Stephan Robert-Nicoud

OpenAI, the maker of ChatGPT, says it intends to set up an office in Zurich. The move is the latest development in the company's European expansion plans, after setting up offices in London, Paris, Brussels and Dublin.

Abstract  Neural architecture search (NAS) emerged as a way to automatically optimize neural networks for a specific task and dataset. Despite an abundance of research on NAS for images and natural language applications, similar studies for time series data are lacking. Among NAS search spaces, chain-structured are the simplest and most applicable to small datasets like time series. We compare three popular NAS strategies on chain-structured search spaces: Bayesian optimization (specifically Tree-structured Parzen Estimator), the hyperband method, and reinforcement learning in the context of financial time series forecasting. These strategies were employed to optimize simple well-understood neural architectures like the MLP, 1D CNN, and RNN, with more complex temporal fusion transformers (TFT) and their own optimizers included for comparison. We find Bayesian optimization and the hyperband method performing best among the strategies, and RNN and 1D CNN best among the architectures,...

 From Open AI:  Introducing OpenAI o1-preview A new series of reasoning models for solving hard problems. We've developed a new series of AI models designed to spend more time thinking before they respond. They can reason through complex tasks and solve harder problems than previous models in science, coding, and math. Today, we are releasing the first of this series in ChatGPT and our API. This is a preview and we expect regular updates and improvements. Alongside this release, we’re also including evaluations for the next update, currently in development.

AI achieves silver-medal standard solving International Mathematical Olympiad problems Breakthrough models AlphaProof and AlphaGeometry 2 solve advanced reasoning problems in mathematics Artificial general intelligence (AGI) with advanced mathematical reasoning has the potential to unlock new frontiers in science and technology. We’ve made great progress building AI systems that help mathematicians discover new insights, novel algorithms and answers to open problems. But current AI systems still struggle with solving general math problems because of limitations in reasoning skills and training data. Today, we present AlphaProof, a new reinforcement-learning based system for formal math reasoning, and AlphaGeometry 2, an improved version of our geometry-solving system. Together, these systems solved four out of six problems from this year’s International Mathematical Olympiad (IMO), achieving the same level as a silver medalist in the competition for the first time.

  Avi Wigderson Receives ACM A.M. Turing Award for Groundbreaking Insights on Randomness Leading Theoretical Computer Scientist Cited for Field-Defining Contributions New York, NY, April 10, 2024  – ACM, the Association for Computing Machinery, today named Avi Wigderson as recipient of the 2023 ACM A.M. Turing Award for foundational contributions to the theory of computation, including reshaping our understanding of the role of randomness in computation, and for his decades of intellectual leadership in theoretical computer science. Wigderson is the Herbert H. Maass Professor in the School of Mathematics at the Institute for Advanced Study in Princeton, New Jersey. He has been a leading figure in areas including computational complexity theory, algorithms and optimization, randomness and cryptography, parallel and distributed computation, combinatorics, and graph theory, as well as connections between theoretical computer science and mathematics and science. The ACM A.M. Turin...

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