Race and Technology: A Research Lecture Series features 14 distinguished scholars and domain experts from a diverse range of research areas and disciplines. From top left: Dr. Sareeta Amrute, Dr. Kim TallBear, Dr. Charlton McIlwain, Dr. Ruha Benjamin, Dr. Lisa Nakamura, Dr. Simone Browne, and Dr. André Brock. From bottom left: Dr. Sohini Ramachandran, Dr. C. Brandon Ogbunu, Dr. Kishonna L. Gray, Dr. Desmond Upton Patton, Merisa Heu-Weller, J.D., Dr. Denae Ford Robinson, and Dr.…
In this episode, Senior Principal Researcher Hunt Allcott talks with Postdoctoral Researcher Evan Rose about Allcott’s work exploring the everyday decisions people face, like buying fuel-efficient cars or taking out payday loans, and how a clearer understanding of these decisions can shape meaningful public policy. Allcott shares how his and others’ research shows that policy can often have complex outcomes resulting in hidden benefits and drawbacks, as in the case of taxes on sugary beverages.…
Interviewed by Senior Principal Researcher Hunt Allcott, Economist David Rothschild discusses how the news media has evolved alongside social media and the internet, from story development to distribution of news via aggregators and wire services. Rothschild illuminates how and where people are consuming news and shares some of the strategies he’s seeing news outlets use to appeal to their audiences. He also covers research insights into media bias, misinformation, and how this knowledge could inform…
Susan Dumais was inducted into the first class of the ACM SIGIR Academy in recognition of her significant, cumulative contributions to the development of the field of information retrieval (IR). Susan is a leader in IR, and has shaped the discipline through significant contributions to research and innovation. She is joined by 24 other individuals around the globe: James Allan, Ricardo Baeza-Yates, Nicholas Belkin, Andrei Broder, Jamie Callan, William Cooper, W. Bruce Croft, Edward Fox,…
Reinforcement learning is about agents taking information from the world and learning a policy for interacting with it, so that they perform better. So, you can imagine a future where, every time you type on the keyboard, the keyboard learns to understand you better. Or every time you interact with some website, it understands better what your preferences are, so the world just starts working better and better at interacting with people.John Langford, Partner Research…
Sber and Microsoft Research summed up the results of a joint project launched in October 2019 to develop a unique AI system that offers the opportunity to teach robots to manipulate physical objects of unstable shape in almost the same way that humans do.
Countless companies use online recommendation engines to show customers products and experiences that match their interests. And yet, traditional machine learning models that predict what people might prefer are often based on data from past experience.
Last week at ICML 2020, Mikael Henaff, Akshay Krishnamurthy, John Langford and Dipendra Misra had a paper on a new reinforcement learning (RL) algorithm that solves three key problems in RL: (i) global exploration, (ii) decoding latent dynamics, and (iii) optimizing a given reward function. Their ICML poster is here.
At ICML 2020, Mikael Henaff, Akshay Krishnamurthy, John Langford and Dipendra Misra published a paper presenting a new reinforcement learning (RL) algorithm called HOMER that addresses three main problems in real-world RL problem: (i) exploration, (ii) decoding latent dynamics, and (iii) optimizing a given reward function. ArXiv version of the paper can be found here, and the ICML version would be released soon.
