| I started (and quit) a PhD based on the Machine Learning algorithms used for BCI systems such as this. Every 6-12 months, someone will write a puff piece about their research in order to get grant money[1]/street cred. And every time, HN responds as if it's sci-fi tech from the future and I shit on everybody's dreams. So here we go: At a glance, nothing about this system in particular is new. It seems to solve the same problems as older invasive arrays from 15 years ago, with the same deal-breaking flaw still unfixed (namely, the body rejecting the implant over time). He had OK-ish performance in a single patient (one character per minute on a good day), but single-patient performance is misleading as it's very common for a system to work with one "golden" patient but not others. Interestingly, no real research has been done (at least, none as of 4 years ago) to look into why this is the case - most "breakthroughs" are people throwing machine-learning spaghetti at the wall to cover up deliberately flawed benchmarking. (To be fair, this is more of a comment on the field as a whole than this particular trial. His results seem honest and humble.) The field is going nowhere, and this article is yet another shining example of this. Nothing will ever be achieved until we abandon the current paradigm and actually learn more about the underlying neurophysiology (or possibly create batshit-insane sensors). [1] They actually explicitly mention this in the article this time, which is pretty bold: >Chaudhary’s foundation is seeking funding to give similar implants to several more people with ALS. He estimates the system would cost close to $500,000 over the first 2 years. |