I implement a prompt-based learning strategy to extract measures of sentiment and other features from confidential reference letters. I show that the contents of reference letters is clearly reflected in the performance of job market candidates in the Economics academic job market. In contrast, applying traditional ``bag-of-words'' approaches produces measures of sentiment that, while positively correlated to my LLM-based measure, are not predictive of job market outcomes. Using a random forest, I show that both letter quality and length are predictive of success in the job market. Letters authored by advisers appear to be as important as those written by other referees.

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The National Center For Artificial Intelligence (Cenia) was established in 2021 as a core component of the Chile National Policy for AI. Cenia is the main institution dedicated to AI in Chile, with a mission to be at the forefront of scientific and technological innovation in this field. As a main leading guide, Cenia promotes sustainable and ethical progress, in harmony with the environment and human development.

Samuel Kaski) - One of the core questions in machine learning at the moment is how to interact with humans. We turn this question into a probabilistic modelling problem, and model both the user and the task to drive the interaction. The solutions need combinations of probabilistic modelling, reinforcement learning and approximate Bayesian computation. We are looking for a postdoc who already masters some of these and offer an opportunity to learn the rest and work with us on this exciting bleeding-edge problem. Antti Oulasvirta) - The position offers an exciting opportunity to learn about and work on applications of machine learning methods and computational models of cognition, perception, and behavior in interactive systems.

DSR Lab at UF invites applications for a post doctoral position with a strong background in probabilistic models, knowledge bases and graph mining. The successful candidate will work on developing novel strategies to understand and reason about events, situations, and trends in large-scale probabilistic knowledge bases. The data sources where events come from is highly heterogeneous and include multiple representations (structured and unstructured), semantics (variety of schemas, ontologies, or complete absence of both), languages and modalities (text, image, video, sensors,etc). Moreover, interpretations also uses humans-in-the-loop for further adjustments. The position involve close scientific collaboration with Dr. Daisy Zhe Wang and interaction with two PhD students in the DSR lab on this project.

It is well known that the global optimum of a MDP with finite state and action sets can be obtained through methods based on dynamic programming. Unfortunately, these techniques are known to suffer from the curse of dimensionality, which makes them infeasible for many real-world problems of interest. As a result, most research in the reinforcement learning and control theory literature has focused on obtaining approximate or locally optimal solutions. There exists a broad spectrum of such techniques, including approximate dynamic programming methods, tree search methods, local trajectory-optimization techniques, such as differential dynamic programming and iLQG, and policy search methods. In this talk I shall provide an introduction to policy search methods, which are a family of algorithms that have proven extremely popular in recent years, and which have numerous desirable properties that make them attractive in practice.

Some of the smartest minds in the UK are being lured away from their research positions at Oxford and Cambridge by DeepMind -- a London-based AI lab that was acquired by Google for £400 million in 2014. More than a dozen AI researchers have left the academic powerhouses over the last couple of years for what are likely to be better-paid roles at DeepMind, according to LinkedIn. Steven Cave, the director of Cambridge University's new Centre for the Future of Intelligence, believes that the exodus of talent from academia to corporates is something of a problem. "The best people are being offered huge sums of money to go and work at these tech companies," Cave told Business Insider in Cambridge last week. "You find that you're talking to someone and they're expressing a great deal of interest in a research project and then they're snapped up. We understand that ambitious young people want to work at these big name companies and earn lots of money and that's fine. But at the same time we hope that there will be enough bright young things who are motivated by the intellectual challenge of the issues we're working on and by the sense of wanting to do something good that makes a difference for the world."