Researchers explore how to bring larger neural networks closer to the energy efficiency of biological brains

The more lottery tickets you buy, the higher your chances of winning, but spending more than you win is obviously not a wise strategy. Something similar happens in AI powered by deep learning: we know that the larger a neural network is (i.e., the more parameters it has), the better it can learn the task …

Effortless robot movements

Humans and animals move with remarkable economy without consciously thinking about it by utilizing the natural oscillation patterns of their bodies. A new tool can now utilize this knowledge for the first time to make robots move more efficiently.

Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models

This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP) Workshop at NeurIPS 2024. Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recent advancements in mixture of expert (MoE) models, speculative decoding, and early exit strategies …

ML 17714 image001

Build cost-effective RAG applications with Binary Embeddings in Amazon Titan Text Embeddings V2, Amazon OpenSearch Serverless, and Amazon Bedrock Knowledge Bases

Today, we are happy to announce the availability of Binary Embeddings for Amazon Titan Text Embeddings V2 in Amazon Bedrock Knowledge Bases and Amazon OpenSearch Serverless. With support for binary embedding in Amazon Bedrock and a binary vector store in OpenSearch Serverless, you can use binary embeddings and binary vector store to build Retrieval Augmented …