Looks like we're listening to different Elons. The one is a tech guy, the other is politician, so to speak. Long time ago I decided for myself that I never trust words solely on their origin. However, I take into account what the person is known for and their profile of competence. I think no one would argue that Elon is competent in technology. Yes, Elon time is a thing, but again, what he does is not just another college project, you know. Space is hard, so does AI and the human brain. There is fair amount of uncertainty in the domain itself which makes all predictions estimates at most.

Sure, you can ask your grandma for investment advice and that would probably be a poor decision, unless she worked in finance her whole life. On the other hand, she would probably be more than competent to answer how to make cookies and pies.

Long story short: use your brain, don't trust media, do your own research. Even Nobel winners are often known for some crazy or unscientific stuff. People are people, after all. You can't be an expert in everything. Otherwise we'd cancel literally everyone.

Still, I'd rather patiently wait for him to serialize his thoughts, than to listen to some super fluent person saying utter nonsense, especially if it's a pitch talk. It's all about _what_ is being said, not _how_.

https://en.wikipedia.org/wiki/Con_Kolivas

P.S.: This project is obligated to have an anime mascot girl and a logo where she is embarrassed while trying to keep her skirt in place because a house fan suddenly turned on xD

Even bigger companies do rebranding sometimes.

OTOH, smaller model did it perfectly.

These things are illegal for a reason.

It would be a really interesting option as a Framework mainboard. Unfortunately, Framework 13" has rather tight constraints on mainboard geometry.

Relevant links:

https://turingpi.com/product/turing-rk1/?attribute_ram=16+GB

https://lore.kernel.org/lkml/20240612-6-10-rocket-v1-0-060e4...

https://www.phoronix.com/news/Rocket-Rockchip-NPU-Driver

Still, as a neuroscience researcher I am very interested, if possible, in hearing a little bit more details. You mentioned a color shift. Since you compared that with a broken VGA, I assume the colors were attached to actual objects but were incorrect, right? As if someone had played with color filters in Photoshop.

Did you experience color that was "detached" from an object, like a poorly calibrated printing press that shifts one of the colors a bit?

What about the fills? Was it like a random color noise texture or a solid fill but with incorrect color?

I'm not very familiar with their theory and technology, so please take my opinion with a grain of salt.

Both projects are inspired by the real brain. The differences are in perspective.

Here's a quote from their description:

"Every cortical column learns models of complete objects. They achieve this by combining input with a grid cell-derived location, and then integrating over movements (see Hawkins et al., 2017; Lewis et al., 2018 for details). This suggests a modified interpretation of the cortical hierarchy, where complete models of objects are learned at every hierarchical level, and every region contains multiple models of objects".

If I understood correctly, they state that each minicolumn remembers how an object is represented in this particular location meaning that each minicolumn has its own memory.

"For example, there is no single model of a coffee cup that includes what a cup feels like and looks like. Instead there are 100s of models of a cup. Each model is based on a unique subset of sensory input within different sensory modalities."

This is very different from our approach. In our case, minicolumns are independent context processors that work by mapping input stimuli to produce an interpretation and then using their local memory to estimate the validity of such interpretation. The key difference is that the whole cortex area has a single model of the object that is recognized in many contexts. The idea of a context as a set of interpretation rules is essential to our theory.

So, to put it simply, instead of remembering the cup in every possible scenario we remember only one concept of a cup, and then use the context space to find the right interpretation of the input. This allows us to train the models on a limited input and, more importantly, transfer the knowledge between different contexts to think by analogy.

P.S.: You may also be interested in the discussion that takes place on Reddit: https://www.reddit.com/r/artificial/comments/amogl2/brain_in... Hopefully, this helps you to feel the difference.