Elon Musk’s quest to wirelessly connect human brains with machines has run into a seemingly impossible obstacle, experts say. The company is now asking the public for help finding a solution.
Musk’s startup Neuralink, which is in the early stages of testing in human subjects, is pitched as a brain implant that will let people control computers and other devices using their thoughts. Some of Musk’s predictions for the technology include letting paralyzed people “walk again and use their arms normally.”
Turning brain signals into computer inputs means transmitting a lot of data very quickly. A problem for Neuralink is that the implant generates about 200 times more brain data per second than it can currently wirelessly transmit. Now, the company is seeking a new algorithm that can transmit this data in a smaller package — a process called compression — through a public challenge.
As a barebones web page announcing the Neuralink Compression Challenge posted on Thursday explains, “[greater than] 200x compression is needed.” The winning solution must also run in real time, and at low power.
I’m not an Information Theory guy, but I am aware that, regardless of how clever one might hope to be, there is a theoretical limit on how compressed any given set of information could possibly be; and this is particularly true for the lossless compression demanded by this challenge.
Quote from the article:
The skepticism is well-founded, said Karl Martin, chief technology officer of data science company Integrate.ai. Martin’s PhD thesis at the University of Toronto focused on data compression and security.
Neuralink’s brainwave signals are compressible at ratios of around 2 to 1 and up to 7 to 1, he said in an email. But 200 to 1 “is far beyond what we expect to be the fundamental limit of possibility.”
I’m no expert in this subject either, but a theoretical limit could be beyond 200x - depending on the data.
For example, a basic compression approach is to use a lookup table that allows you to map large values to smaller lookup ids. So, if the possible data only contains 2 values: One consisting of 10,000 letter 'a’s. The other is 10,000 letter 'b’s. We can map the first to number 1 and the second to number 2. With this lookup in place, a compressed value of “12211” would uncompress to 50,000 characters. A 10,000x compression ratio. Extrapolate that example out and there is no theoretical maximum to the compression ratio.
But that’s when the data set is known and small. As the complexity grows, it does seem logical that a maximum limit would be introduced.
So, it might be possible to achieve 200x compression, but only if the complexity of the data set is below some threshold I’m not smart enough to calculate.