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…
During the first weeks of February, we asked recipients of our Data and AI Newsletters to participate in a survey on AI adoption in the enterprise. We were interested in answering two questions. First, we wanted to understand how the use of AI grew in the past year. We were also interested in the practice…
It seems harder than ever to agree with others on basic facts, let alone to develop shared values and goals: we even claim to live in a post-truth era1. With anti-vaxxers, QAnon, Bernie Bros, flat earthers, the intellectual dark web, and disagreement worldwide as to the seriousness of COVID-19 and the effectiveness of masks, have…
Programs like AlphaZero and GPT-3 are massive accomplishments: they represent years of sustained work solving a difficult problem. But these problems are squarely within the domain of traditional AI. Playing Chess and Go or building ever-better language models have been AI projects for decades. The following projects have a different flavor: In February, PLOS Genetics…
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…
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…
Many experimental works have observed that generalization in deep RL appears to be difficult: although RL agents can learn to perform very complex tasks, they don’t seem to generalize over diverse task distributions as well as the excellent generalization of supervised deep nets might lead us to expect. In this blog post, we will aim…
An example of our method deployed on a Clearpath Jackal ground robot (left) exploring a suburban environment to find a visual target (inset). (Right) Egocentric observations of the robot. Imagine you’re in an unfamiliar neighborhood with no house numbers and I give you a photo that I took a few days ago of my house,…
Many experimental works have observed that generalization in deep RL appears to be difficult: although RL agents can learn to perform very complex tasks, they don’t seem to generalize over diverse task distributions as well as the excellent generalization of supervised deep nets might lead us to expect. In this blog post, we will aim…