@Cohere.com's non-profit research lab and open science initiative that seeks to solve complex machine learning problems. Join us in exploring the unknown, together. https://cohere.com/research
Cohere Labs
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Ananya Sahu & Mehrnaz Mofakhami, research scholars at Cohere Labs, will explore:
🌏 How cultural awareness is currently tested in AI and what challenges remain
📜 What we’re learning from Tiny Aya and its support for underrepresented languages
💬 Your diverse perspectives and what you want to see next
AI is getting better at math. Better at code. But is it getting better at understanding cultural nuances? 🤔
Join us for “Cultural Awareness in AI — From Knowledge Tests to Social Norms and Beyond”, a conversation on what it means to build AI systems that work at global scale.
Does AI truly understand different cultures and languages?
We’re surveying cultural awareness in real-world AI use.
✨ When cultural awareness matters in real-world AI use
💡 Whether AI reflects diverse norms, communication styles & knowledge
🫥Where AI falls short in cultural understanding
🌱Very proud of our team's latest release 😊 meet Tiny Aya, a massively multilingual model with 3.35B parameters.
Tech report here: github.com/Cohere-Labs/...
Whether you’re a researcher, builder, or just curious about AI’s cultural limitations, join this conversation!
Learn more: cohere.com/events/coher...
Ensure your cultural perspective is represented. cohere.link/FyKPWbQ
It’s S4E4 of Papers In The Park. Javad Rajabi is here to walk us through his paper on SEGA.
arxiv.org/abs/2605.22668
Thanks to @cohereforai.bsky.social for sponsoring, to Anthony and Alvin for organizing, and Javad for walking us.
Cohere Labs
Cohere Labs
Cohere Labs
Julia Kreutzer
github.com
Cohere Labs
1) what? Cohere is here?!!!!
2) this is crazy
Woo hoo, who would have thought Canada would produce efficient massively multicultural models
Cohere Labs
S2E2 of Papers In The Park!
This week: DFlash: Block Diffusion for Flash Speculative Decoding
arxiv.org/abs/2602.06036
Introducing ✨Tiny Aya✨, a family of massively multilingual small language models built to run where people actually are.
Tiny Aya delivers strong multilingual performance in 70+ global languages in a 3.35B parameter model, efficient enough to run locally, even on a phone.
