Silicon Valley Is Pricing Academics Out of AI Research

Stanford University's Fei-Fei Li, an ACM Fellow known as the "godmother of AI," pressed President Joe Biden, following his State of the Union address, to fund a national warehouse of computing power and datasets to ensure the nation's leading AI researchers can keep pace with big tech firms. Said Li, "The public sector is now significantly lagging in resources and talent compared to that of industry. This will have profound consequences because industry is focused on developing technology that is profit-driven, whereas public sector AI goals are focused on creating public goods.". The Washington Post; Naomi Nix; Cat Zakrzewski; Gerrit De Vynck (March 10, 2024)

Comments

Post a Comment

Popular posts from this blog

 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.
Read more

Chain-structured neural architecture search for financial time series forecasting

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,...
Read more