Elon Musk's Neuralink Brain Interface Demo

This is a great talk / demo of Neuralink's implantable brain-machine interface.  Elon Musk gives the initial overview talk (which is a great overview of neural computer interfaces in general), and then Neuralink engineers answer questions from the room and the web. 

The Neuralink interface currently is the size of a small coin, and a robot surgeon that Neuralink also manufactures inserts it into your scull.  The robot also inserts the individual electrodes into the brain while monitoring that process to avoid puncturing any blood vessels while doing that.  So for being such an invasive device, it's surprisingly non-invasive. 

The current generation of devices is shown working in a cute pig.  Another cute pig who had the device in it's scull and then had it removed is also shown.  Pigs seem happy and healthy.  Neuralink is on fast track with the FDA to get this approved for use in humans.  Otherwise pigs will be the ones talking to our AI overlords via direct neural interconnect.

When Elon talks about the scalability of the device and associated wiring electrodes being scalable like moore's law integrated circuit technology, pay attention.  When he talks about all of the different problems this interface can address, be a little bit more critical. 

Monitoring sensory input or muscle movements in the cortex, curing retinal blindness or deficient hair cells in the auditory system, absolutely.  Understanding what people are thinking in rapid train of thought and piping that understanding directly into the computer, restoring all of your memories, not quite so fast.

Everything in the first part we know maps to non-linear maps represented in specific areas of the cortex.  The second category of wish list items, we don't know how they work yet.  If they end up just being more multi-dimensionally contorted localilzed cortex maps, then sure.  Otherwise, we need to figure out how they actually work before we can interface to them.  But devices like Neuralink might help us do that figuring out part.

I also think the whole concept of how the company is setup is fascinating, and very reminiscent of high tech companies during the golden age of USA manufacturing.  They are doing everything in house.   The entire technology stack.  All the way down to making the custom materials necessary to build it, making wires, welding glass, etc.  An amazing confluence of different areas of high tech all brought together, and everyone there very much devoted to this get-it-done DIY philosophy.  And probably necessary to pull off what they are trying to do.

Comments

Post a Comment

Popular posts from this blog

CycleGAN: a GAN architecture for learning unpaired image to image transformations

Image
 Like the Doublemint Twins touting the joys of Doublemint gum, 2 GANs are surely better than 1 GAN.  Especially if we package them together inside of one meta GAN module.  And this is exactly what the CycleGAN architecture does. Have you ever harbored dark secrets of turning a horse into a zebra?  The CycleGAN was developed to do just that. Learn how to turn a horse into a zebra.  And more. Now right away you can notice a difference between the image to image transformation GAN architectures we've been discussing over the last few posts.  Those last few posts described systems that learn from a database of matched input-output image pairs. And if your goal is to turn an edge representation into a nicely filled in continuous tone image, it's easy to build your database of matched input-output image pairs that your GAN system can then learn off of.  Take a continuous tone image (which will be the output of the database pair entry), then run it through an edge detector algorithm. 
Read more

Pix2Pix: a GAN architecture for image to image transformation

Image
 I thought following up yesterday's TraVelGAN post with a Pix2Pix GAN post would be useful to compare what is going on in the 2 architectures.  Two different approaches to the same problem. I stole this Pix2Pix Overview slide below from an excellent deeplearning.ai GAN course (note that they borrowed it from the original paper) because it gives you a good feel for what is going on inside of the Pix2Pix architecture.    Note how the Generator part is very much like an auto-encoder architecture, but rebuilt using the U-Net architecture features (based on skip-connections) that fastai has been discussing in their courses for several years before it became more widely known to the deep learning community at large  (and which originally came from an obscure medical image segmentation paper) . So the Generator in this Pix2Pix GAN is really pretty sophisticated, consisting of a whole image to image auto-encoder network with U-Net skip connections to generate better image quality at highe
Read more

Smart Fabrics

Image
"What makes smart fabrics revolutionary is that they have the ability to do many things that traditional fabrics cannot, including communicate, transform, conduct energy and even grow."  This is a quote from Rebeccah Pailes-Friedman, who is a professor at Pratt with a research focus on developing wearable technology and smart textiles. Enhancing textiles can be viewed through different lens.  Enhancements can be aesthetic, or performance boosting, or durability boosting. 
Read more