The importance of transparency and user control in machine learning

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The O’Reilly Data Show Podcast: Guillaume Chaslot on bias and extremism in content recommendations.

In this episode of the Data Show, I spoke with Guillaume Chaslot, an ex-YouTube engineer and founder of AlgoTransparency, an organization dedicated to helping the public understand the profound impact algorithms have on our lives. We live in an age when many of our interactions with companies and services are governed by algorithms. At a time when their impact continues to grow, there are many settings where these algorithms are far from transparent. There is growing awareness about the vast amounts of data companies are collecting on their users and customers, and people are starting to demand control over their data. A similar conversation is starting to happen about algorithms—users are wanting more control over what these models optimize for and an understanding of how they work.

I first came across Chaslot through a series of articles about the power and impact of YouTube on politics and society. Many of the articles I read relied on data and analysis supplied by Chaslot. We talked about his work trying to decipher how YouTube’s recommendation system works, filter bubbles, transparency in machine learning, and data privacy.

Here are some highlights from our conversation:

Why YouTube’s impact is less understood

My theory why people completely overlooked YouTube is because on Facebook and Twitter, if one of your friends posts something strange, you’ll see it. Even if you have 1,000 friends, if one of them posts something really disturbing, you see it, so you’re more aware of the problem. Whereas on YouTube, some people binge watch some very weird things that could be propaganda, but we won’t know about it because we don’t see what other people see. So, YouTube is like a TV channel that doesn’t show the same thing to everybody and when you ask YouTube, “What did you show to other people?” YouTube says, ‘I don’t know, I don’t remember, I don’t want to tell you.’

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What machine learning engineers need to know

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The O’Reilly Data Show Podcast: Jesse Anderson and Paco Nathan on organizing data teams and next-generation messaging with Apache Pulsar.

In this episode of the Data Show, I spoke Jesse Anderson, managing director of the Big Data Institute, and my colleague Paco Nathan, who recently became co-chair of Jupytercon. This conversation grew out of a recent email thread the three of us had on machine learning engineers, a new job role that LinkedIn recently pegged as the fastest growing job in the U.S. In our email discussion, there was some disagreement on whether such a specialized job role/title was needed in the first place. As Eric Colson pointed out in his beautiful keynote at Strata Data San Jose, when done too soon, creating specialized roles can slow down your data team.

We recorded this conversation at Strata San Jose, while Anderson was in the middle of teaching his very popular two-day training course on real-time systems. We closed the conversation with Anderson’s take on Apache Pulsar, a very impressive new messaging system that is starting to gain fans among data engineers.

Here are some highlights from our conversation:

Why we need machine learning engineers

Jesse Anderson: One of the issues I’m seeing as I work with teams is that they’re trying to operationalize machine learning models, and the data scientists are not the one to productionize these. They simply don’t have the engineering skills. Conversely, the data engineers don’t have the skills to operationalize this either. So, we’re seeing this kind of gap in between the data science and the data engineering, and the gap I’m seeing and the way I’m seeing it being filled, is through a machine learning engineer.

… I disagree with Paco that generalization is the way to go. I think it’s hyper-specialization, actually. This is coming from my experience having taught a lot of enterprises. At a startup, I would say that super-specialization is probably not going to be as possible, but at an enterprise, you are going to have to have a team that specializes in big data, and that is a part from a team, even a software engineering team, that doesn’t work with data.

Putting Apache Pulsar on the radar of data engineers

Key features of Apache Pulsar. Image by Karthik Ramasamy, used with permission.


Jesse Anderson: A lot of my time, since I’m really teaching data engineering is spent on data integration and data ingestion. How do we move this data around efficiently? For a lot of that time Kafka was really the only open source game in town for that. But now there’s another technology called Apache Pulsar. I’ve spent a decent amount of time actually going through Pulsar and there are some things that I see in it that Kafka will either have difficulty doing or won’t be able to do.

… Apache Pulsar separates pub-sub from storage. When I first read about that, I didn’t quite get it. I didn’t quite see, why is this so important or why this is so interesting. It’s because you can individually scale your pub-sub and your storage resources independently. Now you’ve got something. Now you can say, “Well, we originally decided I wanted to store data for seven days. All right, let’s spin up some more bookkeeper processes and now we can store fourteen days, now we can store twenty one days.” I think that’s going to be a pretty interesting addition there. Where the other side of that, the corollary to that is, “Okay, we’re hitting Black Friday and we don’t have so much more data coming through as we have way more consumption and have way more things hitting our pub-sub. We could spin up more pub-sub with that.” This separation is actually allowing some interesting use cases.

