🚨IT'S HAPPENING🚨 a woman who cannot speak now speaks through her brain, in real time, with her own voice. no typing, delay, or sounds made. just neural intent to streaming speech this isn’t prediction. it’s embodiment 1/

a woman with complete paralysis and zero vocal function just regained the ability to speak at ~conversational speed. with AI, a brain implant learned to read her intention to speak and synthesized her words in her own voice. live. streaming. no delay 2/

not typing or text. actual speech. before this, the best systems let patients “type” at 8–14 words per minute. this one does 90+ WPM. in audio, with prosody. from brain activity alone. and it uses no audible training data. she doesn’t even need to try making sounds. 3/

the system: - a 253-channel ECoG array on her speech motor cortex - a deep neural decoder trained on 23,000 silent-speech attempts - RNN-T architecture decoding in 80ms chunks - dual output: synthesized speech + real-time transcript - voice personalized to her own, pre-injury audio 4/

the decoder streams speech every 80ms most systems wait for full sentences before outputting anything. this one doesn’t. it emits speech as the brain thinks. lag: ~1 second. the system literally streams her neural intention into speech in near real time. 5/

the speech is fast, fluent, and accurate on testing: 50-phrase set (caregiver needs): 91 WPM 12% word error rate (WER) 11% character error rate large 1,024-word set (natural sentences): 47 WPM 59% WER (harder) 45% character error rate it’s not perfect. but it works. 6/

it even decoded words it never saw the model successfully synthesized novel words not seen during training. when given 24 new silent words like “Zulu” or “Romeo,” it correctly identified them 46% of the time vs 3.8% by chance. from neural activity alone 7/

@IterIntellectus This isn’t true mind reading though but mind training. It has been demonstrated many times that this is the case where patients learn to think to the system, and the above shows this to be so, especially with the especially high error rates in the large word set.

@IterIntellectus More than 1 out of 2 words decoded is wrong?