Managing risk in machine learning

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Considerations for a world where ML models are becoming mission critical.

In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.

Let’s begin by looking at the state of adoption. We recently conducted a surveywhich garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. One of the things we learned was that many companies are still in the early stages of deploying machine learning (ML):

As far as reasons for companies holding back, we found from a survey we conducted earlier this year that companies cited lack of skilled people, a “skills gap,” as the main challenge holding back adoption.

Interest on the part of companies means the demand side for “machine learning talent” is healthy. Developers have taken notice and are beginning to learn about ML. In our own online training platform (which has more than 2.1 million users), we’re finding strong interest in machine learning topics. Below are the top search topics on our training platform:
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How social science research can inform the design of AI systems

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The O’Reilly Data Show Podcast: Jacob Ward on the interplay between psychology, decision-making, and AI systems.

In this episode of the Data Show, I spoke with Jacob Ward, a Berggruen Fellow at Stanford University. Ward has an extensive background in journalism, mainly covering topics in science and technology, at National Geographic, Al Jazeera, Discovery Channel, BBC, Popular Science, and many other outlets. Most recently, he’s become interested in the interplay between research in psychology, decision-making, and AI systems. He’s in the process of writing a book on these topics, and was gracious enough to give an informal preview by way of this podcast conversation.

Here are some highlights from our conversation:

Psychology and AI

I began to realize there was a disconnect between what is a totally revolutionary set of innovations coming through in psychology right now that are really just beginning to scratch the surface of how human beings make decisions; at the same time, we are beginning to automate human decision-making in a really fundamental way. I had a number of different people say, ‘Wow, what you’re describing in psychology really reminds me of this piece of AI that I’m building right now,’ to change how expectant mothers see their doctors or change how we hire somebody for a job or whatever it is.

Transparency and designing systems that are fair

I was talking to somebody the other day who was trying to build a loan company that was using machine learning to present loans to people. He and his company did everything they possibly could to not redline the people they were loaning to. They were trying very hard not to make unfair loans that would give preference to white people over people of color.

They went to extraordinary lengths to make that happen. They cut addresses out of the process. They did all of this stuff to try to basically neutralize the process, and the machine learning model still would pick white people at a disproportionate rate over everybody else. They can’t explain why. They don’t know why that is. There’s some variable that’s mapping to race that they just don’t know about.

But that sort of opacity—this is somebody explaining it to me who just happened to have been inside the company, but it’s not as if that’s on display for everybody to check out. These kinds of closed systems are picking up patterns we can’t explain, and that their creators can’t explain. They are also making really, really important decisions based on them. I think it is going to be very important to change how we inspect these systems before we begin trusting them.

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Using machine learning to improve dialog flow in conversational applications

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The O’Reilly Data Show Podcast: Alan Nichol on building a suite of open source tools for chatbot developers.

In this episode of the Data Show, I spoke with Alan Nichol, co-founder and CTO of Rasa, a startup that builds open source tools to help developers and product teams build conversational applications. About 18 months ago, there was tremendous excitement and hype surrounding chatbots, and while things have quieted lately, companies and developers continue to refine and define tools for building conversational applications. We spoke about the current state of chatbots, specifically about the types of applications developers are building today and how he sees conversational applications evolving in the near future.

As I described in a recent post, workflow automation will happen in stages. With that in mind, chatbots and intelligent assistants are bound to improveas underlying algorithms, technologies, and training data get better.

Here are some highlights from our conversation:

Chatbots and state machines

The first component is what we call natural language understanding, which typically means taking a short message that a user sends and extracting some meaning from it, which means turning it into structured data. In the case we talked about regarding the SQL database, if somebody asks, for example, ‘What was my ROI on my Facebook campaigns last month?’, the first thing you want to understand is that this is a data question and you want to assign it a label identifying it as a person, and they’re not saying hello, or goodbye, or thank you, but asking a specific question. Then you want to pick out those fields to help you create a query.
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Building accessible tools for large-scale computation and machine learning

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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 framework that lets data enthusiasts proficient with Python run existing code at massive scale on Amazon Web Services. Jonas and his collaborators are working on a related project, NumPyWren, a system for linear algebra built on a serverless architecture. Their hope is that by lowering the barrier to large-scale (scientific) computation, we will see many more experiments and research projects from communities that have been unable to easily marshal massive compute resources. We talked about Bayesian machine learning, scientific computation, reinforcement learning, and his stint as an entrepreneur in the enterprise software space.

Here are some highlights from our conversation:

Pywren

The real enabling technology for us was when Amazon announced the availability of AWS Lambda, their microservices framework, in 2014. Following this prompting, I went home one weekend and thought, ‘I wonder how hard it is to take an arbitrary Python function and marshal it across the wire, get it running in Lambda; I wonder how many I can get at once?’ Thus, Pywren was born.
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Notes from the first Ray meetup

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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 to support end-to-end applications. While some interesting applications of unsupervised learning are beginning to emerge, many current machine learning applications rely on supervised learning. In a recent series of posts, Ben Recht makes the case for why some of the most interesting problems might actually fall under reinforcement learning (RL), specifically systems that are able to act based upon past data and do so in a way that is safe, robust, and reliable.

