iiinova — where your digital self keeps evolving

Reposted by Linyang He

Pinyuan Feng (Tony) @tonyfeng.bsky.social · 15/06/2026

Excited to announce #CCN2026 satellite event: Modeling and Understanding Human Brain Computation at Scale Organized with Hossein Adeli, Fan Cheng, Andrew Luo, Nikolaus Kriegeskorte Link for details and registration: tinyurl.com/3jf2urze @cogcompneuro.bsky.social

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Columbia University's Zuckerman Institute @zuckermanbrain.bsky.social · 11/05/2026

Hearing aids amplify all incoming sound, and so struggle with noisy surroundings. Brain-controlled hearing tech from Nima Mesgarani, Vishal Choudhari & team could lead to a new generation of hearing systems that help people single out a voice in a crowd. youtube.com/shorts/TONWQ...

youtube.com

Can Mind-Reading Tech Help People Hear Better?

YouTube video by Columbia University's Zuckerman Institute

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Siyuan Song @siyuansong.bsky.social · 24/04/2026

🚀 Announcing the Chinese BabyLM Challenge: the first shared task on data-efficient pretraining for Chinese. 📍 Co-located with NLPCC 2026 (Nov 3–5, Macau🇨🇳🇲🇴) Can you train a strong Chinese LM on just ~100M words? chinese-babylm.github.io 🧵 👇(1/6)

chinese-babylm.github.io

Chinese BabyLM Challenge

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Linyang He @linyanghe.bsky.social · 30/10/2025

🧠 New at #NeurIPS2025! 🎵 We're far from the shallow now🎵 TL;DR: We introduce the first "reasoning embedding" and uncover its unique spatio-temporal pattern in the brain. 🔗 arxiv.org/abs/2510.228...

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Jaap Jumelet @jumelet.bsky.social · 15/10/2025

🌍Introducing BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data! LLMs learn from vastly more data than humans ever experience. BabyLM challenges this paradigm by focusing on developmentally plausible data We extend this effort to 45 new languages!

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RJ Antonello @rjantonello.bsky.social · 18/08/2025

In our new paper, we explore how we can build encoding models that are both powerful and understandable. Our model uses an LLM to answer 35 questions about a sentence's content. The answers linearly contribute to our prediction of how the brain will respond to that sentence. 1/6

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