Eshika Saxena

Meta FAIR (@AIatMeta) | prev CS @harvard

[Eshika Saxena @eshikasax]

proud to have worked on muse spark open with the best team!! check it out: https://t.co/S26Ko6KsDH

[Alexandr Wang @alexandr_wang]

1/ muse spark open is the most capable US open-weight model to date.

816B total params, 42B active. 1M-token context. natively multimodal. built for coding and agentic work like long-horizon tasks, hard software engineering, multi-step reasoning.

america! 🇺🇲

[Eshika Saxena @eshikasax]

🚀🚀🚀

[Mark Zuckerberg @finkd]

Muse Spark Open -- weights releasing today -- runs great on new Mac Studios thanks to work with @johnternus and Apple.

[Eshika Saxena @eshikasax]

Check out a bundle of exciting releases from FAIR today, including our latest work on SALSA, which enables researchers to benchmark AI-based attacks in support of validating security for post-quantum cryptography 🎉
Blog: https://t.co/ObPrTGWfg1
Paper: https://t.co/Xwn7MDBhmQ

[AI at Meta @AIatMeta]

Open science is how we continue to push technology forward and today at Meta FAIR we’re sharing eight new AI research artifacts including new models, datasets and code to inspire innovation in the community. More in the video from @jpineau1.

This work is another important step towards our goal of achieving Advanced Machine Intelligence (AMI).

What we’re releasing:
• Meta Spirit LM: An open source language model for seamless speech and text integration.
• Meta Segment Anything Model 2.1: An updated checkpoint with improved results on visually similar objects, small objects and occlusion handling. Plus a new developer suite to make it easier for developers to build with SAM 2.
• Layer Skip: Inference code and fine-tuned checkpoints demonstrating a new method for enhancing LLM performance.
• SALSA: New code to enable researchers to benchmark AI-based attacks in support of validating security for post-quantum cryptography.
• Meta Lingua: A lightweight and self-contained codebase designed to train language models at scale.
• Meta Open Materials: New open source models and the largest dataset of its kind to accelerate AI-driven discovery of new inorganic materials.
• MEXMA: A new research paper and code for our novel pre-trained cross-lingual sentence encoder with coverage across 80 languages.
• Self-Taught Evaluator: a new method for generating synthetic preference data to train reward models without relying on human annotations.

Access to state-of-the-art AI creates opportunities for everyone. We’re excited to share this work and look forward to seeing the community innovation that results from it.

Details and access to everything released by FAIR today ➡️ https://t.co/P3XkdN2WQN

[𝙷𝚒𝚖𝚊 𝙻𝚊𝚔𝚔𝚊𝚛𝚊𝚓𝚞 @hima_lakkaraju]

[Conference Paper] OpenXAI: Towards a Transparent Evaluation of Model Explanations (joint work w/ @_cagarwal, @SatyaXploringAI, @eshikasax, @MartinPawelczyk, @narijohnson, #Isha, @marinkazitnik) -- https://t.co/aIrKfiRPXX. More details in this thread https://t.co/I9ScNmdaLH [4/N]

[Eshika Saxena @eshikasax]

@alishasax either way football wins 🤷🏽‍♀️

[𝙷𝚒𝚖𝚊 𝙻𝚊𝚔𝚔𝚊𝚛𝚊𝚓𝚞 @hima_lakkaraju]

Excited to share that our OpenXAI framework designed to benchmark explanation methods has been accepted at #NeurIPS2022. @_cagarwal @SatyaXploringAI #Eshika @MartinPawelczyk @narijohnson #Isha @marinkazitnik

[𝙷𝚒𝚖𝚊 𝙻𝚊𝚔𝚔𝚊𝚛𝚊𝚓𝚞 @hima_lakkaraju]

Very excited to introduce, OpenXAI - https://t.co/gSPMNgdlqq, an open-source framework we have been building for the past year to evaluate/benchmark the faithfulness, stability, fairness of post hoc explanation methods using easy-to-use API & just a few lines of code. [1/N] #XAI