I've been working on implementing this too! It's been fun trying to debug problems by figuring out how to reduce it to the "bare minimum" to repro. Ex: does it work if I disable positional encoding? Does it work if I have only one sample per ray on a single image dataset? Etc
Electrical engineering. Getting a working PCB design for a modern CPU, especially in tight space constraints, is quite challenging.
It's probably something we will do in the v2 (the NUC solution has its drawbacks in terms of exposed IO and such), but I'm glad we don't have to do a full design immediately; only the carrier board that routes the NUC compute element to whereever we need it.
> Enabled by a joint design of the camera’s hardware and computational processing, the system could enable minimally invasive endoscopy with medical robots to diagnose and treat diseases, and improve imaging for other robots with size and weight constraints. Arrays of thousands of such cameras could be used for full-scene sensing, turning surfaces into cameras.
There are existing swallowable capsule endoscopy devices like the PillCam[1], the entirety of which is 11mm x 26mm[2]
I likely would as well if I remembered it. I have always had a problem recalling key combinations across all the applications I use which is why I rely on context menus and tool bars heavily. I've called out VSCode on this a few times as well as it is a memorization fest to work efficiently in there without proper toolbars. It's not just this but I have memory issues in general which have only gotten worse as I've aged. Easy discoverability / access in UI design is a big focus for me.
Yep, but I find it to be a really poor paradigm for efficient work if you don't remember them. Having to search the command palette is much slower than being able to set up a toolbar of often used functions. Don't get me wrong, the command palette is great to have but if you can't memorize all those functions then it's a really poor replacement.
For efficiency Key combo > toolbar > command palette. I just hate to be forced into #3 because #2 doesn't exist. (not to mention that the developers make crazy arguments in the forums about why they refuse to impliment it... but that's all off topic)
Looking for opportunities in scientific computing, geometry processing, graphics, computer vision, and/or machine learning. I have over 3 years of experience working on problems in scientific computing, deep learning, backend engineering, physics simulations, and graphics at startups and research labs.
Stack depth is finite. If consciousness is cognitive recursion, we only can get so far down before the results are garbage. My working theory is that "max cognitive stack depth" is a measure of consciousness.
The scarier concern is that consciousness is a useful intelligent behavior bootstrapping tool, but once we encode everything it has helped generate into cultural DNA or literal DNA it becomes an evolutionary redundant appendix-like organ that will atrophy in the coming generations (See Blindsight by peter watts)
That analogy doesn't work at all, since "the body contains the brain" does not describe a recursive relationship.
By contrast, you see people trying to "explain consciousness" by telling a story that assumes consciousness. When someone makes a statement like "Consciousness emerges from mechanism X in the brain.", every observation that lead to this statement originated in someone's consciousness.
It's less obvious than, but completely analogous to, how it's impossible to decide whether we live in a "base reality" or some sort of simulation - everything you could say about this reality that we perceive is contingent on that very reality.
It's not weird because where you expect it to be is where it is, if it were somewhere else (some distant ansible transmission) you would be used to that and think it weird to imagine it being in the body.
Looking for opportunities in scientific computing, geometry processing, graphics, computer vision, and/or machine learning. I over 3 years of experience working on problems in scientific computing, deep learning, backend engineering, physics simulations, and graphics at startups and research labs.
