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.

  • Cosmicomical@lemmy.worldEnglish
    1·
    6 months ago

    just have to tell them the prompt, along with a few variables

    Before you can do that, you have to spend hours of computation to figure out a prompt and a set of variables that perfectly match the picture you want to transmit.

    • QuadratureSurfer@lemmy.worldEnglish
      01·
      6 months ago

      Sure, but this is just a more visual example of how compression using an ML model can work.

      The time you spend reworking the prompt, or tweaking the steps/cfg/etc. is outside of the scope of this example.

      And if we’re really talking about creating a good pic it helps to use tools like control net/inpainting/etc… which could still be communicated to the receiving machine, but then you’re starting to lose out on some of the compression by a factor of about 1KB for every additional additional time you need to run the model to get the correct picture.

      • Cosmicomical@lemmy.worldEnglish
        1·
        6 months ago

        You are removing the most computationally intensive part of the process in your example, that’s making it sound easy, while adding it back shows that your process is not practical.

        • QuadratureSurfer@lemmy.worldEnglish
          01·
          6 months ago

          The first thing I said was, “the more you compress something, the more processing power you’re going to need [to decompress it]”

          I’m not removing the most computationally expensive part by any means and you are misunderstanding the process if you think that.

          That’s why I specified:

          The drawback is that you need a powerful computer and a lot of energy to regenerate those images, which brings us back to the problem of making this data conveyed in real-time while using low-power.

          And again

          But of course, that’s still going to take time to decompress as well as a decent spike in power consumption for about 30-60+ seconds (depending on hardware)

          Those 30-60+ second estimates are based on someone using an RTX 4090, the top end Consumer grade GPU of today. They could speed up the process by having multiple GPUs or even enterprise grade equipment, but that’s why I mentioned that this depends on hardware.

          So, yes, this very specific example is not practical for Neuralink (I even said as much in my original example), but this example still works very well for explaining a method that can allow you a compression rate of over 20,000x.

          Yes you need power, energy, and time to generate the original image, and yes you need power, energy, and time to regenerate it on a different computer. But to transmit the information needed to regenerate that image you only need to convey a tiny message.