Facebook drops funding for interface that reads the brain
technologyreview.com
technologyreview.com
I dropped out of a PhD back in 2018 all about the application of Machine Learning to Neurosignal Decoding, and that quote just perfectly sums up everything wrong with the state-of-the-art approach.
A bizarre prompt, an infeasibly boring task, and they somehow magically expect a machine learning to smooth everything over despite the fact that the subject isn't even generating a consistent signal to start with.
It's a hard problem, even neutral implants and mics isn't enough. I had two months to come up with some ideas about how we could get a human to produce reliable signals, and failed completely. That was when I walked away and got a job.
There are at least a gazillion things in the history of mankind that we have not been able to reduce to a material reality.
Abstraction makes it much easier to reason about complex systems, such as the world :)
To actually capture words you have much better chances reading the brain while writing the word with a pen because you are actually sending a signal from the brain to your hand, which is what they did here (even if he doesn't actually have a hand to move, the brain still can emit the very same commands): https://www.cnet.com/google-amp/news/brain-implants-let-para...
The study you quoted uses similar principles.
>I don't think it is anything like simulating a mechanical action like throwing a ball, just saying a word alters brains in completely different ways depending on the person
It's exactly like imagining throwing a ball. A disproportionately large amount of the motor cortex is used for facial muscle and tongue control. Look up the "cortical homunculus".
>for example saying "spider" reminds me of spiderman and there is little I can to stop such through from happening
These BCIs can't tell whether or not you're thinking about Spider-Man, otherwise they'd be used on terrorists to get information. Instead, they're mainly focused on broad fitting synchronisation of M1 neurons (indicating rest)
>To actually capture words you have much better chances reading the brain while writing the word with a pen because you are actually sending a signal from the brain to your hand
Real movement does produce much more consistent results than imagined movements, but it doesn't translate well to the target market for BCIs (people with severe motor disabilities)
>even if he doesn't actually have a hand to move, the brain still can emit the very same commands
It doesn't work like that in real life. With no feedback, we're back to square one with the "imagined movement".
“While we still believe in the long-term potential of head-mounted optical [brain-computer interface] technologies, we’ve decided to focus our immediate efforts on a different neural interface approach that has a nearer-term path to market,” the company said.
Could they start again in the future? Sure, but in practical terms removing funding and stopping people from working on a project indefinitely is the same as cancelling. This is just corporate-speak, which requires translation - if removing funding, staff, research space and equipment and stopping all work indefinitely isn’t cancelling something, what extra steps are required for cancelling something?
The different neural approach is reading signals from muscles in your arms, so it’s definitely not the brain-speech interface they were talking about, but the fact they say it’s closer to market will placate investors.
In having on-again, off-again RSI issues over the years from coding, I think this tech would be hugely beneficial and is more realistic to ship in the near future.
i suppose we have language models for code now, but i suspect it would still be frustrating- at least in the short term.
but, we shall see...
Also, check your desk and chair ergonomics. I find having my elbows on the desk reduces wrist strain.
I don't know if it was the book, a placebo effect, or something else, but I got better after reading that.
Current BCI tech is nowhere near the fidelity of non-BCI control methods (e.g. eye tracking). If you're serious about it, look into them.
I think it's definitely a useful imaging technique for certain (highly specific) tasks. But to detect language? They would need many, many more source/detector pairs, considering the vast swathes of the brain partially responsible. Language is already very fuzzy within fMRI.
Also, i don't really follow the tech, but i thought i remembered seeing (on HN maybe?) positive reports of actually useful BCI prototypes. Maybe those were overblown or limited, though.
There are useful prototypes out there ("useful" as in someone with motor neurone disease moving a computer cursor on a text to speech machine), but all of them use invasive, penetrative electrodes that fail after a matter of months as the body rejects them.
The less invasive prototypes have seen no real breakthroughs in the last decade and a half. Only a small percentage of people can use them with any real accuracy, nobody knows why and frankly nobody's doing the research on it.
All the grant money goes to people throwing random ML models at standard datasets and patting themselves on the back when it gets a 2% more accurate fit on one (and only one) set.
Couple this with the fact that technology relying on other biosignals is yielding amazing progress (mainly the translation of voluntary muscle/tendon movement into control signals for a computer, like what Stephen Hawking had but more recently prosthetic hands with dinner and finger control), I suspect that most of the research into BCIs will be put on ice until we have a more complete understanding of how the brain works or can no longer make progress with other control technologies.
Nothing wrong with being lofty, but with the money and manpower that FB has put behind BCI, versus the talent flight from Neurallink – FB will likely be the winner.
A shame, because I don't think anyone else can even come close to really stacking up.
I don't know where this will land, but we could argue that Facebook is being unrealistic and true consumer BCI does require an implant.
Good luck convincing millions of people getting a brain implant.
Who left?
https://www.nejm.org/doi/full/10.1056/NEJMoa2027540?query=fe...
Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria
METHODS
We implanted a subdural, high-density, multielectrode array over the area of the sensorimotor cortex that controls speech in a person with anarthria (the loss of the ability to articulate speech) and spastic quadriparesis caused by a brain-stem stroke.
Over the course of 48 sessions, we recorded 22 hours of cortical activity while the participant attempted to say individual words from a vocabulary set of 50 words.
We used deep-learning algorithms to create computational models for the detection and classification of words from patterns in the recorded cortical activity. We applied these computational models, as well as a natural-language model that yielded next-word probabilities given the preceding words in a sequence, to decode full sentences as the participant attempted to say them.
RESULTS We decoded sentences from the participant’s cortical activity in real time at a median rate of 15.2 words per minute, with a median word error rate of 25.6%.
In post hoc analyses, we detected 98% of the attempts by the participant to produce individual words, and we classified words with 47.1% accuracy using cortical signals that were stable throughout the 81-week study period.
CONCLUSIONS In a person with anarthria and spastic quadriparesis caused by a brain-stem stroke, words and sentences were decoded directly from cortical activity during attempted speech with the use of deep-learning models and a natural-language model.
(Funded by Facebook and others; ClinicalTrials.gov number, NCT03698149. opens in new tab.)
Anyone found this repo ? I would love to see some of their pipeline’s parameters. They ‘should’ have a fine-tuned processing pipeline that’s interesting for different kind of bio-signal sensors.
[0] https://www.privateinternetaccess.com/blog/spotify-granted-p...
Interestingly, I interviewed at one of the many startups who wants to implant thousands of sensors directly into the brain. The only issue is they had the same problem that MIT Lincoln Labs has: they're snobs about hiring only PhD EEs, creating an ideological monoculture.
Everyone having the same background opens you up to blind spots, that are arguably worse IMO when you're trying to put things in human brains.
Also, PhD EEs != competence.
totally agree that heterogeneous teams are vastly superior in avoiding blind spots.
Ph.D.s are trained in two things. "Reasoning about an unknown" is one of them. But the particular niche knowledge they acquired while training this is the other. That niche can save my life if the niche matches the thing to be implanted into my brain. No matter how many youtube-educated wannabe-experts take issue with formal education.
(I have many issues with the academic system, but this kind of critisism is ridiculous.)
It might just be me, but I actually feel more comfortable with the former.
If you ONLY hire people from specific backgrounds, you get blind spots. That's dangerous. Excluding anyone without a PhD should not be expected to improve safety.
The distinction you make does not exist in that sense. The only actual difference is that the "extra" 3 Ph.D. graduates have had a certification for a somewhat structured education in some research field, while the youtoube fans don't. So, everything else equal, that group has an advantage.