But first we need RL tools that are accessible for practitioners. Unlike supervised learning, in the past there hasn’t been a good open source tool for easily trying RL at scale. I think things are about to change. I was fortunate enough to receive an invite to the first meetup devoted to RayRISE Lab’s high-performance, distributed execution engine, which targets emerging AI applications, including those that rely on reinforcement learning. This was a small, invite-only affair held at OpenAI, and most of the attendees were interested in reinforcement learning.

Here’s a brief rundown of the program:

  • Robert Nishihara and Philipp Moritz gave a brief overview and update on the Ray project, including a description of items on the near-term roadmap.
  • Eric Liang and Richard Liawgave a quick tour of two libraries built on top of Ray: RLlib(scalable reinforcement learning) and Tune(a hyperparameter optimization framework). They also pointed a to a recent ICML paper on RLlib. Both of these libraries are easily accessible to anyone familiar with Python, and both should prove popular among industrial data scientists.

RLlib and reinforcement learning. Image courtesy of RISE Lab.

  • Eugene Vinitsky showed some amazing videos of how Ray is helping them understand and predict traffic patterns in real time, and in the process help researchers study large transportation networks. The videos were some of the best examples of the combination of IoT, sensor networks, and reinforcement learning that I’ve seen.
  • Alex Bao of Ant Financial described three applications they’ve identified for Ray. I’m not sure I’m allowed to describe them here, but they were all very interesting and important use cases. The most important takeaway for the evening was Ant Financial is already using Ray in production in two of the three use cases (and they are close to deploying Ray to production for the third)! Given that Ant Financial is the largest unicorn company in the world, this is amazing validation for Ray.

With the buzz generated by the evening’s presentations and early examples of production deployments beginning to happen, I expect meetups on Ray to start springing up in other geographic areas. We are still in the early stages of adoption of machine learning technologies. The presentations at this meetup confirm that an accessible and scalable platform like Ray, opens up many possible applications of reinforcement learning and online learning.

For more on Ray:

Specialized hardware for deep learning will unleash innovation

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The O’Reilly Data Show Podcast: Andrew Feldman on why deep learning is ushering a golden age for compute architecture.

In this episode of the Data Show, I spoke with Andrew Feldman, founder and CEO of Cerebras Systems, a startup in the blossoming area of specialized hardware for machine learning. Since the release of AlexNet in 2012, we have seen an explosion in activity in machine learning, particularly in deep learning. A lot of the work to date happened primarily on general purpose hardware (CPU, GPU). But now that we’re six years into the resurgence in interest in machine learning and AI, these new workloads have attracted technologists and entrepreneurs who are building specialized hardware for both model training and inference, in the data center or on edge devices.

In fact, companies with enough volume have already begun building specialized processors for machine learning. But you have to either use specific cloud computing platforms or work at specific companies to have access to such hardware. A new wave of startups (including Cerebras) will make specialized hardware affordable and broadly available. Over the next 12-24 months architects and engineers will need to revisit their infrastructure and decide between general purpose or specialized hardware, and cloud or on-premise gear.

In light of the training durationand cost they face using current (general purpose) hardware, some experiments might be hard to justify. Upcoming specialized hardware will enable data scientists to try out ideas that they previously would have hesitated to pursue. This will surely lead to more research papers and interesting products as data scientists are able to run many more experiments (on even bigger models) and iterate faster.

As founder of one of the most anticipated hardware startups in the deep learning space, I wanted get Feldman’s views on the challenges and opportunities faced by engineers and entrepreneurs building hardware for machine learning workloads.

Here are some highlights from our conversation:
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Data collection and data markets in the age of privacy and machine learning

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While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data.

In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. My goal was to remind the data community about the many interesting opportunities and challenges in data itself. Much of the focus of recent press coverage has been on algorithms and models, specifically the expanding utility of deep learning. Because large deep learning architectures are quite data hungry, the importance of data has grown even more. In this short talk, I describe some interesting trends in how data is valued, collected, and shared.

Economic value of data

It’s no secret that companies place a lot of value on data and the data pipelines that produce key features. In the early phases of adopting machine learning (ML), companies focus on making sure they have sufficient amount of labeled (training) data for the applications they want to tackle. They then investigate additional data sources that they can use to augment their existing data. In fact, among many practitioners, data remains more valuable than models (many talk openly about what models they use, but are reticent to discuss the features they feed into those models).

But if data is precious, how do we go about estimating its value? For those among us who build machine learning models, we can estimate the value of data by examining the cost of acquiring training data:
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