AI Machine Learning & Data Science Research

DeepMind Explores the Connection Between Gradient-Based Meta-Learning and Convex Optimization

In the new paper Optimistic Meta-Gradients, a DeepMind research team explores the connection between gradient-based meta-learning and convex optimization, demonstrating that optimism in meta-learning is achievable via the Bootstrapped Meta-Gradients approach.

AI Machine Learning & Data Science Research

Google & Lund U’s Optimus Learned Optimization Architecture Efficiently Captures Complex Dependencies

In the new paper Transformer-Based Learned Optimization, a Google Research and Lund University team presents Optimus, an expressive neural network architecture for learned optimization that captures complex dependencies in the parameter space and achieves competitive results on real-world tasks and benchmark optimization problems.

AI Machine Learning & Data Science Research

Google & UC Berkeley’s Data-Driven Offline Optimization Approach Significantly Boosts Hardware Accelerator Performance, Reduces Simulation Time by More Than 90%

A research team from Google Research and UC Berkeley proposes PRIME, an offline data-driven approach that can architect hardware accelerators without any form of simulations. Compared to state-of-the-art simulation-driven methods, PRIME achieves impressive performance improvements of up to 1.54× while reducing the total required simulation time by up to 99 percent.


How AI Can Help The Oil Industry

To apply AI technology to the oil and gas industry, oil companies and startups generally first establish either a research group or a research center for the purpose. AI for oil and gas is a huge potential market, expected to reach US$2.85 billion by 2022.