I was interested to hear Zadeh’s take on the topic.

Here are some highlights from our conversation:
Continue reading “Scaling machine learning”

Architecting and building end-to-end streaming applications

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The O’Reilly Data Show Podcast: Karthik Ramasamy on Heron, DistributedLog, and designing real-time applications.

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In this episode of the Data Show, I spoke with Karthik Ramasamy, adjunct faculty member at UC Berkeley, former engineering manager at Twitter, and co-founder of Streamlio. Ramasamy managed the team that built Heron, an open source, distributed stream processing engine, compatible with Apache Storm.  While Ramasamy has seen firsthand what it takes to build and deploy large-scale distributed systems (within Twitter, he worked closely with the team that built DistributedLog), he is first and foremost interested in designing and building end-to-end applications. As someone who organizes many conferences, I’m all too familiar with the vast array of popular big data frameworks available. But, I also know that engineers and architects are most interested in content and material that helps them cut through the options and decisions.

Ramasamy and I discussed the importance of designing systems that can be combined to produce end-to-end applications with the requisite characteristics and guarantees.

Here are some highlights from our conversation:

Moving from Apache Storm to Heron

A major consideration was that we had to fundamentally change a lot of things. So, the team weighed the cost: should we go with an existing code base or develop a new code base? We thought that even if we developed a new code base, we would be able to get it done very quickly and the team was excited about it. That’s what we did and we got the first version of Heron done in eight or nine months.

I think it was one of the quickest transitions that ever happened in the history of Twitter. Apache Storm was hit by a lot of failures. There was a strong incentive to move to a new system. Once we proved the new system was highly reliable, we created a compelling value for the engineering teams. We also made it very painless for people to move. All they had to do was recompile a job and launch it. So, when you make a system like that, then people are just going to say, ‘let me give it a shot.’ They just compile it, launch it, then they say, ‘for a week, my job has been running without any issues; that’s good, I’m moving.’ So, we got migration done, from Storm to Heron, in less than six months. All the teams cooperated with us, and it was just amazing that we were able to get it done in less than six months. And we provided them a level of reliability that they never had with Storm.

Continue reading “Architecting and building end-to-end streaming applications”

Becoming a machine learning engineer

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The O’Reilly Data Show Podcast: Aurélien Géron on enabling companies to use machine learning in real-world products.

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In this episode of the Data Show, I spoke with Aurélien Géron, a serial entrepreneur, data scientist, and author of a popular, new book entitled Hands-on Machine Learning with Scikit-Learn and TensorFlow. Géron’s book is aimed at software engineers who want to learn machine learning and start deploying machine learning models in real-world products.

As more companies adopt big data and data science technologies, there is an emerging cohort of individuals who have strong software engineering skills and are experienced using machine learning and statistical techniques. The need to build data products has given rise to what many are calling “machine learning engineers”: individuals who can work on both data science prototypes and production systems.

data science machine learning jobs
Chart by Ben Lorica.

Géron is finding strong demand for his services as a consulting machine learning engineer, and he hopes his new book will be an important resource for those who want to enter the field.

Here are some highlights from our conversation:

Continue reading “Becoming a machine learning engineer”

Natural language analysis using Hierarchical Temporal Memory

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The O’Reilly Data Show Podcast: Francisco Webber on building HTM-based enterprise applications.

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In this episode of the Data Show, I spoke with Francisco Webber, founder of Cortical.io, a startup that is applying tools based on Hierarchical Temporal Memory (HTM) to natural language understanding. While HTM has been around for more than a decade, there aren’t many companies that have released products based on it (at least compared to other machine learning methods). Numenta, an organization developing open source machine intelligence based on the biology of the neocortex, maintains a community site featuring showcase applications. Webber’s company has been building tools based on HTM and applying them to big text data in a variety of industries; financial services has been a particularly strong vertical for Cortical.

Here are some highlights from our conversation:
Continue reading “Natural language analysis using Hierarchical Temporal Memory”

Deep learning that’s easy to implement and easy to scale

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The O’Reilly Data Show Podcast: Anima Anandkumar on MXNet, tensor computations and deep learning, and techniques for scaling algorithms.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS.

In this episode of the Data Show, I spoke with Anima Anandkumar, a leading machine learning researcher, and currently a principal research scientist at Amazon. I took the opportunity to get an update on the latest developments on the use of tensors in machine learning. Most of our conversation centered around MXNet—an open source, efficient, scalable deep learning framework. I’ve been a fan of MXNet dating back to when it was a research project out of CMU and UW, and I wanted to hear Anandkumar’s perspective on its recent progress as a framework for enterprises and practicing data scientists.

Here are some highlights from our conversation:
Continue reading “Deep learning that’s easy to implement and easy to scale”