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…
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…
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…
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…
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…
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…
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…
Diagram of MURAL, our method for learning uncertainty-aware rewards for RL. After the user provides a few examples of desired outcomes, MURAL automatically infers a reward function that takes into account these examples and the agent’s uncertainty for each state. Although reinforcement learning has shown success in domains such as robotics, chip placement and playing…
Fig 1. Measures of generalization performance for neural networks trained on four different boolean functions (colors) with varying training set size. For both MSE (left) and learnability (right), theoretical predictions (curves) closely match true performance (dots). Deep learning has proven a stunning success for countless problems of interest, but this success belies the fact that,…
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…