A couple of years ago, Pete Skomoroch, Roger Magoulas, and I talked about the problems of being a product manager for an AI product. We decided that would be a good topic for an article, and possibly more. After Pete and I wrote the first article for O’Reilly Radar, it was clear that there was…
Much has been written about struggles of deploying machine learning projects to production. As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like…
Kevlin Henney and I were riffing on some ideas about GitHub Copilot, the tool for automatically generating code base on GPT-3’s language model, trained on the body of code that’s in GitHub. This article poses some questions and (perhaps) some answers, without trying to present any conclusions. First, we wondered about code quality. There are…
In June 2021, we asked the recipients of our Data & AI Newsletter to respond to a survey about compensation. The results gave us insight into what our subscribers are paid, where they’re located, what industries they work for, what their concerns are, and what sorts of career development opportunities they’re pursuing. While it’s sadly premature to…
In the first article of this series, we discussed communal computing devices and the problems they create–or, more precisely, the problems that arise because we don’t really understand what “communal” means. Communal devices are intended to be used by groups of people in homes and offices. Examples include popular home assistants and smart displays like…
Home assistants and smart displays are being sold in record numbers, but they are built wrong. They are designed with one person in mind: the owner. These technologies need to fit into the communal spaces where they are placed, like homes and offices. If they don’t fit, they will be unplugged and put away due…
OpenAI’s text generating system GPT-3 has captured mainstream attention. GPT-3 is essentially an auto-complete bot whose underlying Machine Learning (ML) model has been trained on vast quantities of text available on the Internet. The output produced from this autocomplete bot can be used to manipulate people on social media and spew political propaganda, argue about…
Sequence Modeling Solutions for Reinforcement Learning Problems Long-horizon predictions of (top) the Trajectory Transformer compared to those of (bottom) a single-step dynamics model. Modern machine learning success stories often have one thing in common: they use methods that scale gracefully with ever-increasing amounts of data. This is particularly clear from recent advances in sequence modeling,…
Processing raw sensory inputs is crucial for applying deep RL algorithms to real-world problems. For example, autonomous vehicles must make decisions about how to drive safely given information flowing from cameras, radar, and microphones about the conditions of the road, traffic signals, and other cars and pedestrians. However, direct “end-to-end” RL that maps sensor data…
Fig. 1: The BRIDGE dataset contains 7200 demonstrations of kitchen-themed manipulation tasks across 71 tasks in 10 domains. Note that any GIF compression artifacts in this animation are not present in the dataset itself. When we apply robot learning methods to real-world systems, we must usually collect new datasets for every task, every robot, and…