Related resources:

How to train and deploy deep learning at scale

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The O’Reilly Data Show Podcast: Ameet Talwalkar on large-scale machine learning.

In this episode of the Data Show, I spoke with Ameet Talwalkar, assistant professor of machine learning at CMU and co-founder of Determined AI. He was an early and key contributor to Spark MLlib and a member of AMPLab. Most recently, he helped conceive and organize the first edition of SysML, a new academic conference at the intersection of systems and machine learning (ML).

We discussed using and deploying deep learning at scale. This is an empirical era for machine learning, and, as I noted in an earlier article, as successful as deep learning has been, our level of understanding of why it works so well is still lacking. In practice, machine learning engineers need to explore and experiment using different architectures and hyperparameters before they settle on a model that works for their specific use case. Training a single model usually involves big (labeled) data and big models; as such, exploring the space of possible model architectures and parameters can take days, weeks, or even months. Talwalkar has spent the last few years grappling with this problem as an academic researcher and as an entrepreneur. In this episode, he describes some of his related work on hyperparameter tuning, systems, and more.

Here are some highlights from our conversation:

Deep learning

I would say that you hear a lot about the modeling of problems associated with deep learning. How do I frame my problem as a machine learning problem? How do I pick my architecture? How do I debug things when things go wrong? … What we’ve seen in practice is that, maybe somewhat surprisingly, the biggest challenges that ML engineers face actually are due to the lack of tools and software for deep learning. These problems are sort of like hybrid systems/ML problems. Very similar to the sorts of research that came out of the AMPLab.

… Things like TensorFlow and Keras, and a lot of those other platforms that you mentioned, are great and they’re a great step forward. They’re really good at abstracting low-level details of a particular learning architecture. In five lines, you can describe how your architecture looks and then you can also specify what algorithms you want to use for training.

There are a lot of other systems challenges associated with actually going end to end, from data to a deployed model. The existing software solutions don’t really tackle a big set of these challenges. For example, regardless of the software you’re using, it takes days to weeks to train a deep learning model. There’s real open challenges of how to best use parallel and distributed computing both to train a particular model and in the context of tuning hyperparameters of different models.
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Using machine learning to monitor and optimize chatbots

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The O’Reilly Data Show Podcast: Ofer Ronen on the current state of chatbots.

In this episode of the Data Show, I spoke with Ofer Ronen, GM of Chatbase, a startup housed within Google’s Area 120. With tools for building chatbots becoming accessible, conversational interfaces are becoming more prevalent. As Ronen highlights in our conversation, chatbots are already enabling companies to automate many routine tasks (mainly in customer interaction). We are still in the early days of chatbots, but if current trends persist, we’ll see bots deployed more widely and take on more complex tasks and interactions. Gartner recently predicted that by 2021, companies will spend more on bots and chatbots than mobile app development.

Like any other software application, as bots get deployed in real-world applications, companies will need tools to monitor their performance. For a single, simple chatbot, one can imagine developers manually monitoring log files for errors and problems. Things get harder as you scale to more bots and as the bots get increasingly more complex. As in the case of other machine learning applications, when companies start deploying many more chatbots, automated tools for monitoring and diagnostics become essential.

The good news is relevant tools are beginning to emerge. In this episode, Ronen describes a tool he helped build: Chatbase is a chatbot analytics and optimization service that leverages machine learning research and technologies developed at Google. In essence, Chatbase lets companies focus on building and deploying the best possible chatbots.

Here are some highlights from our conversation:

Democratization of tools for bot developers

It’s been hard to get the natural language processing to work well and to recognize all the different ways people might say the same thing. There’s been an explosion of tools that leverage machine learning and natural language processing (NLP) engines to make sense of all that’s being asked of bots. But with increased capacity and capability to process data, there’s now better third-party tools for any company to take advantage of and build a decent bot out of the box.

… I see three levels of bot builders out there. There’s the non-technical kind where marketing or sales might create a prototype using a user interface—like maybe Chatfuel, which requires no programming, and create a basic experience. Or they might even create some sort of decision tree bot that is not flexible, but is good for maybe basic lead-gen experiences. But they often can’t handle type-ins. It’s often button-based. So, that’s one level, the non-technical folks.
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Unleashing the potential of reinforcement learning

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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 led data and machine learning teams at Microsoft, Amazon, and Uber, where his teams were responsible for building data science tools used by other developers and analysts within those companies. When I first heard that he was moving to Unity, I was curious as to why he decided to join a company whose core product targets game developers.

As you’ll glean from our conversation, Unity is at the forefront of some of the most exciting, practical applications of deep learning (DL) and reinforcement learning (RL). Realistic scenery and imagery are critical for modern games. GANs and related semi-supervised techniques can ease content creation by enabling artists to produce realistic images much more quickly. In a previous post, Lange described how reinforcement learning opens up the possibility of training/learning rather than programming in game development.

