Building tools for the AI applications of tomorrow

[A version of this post appears on the O’Reilly Radar.]

We’re currently laying the foundation for future generations of AI applications, but we aren’t there yet.

By Ben Lorica and Mike Loukides

For the last few years, AI has been almost synonymous with deep learning (DL). We’ve seen AlphaGo touted as an example of deep learning. We’ve seen deep learning used for naming paint colors (not very successfully), imitating Rembrandt and other great painters, and many other applications. Deep learning has been successful in part because, as François Chollet tweeted, “you can achieve a surprising amount using only a small set of very basic techniques.” In other words, you can accomplish things with deep learning that don’t require you to become an AI expert. Deep learning’s apparent simplicity–the small number of basic techniques you need to know–makes it much easier to “democratize” AI, to build a core of AI developers that don’t have Ph.D.s in applied math or computer science.

But having said that, there’s a deep problem with deep learning. As Ali Rahimi has argued, we can often get deep learning to work, but we aren’t close to understanding how, when, or why it works: “we’re equipping [new AI developers] with little more than folklore and pre-trained deep nets, then asking them to innovate. We can barely agree on the phenomena that we should be explaining away.” Deep learning’s successes are suggestive, but if we can’t figure out why it works, its value as a tool is limited. We can build an army of deep learning developers, but that won’t help much if all we can tell them is, “Here are some tools. Try random stuff. Good luck.”

However, nothing is as simple as it seems. The best applications we’ve seen to date have been hybrid systems. AlphaGo wasn’t a pure deep learning engine; it incorporated Monte Carlo Tree Search, and at least two deep neural networks. At O’Reilly’s New York AI Conference in 2017, Josh Tenenbaum and David Ferrucci sketched out systems they are working on, systems that combine deep learning with other ideas and methods. Tenenbaum is working with one-shot learning, imitating the human ability to learn based on a single experience, and Ferrucci is working on building cognitive models that enable machines to understand human language in a meaningful way, not just pattern matching. DeepStack’s poker playing system combines neural networks with counterfactual regret minimization and heuristic search.
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Teaching and implementing data science and AI in the enterprise

[A version of this post appears on the O’Reilly Radar.]

The O’Reilly Data Show Podcast: Jerry Overton on organizing data teams, agile experimentation, and the importance of ethics in data science.

In this episode of the Data Show, I spoke with Jerry Overton, senior principal and distinguished technologist at DXC Technology. I wanted the perspective of someone who works across industries and with a variety of companies. I specifically wanted to explore the current state of data science and AI within companies and public sector agencies. As much as we talk about use cases, technologies, and algorithms, there are also important issues that practitioners like Overton need to address, including privacy, security, and ethics. Overton has long been involved in teaching and mentoring new data scientists, so we also discussed some tips and best practices he shares with new members of his team.

Here are some highlights from our conversation:

Where most companies are in their data journey

Five years ago, we had this moneyball phase where moneyball was new. This idea that you could actually get to value with data, and that data would have something to say that could help you run your business better.

We’ve gone way past that now to where I think it’s pretty much a premise that if you aren’t using your data, you’re losing out on a very big competitive advantage. I think it’s pretty much a premise that data science is necessary and that you need to do something. Now, the big thing is that companies are really unsure as to what their data scientists should be doing—which areas of their business they can make smarter and how to make it smarter.
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The importance of transparency and user control in machine learning

[A version of this post appears on the O’Reilly Radar.]

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.’

Continue reading “The importance of transparency and user control in machine learning”