Acknowledgements Special thanks to Michael Reneer for his critical contributions in setting up Parfait. Direct contributors to work on Parfait repositories include Galen Andrew, Isha Arkatkar, Sean Augenstein, Kallista Bonawitz, Amlan Chakraborty, Zachary Charles, Stanislav Chiknavaryan, DeWitt Clinton, Taylor Cramer, Katharine Daly, Stefan Dierauf, Randy Dodgen, Hubert Eichner, Nova Fallen, Ken Franko, Zachary Garrett, Emily […]

Humans possess a remarkable ability to localize sound sources and perceive the surrounding environment through auditory cues alone. This sensory ability, known as spatial hearing, plays a critical role in numerous everyday tasks, including identifying speakers in crowded conversations and navigating complex environments. Hence, emulating a coherent sense of space via listening devices like headphones

As artificial intelligence (AI) continues to advance, the landscape is becoming increasingly competitive and ethically fraught. Companies like Anthropic, which have missions centered on developing “safe AI,” face unique challenges in an ecosystem where speed, innovation, and unconstrained power are often prioritized over safety and ethical considerations. In this post, we explore whether such companies

Seeing as transformers and MPNNs are not the only ML approaches for the structural analysis of graphs, we also compared the analytical capabilities of a wide variety of other GNN- and transformer-based architectures. For GNNs, we compared both transformers and MPNNs to models like graph convolutional networks (GCNs) and graph isomorphism networks (GINs). Additionally, we

We made strong headway in ML foundations, with extensive work on algorithms, efficiency, data and privacy. We improved ML efficiency through pioneering techniques that reduce the inference times of LLMs, which were implemented across Google products and adopted throughout the industry. Our research on cascades presents a method for leveraging smaller models for “easy” outputs

Responsibility & Safety Published 17 December 2024 Authors FACTS team Our comprehensive benchmark and online leaderboard offer a much-needed measure of how accurately LLMs ground their responses in provided source material and avoid hallucinations Large language models (LLMs) are transforming how we access information, yet their grip on factual accuracy remains imperfect. They can “hallucinate”

What is your take on blog post “Why AI Needs Large Numerical Models (LNMs) for Mathematical Mastery“? Thought about large numerical and mathematics models for a few seconds.Confirming Additional BreakthroughsOK, I’m confirming if LNMs/LMMs need more than Transformer models to match LLM performance, and noting the user’s comprehensive response. Yes. While the Transformer architecture provided

The availability and structure of mathematical training data, combined with the unique characteristics of mathematics itself, suggest that training a Large Numerical Model (LNM) is feasible and may require less data than training a general-purpose LLM. Here’s a detailed look: Availability of Mathematical Training Data Structure of Mathematics and Data Efficiency Mathematics’ highly structured nature