Lange explains why simulation environments are going to be important tools for AI developers. We are still in the early days of machine intelligence, and I am looking forward to more tools that can democratize AI research (including future releases by Lange and his team at Unity).

Here are some highlights from our conversation:

Why reinforcement learning is so exciting

I’m a huge fan of reinforcement learning. I think it has incredible potential, not just in game development but in a lot of other areas, too. … What we are doing at Unity is basically making reinforcement learning available to the masses. We have shipped open source software on GitHub called Unity ML Agents, that include the basic frameworks for people to experiment with reinforcement learning. Reinforcement learning is really creating a machine learned-driven feedback loop. Recall the example I previously wrote about, of the chicken crossing the road; yes, it gets hit thousands and thousands of times by these cars, but every time it gets hit, it learns that’s a bad thing. And every time it manages to pick up a gift package on the way over the road, that’s a good thing.

Over time, it gets superhuman capabilities in crossing this road, and that is fantastic because there’s not a single line of code going into that. It’s pure simulation, and through reinforcement learning it captures a method. It learns a method to cross the road, and you can take that into many different aspects of games. There are many different methods you can train. You can add two chickens—can they collaborate to do something together? We are looking at what we call multi-agent systems, where two or more of these trained reinforcement learning-trained agents are acting together to achieve a goal.
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Graphs as the front end for machine learning

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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 of the large graphs generated by the early social media startups). In recent months, I’ve come across companies releasing and using new tools for creating, storing, and (most importantly) analyzing large graphs. There are many problems and use cases that lend themselves naturally to graphs, and recent advances in hardware and software building blocks have made large-scale analytics possible.

Starting with his work as a graduate student at UC Berkeley, Meyerovich has pioneered the combination of hardware and software acceleration to create truly interactive environments for visualizing large amounts of data. Graphistry has built a suite of tools that enables analysts to wade through large data sets and investigate business and security incidents. The company is currently focused on the security domain—where it turns out that graph representations of data are things security analysts are quite familiar with.

Here are some highlights from our conversation:

Graphs as the front end for machine learning

They’re really flexible. First of all, there’s a pure analytic reason in that there are certain types of queries that one could do efficiently with a graph database. If you needed do a bunch of joins, graphs are really great at that. … Companies want to get into stuff like 360-degree views of things; they want to understand correlations to actually explain what’s going on at a more intelligent level.

… I think that’s where graphs really start to shine. Because companies deal with pretty heterogeneous data, and a graph ends up being a really easy way to deal with that. A lot of questions are basically, “What’s nearby?”—almost like your nearest neighbor type of stuff; the graph becomes, both at the query level and at the visual level, very interpretable. I now have a hypothesis about graphs as being the front end and the UI for machine learning, but that might be a topic for another day.

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Machine learning needs machine teaching

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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 industrial settings. While many articles have been written about developments in computer vision, speech recognition, and autonomous vehicles, I’m particularly excited about near-term applications of AI to manufacturing, robotics, and industrial automation. In a recent post, I outlined practical applications of reinforcement learning (RL)—a type of machine learning now being used in AI systems. In particular, I described how companies like Bonsai are applying RL to manufacturing and industrial automation. As researchers explore new approaches for solving RL problems, I expect many of the first applications to be in industrial automation.

Here are some highlights from our conversation:

Machine learning and machine teaching

Everyone is so focused on making better and faster learning algorithms; what do we do when we have it? Let’s just suppose that you now have an algorithm that can learn as well as or better than humans. How do we use that, how do we apply that in a predictable, scalable, repeatable way toward the objectives that we want to apply it toward?

… I thought about that for a while, and it’s one of those things where the answer is obvious in hindsight, but until you sit down and really chew on it, it doesn’t jump out at you. And it’s that, by design, if you’re building a learning system—if you want to program it—you have to teach it. Machine teaching and machine learning are necessary complements to one another; you need both. And for the large part, most of what comprises machine teaching these days consists of giant label data sets.

… You need machine teaching and machine learning. It dawned on me that this was the core abstraction that was going to make it possible for us to start applying all of this stuff more broadly across all the myriad use cases that we see in the real world without having to turn all of the people who are looking to use it into experts in machine learning and data science. It’s what enabled me to realize what Bonsai’s mission is: to enable your subject matter experts (a chemical engineer or a mechanical engineer, someone who is very, very well versed in whatever their domain is but not necessarily in machine learning or data science) to take that expertise and use it as the foundation for describing what to teach and then automating the underlying pieces for how you can actually effectively learn that.

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