We're excited to help organize the XAI4V workshop at #CVPR2023 — submission deadline is just two weeks away! Want to present your paper or demo? More information in the below.
@aiatmeta
-
Meta AI Researchers Compare Language Models Brain Differences Predictions
By
–
New in Nature Human Behavior, Meta AI researchers show how current language models differ from the human brain & highlight the role of long-range & hierarchical predictions. We hope these findings will help inform the next generation of AI https://
go.nature.com/3SKb3gX -
IWSLT Shared Task on Simultaneous Translation Now Open
By
–
Join us for a shared task on simultaneous translation at #IWSLT this year! Tracks for both speech-to-text & speech-to-speech. Entries evaluated on quality & latency using the latest SimulEval toolkit. What you need to know to participate https://
bit.ly/3ZwNPgh -
CACTI: Scalable Multi-Task Multi-Scene Imitation Learning Framework
By
–
Introducing CACTI — a framework for scalable multi-task multi-scene imitation learning.
— AI at Meta (@AIatMeta) 28 février 2023
Our agents show generalization to unseen distractors & are extremely sample efficient, capable of 10 unique real-world tasks with just 1k demonstrations. Paper ➡️ https://t.co/5zB4eMQvWV pic.twitter.com/nqSVcUP1yrIntroducing CACTI — a framework for scalable multi-task multi-scene imitation learning. Our agents show generalization to unseen distractors & are extremely sample efficient, capable of 10 unique real-world tasks with just 1k demonstrations. Paper https://
bit.ly/3SD5roy -
Meta Releases LLaMA Foundational Language Model Publicly
By
–
Today we're publicly releasing LLaMA, a state-of-the-art foundational LLM, as part of our ongoing commitment to open science, transparency and democratized access to new research. Learn more & request access https://
bit.ly/3SoXdQE -
LLaMA: Competitive AI Model That’s Smaller and More Efficient
By
–
LLaMA (Large Language Model Meta AI) achieves results competitive with the best currently released models while being smaller & more efficient — increasing accessibility to this technology for more researchers working on this important subfield of AI across the globe.
-
Meta AI Newsletter: Open Source Work, Datasets, Community
By
–
A new issue of the Meta AI newsletter is heading to your inbox soon! This edition includes a look at some of our recent open source work, datasets and opportunities to get connected with the AI community! Subscribe
-
Meta AI Generates 240K Diverse Natural Language Instructions
By
–
In ‘Unnatural Instructions’, Meta AI researchers propose a new method to generate natural language instructions, allowing us to scale up to 240K diverse instructions & train models that rival performance of contemporary instruction-tuned models. Paper https://
bit.ly/41ky8uy -
AI Model Interprets Show More Less Signals for Better Feed Content
By
–
We trained an AI model to effectively interpret intent behind “Show More” & “Show Less” signals to surface more of the content people want to see in their Feeds — even if they rarely interact with the signals. More on how we built it https://
bit.ly/3Edj08k -
GenAug: Meta’s AI System for Zero-Shot Robot Behavior Transfer
By
–
Introducing GenAug, a new system by Meta AI researchers that uses text2image models to enable robots to transfer behaviors zero-shot from a simple demonstrated scene to unseen scenes of varying complexity — at no extra robot/human cost.
— AI at Meta (@AIatMeta) 16 février 2023
More details ➡️ https://t.co/GOEWtyg5TS pic.twitter.com/Ar8hwdTUOQIntroducing GenAug, a new system by Meta AI researchers that uses text2image models to enable robots to transfer behaviors zero-shot from a simple demonstrated scene to unseen scenes of varying complexity — at no extra robot/human cost. More details https://
bit.ly/3Kc1RiS