💡Brighten garages, workshops & storage areas with our Foldable Leaf Bulb Light. Adjustable panels help deliver lighting exactly where you need it. 📞 +86 18933439494 #zhongshanpuchuanglighting# #foldablelight# #leaflight# #ledlighting# #warehouselighting# #garagelight# #energysaving#

The retina is one of the most energetically expensive tissues known to science. And yet, birds’ retinas manage without a key energy-saving tool: oxygen. New research explores how.

I recently listen to the book "Life 3.0" written by Max Tegmark. And I had an intresting question pop up in my head, "does our brain follow any computational laws, or a biological built brain?" The only thing I found on this was: That the brain acts as a probabalistic, energy-saving prediction machin. When the brain meets the real world, it main law is to conserve energy, and it has done this to also make our brains highly usable as well. Becuase the probabalistical machine is also an advantage, it makes our brain incredible powerful. As we know it: "And the brain actively generate a top-down mental representations, however uses external input to rework brains own representation." So the brain does not just generate pre mental representations that the brain has stored based on past expereinces. But it also will rework those mental models, based on the external input it recives, so it also has an error-correction mechanism built with it. However the error correction requires energy, but if the pre-built representation is correct, then their is little to none energy wasted. But if this is the case, would that mean that “every time” we learn something new, and expand on it, retreive it. Does it decrease energy cost of having that representation retreived by the brain? Another computational law, that biological systems seems to follow is “FEP - Free Energy Principle", its the law to perserve and waste as little energy as possible. And the biological structure seems to have this principle built into it. To make a short summary of “computational laws a biological system (our brain)” follows: - Probabalistic machine by generating mental representations. - Errors correction if the generate mental shcemas are incorrect based on th external input. - FEP (Free Energy Principle). I just want to say, I am not an expert, just someone trying to learn intresting information, explore different thoughts. And if I am incorrect about something correct me down below. If you want to take the discussion further, or add something more, do it down below or write to me via message here on X.

Please correct me if I’m wrong, but here are my thoughts about AI satellite from SPCX: 1. Training/inference: They are kilometers away from each other. Due to physics limitations, the latency and bandwidth between satellites will make them better suited for inference only, not training. 2. Energy saving: Energy cost for data centers is usually less than 10%. The majority of data center cost today is still depreciation of AI chips. Therefore, even if you consider energy cost as zero, I still have trouble seeing significant cost efficiency when comparing on-ground data centers with in-space ones. 3. Production capacity: If AI chip manufacturing speed is the scaling bottleneck, then launching AI satellites does not unblock scaling, since the satellites still need to wait for the chips. Otherwise, the plan to have 10,000 Starship launches per year becomes the bottleneck, and at this speed, it still does not significantly outpace the expected compute scaling speed on the ground within five years.