This is exciting and all, but the most interesting part for me is this: Auto Research, powered by @cursor_ai agent.
The AI agent independently wrote code, ran experiments, analyzed the results, and improved the model's Word Error Rate by up to 19.8%, vastly outperforming traditional hyperparameter search algorithms (Optuna).
And that's because agents weren't just tweaking hyperparameters. They autonomously discovered and coded ML techniques to make the brain-decoder better.
Multiple agents independently invented strategies like "modality dropout" (forcing the AI to rely more on brain signals rather than its own language predictions) and good old beam search decoding.
Vibe-science era, what can i say.
We’re sharing the next major milestone in our non-invasive brain-to-text decoder research: Brain2Qwerty v2.
Building on v1, which was published today in @Nature, Brain2Qwerty v2 is the highest-performing end-to-end pipeline capable of real-time sentence decoding from raw brain signals. It advances beyond character-level performance to decoding words and semantics, enabling accuracy for overall communication.
We believe this research has the potential to make a real difference for the millions of people who suffer from brain lesions or disorders that prevent them from communicating.
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