Sadly enough, the international community is really good at infighting and wasting money, and really bad at coming up with a followup for the ISS :( So China, for all its flaws, will have the biggest functioning space station in LEO at some point. ISS operations have just been extended to 2034, but if we get a single micrometeorite hit in the wrong spot (e.g. Node 2), that’ll put an end to it.
Too many people think that when we spend money on space, that we are literally sending the money into orbit. Almost all of it is going back into the economy and keeping tons of businesses alive.
I don’t really understand how the entire ISS could be “end of life cycle.” Aren’t there a bunch of different modules of different ages? And anyway, the oldest modules are 24 years old that is nothing with proper maintenance, there are 50 year old trains still in operation daily.
If a train fails, at worst that will happen is it will stop. When a space station fails, the worst that will happen is everyone inside dies.
In addition, a space station is far more expensive, and it may be simply too expensive to still maintain old technology. Ideally, at some point, one will replace it with a newer, more modern, space station. Which will both be cheaper, and allow more, novel, science to be done. Although I don’t know if there is any plan for that.
I’d like to see a space station with a rotating ring, that generates artificial gravity through centrifugal acceleration.
Space races are good for science. As long as spacex engineers keep Elon at bay, Starship should be able to launch stage biggest station modules in history.
In a related anecdote, one thing that blew my mind was the realization that any “program” or “app” fundamentally packages existing functionality that you can already do without that specific app. Most of the time, it still makes sense to use and pay for the abstraction rather than reinventing the wheel constantly but it is a sobering thought for sure.
Your mistake is thinking the picture in the thumbnail was the starting point, when that was actually the end point generated by the algorithm created by the guy who won this award. The AI built these words off of a “crackle pattern” someone else identified from CT scans of the scroll
Farritor then trained a machine-learning model on Casey’s crackle pattern. He identified multiple ink strokes and more letters and used them as training data. His model started identifying letters and hints of words that weren’t visible to him. After he submitted his findings to the program, a panel of papyrologists noted 13 letters and identified that the hidden word is “Porphyras” which means “purple” and is a bit of a rarity in ancient texts.
You understand fully how this 21 year old was able to identify words written on the inside of the charred remains of a 2000 year old unopenable papyrus, impressing a team of professional papyrologists?
I seriously doubt these could be mass-produced in any meaningful way due to the rarity of the requirements. I’d love to hear a more practical argument for this though.
“2D” fab isn’t new, and correct me if I’m wrong, that is sort of how AMD got its start. It’s just the idea of fixing heat dissipation to solve for Moore’s Law, but requires novel materials that didn’t exist yet. This has cropped up in various forms for metal and silicon dynamic replacements over the decades, and I think the last big news I heard about this was 10 years ago regarding graphene being a cheap and plentiful replacement for silicon, and here we are with no proofs of concept.
It’s a paper I guess, but not anything that has the feasibility of showing up in the real world. If anything, I think these labs are working on shrinking quantum computational units down to be more useful for everyday computing, since they kind of already “work”.
Edit: also some recent news about transistor heat dissipation.
Interesting, in this particular case it’s implementing a single operation, but I can imagine they can implement other single operation dedicated chips as well. So I’d expect ASICs but no CPUs
By setting the conductivity of each transistor, we can perform analog vector-matrix multiplication in a single step by applying voltages to our processor and measuring the output
Still, i don’t think it’ll need to get much more complex to be very useful for AI workloads.
People have been discovering that more, and simpler, calculations seem to work better? the trend in AI workloads seems to have gone from FP32 -> FP16 -> INT16 -> INT8 and possibly even INT4?
Seems like just having lots of simple calculations is more efficient/effective than more complex stuff.
Well these chips perform analog math, which means high precision high speed. It’s not as accurate as fp32 as in repeatedly and deterministic outputs, but that’s def not a problem for a deep and wide neural network such as used by llm
interestingengineering.com
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