How does 250 K compare to current[1] superconductors?
[1] pun definitely intended.
[1] pun definitely intended.
Best chart I could find. Looks like there's another at 250 K from 2019. But what I don't have here is the Temperature / Pressure / Timeline, though...? What is the Pressure in the article? Is it STP?
https://en.wikipedia.org/wiki/Superconductivity#/media/File:...
Today though I’m constantly surprised by the number of young people who recognize things from 80s movies and especially music. I’d say that number is higher compared to our generation.
Millenials and Gen Z grew up in an era with much easier access to older media than previous generations. First was the video store - while Gen X had this too, it really took off in the 90s. I remember when I was a kid in the late 90s and early 2000s, it was $5 to rent a new release or 3 for $5 for old releases. This meant that we were basically encouraged by our parents to watch older stuff, and of course the fact that they lived through the 80s themselves meant they tended to recommend movies to us from that era.
Of course, after the video store came VOD services like Netflix. Old movies are a great way to pad out a VOD catalogue, so that increased the access to 80s/90s movies even more.
It also doesn't hurt that, as you've pointed out, many of these films still hold up pretty well today.
In the 50s/60s there were less (but still some of per personal faves) and the dominant genres (Westerns particularly) have been out of fashion for at least 40 years now.
Is that because neither of them have any electronic devices? They both have payphones and cars that don't look like today?
He also was very confused by the “Ronald Reagan? The ACTOR??” joke but immediately thought that BTTF2 Mogul-Biff was supposed to be Trump.
- it was a joke (not typically acceptable on HN)
- it is a dated reference
I decided it was worth it…
As you can see, it's not actually that fun to ride. It hovers in every direction, like standing on an ice cube. The reason ice skates and roller blades work is they have low friction only along one dimension, so you can still apply force to the ground along the normal vector.
Thinking of the 11 rubes who paid $10k each for their own uncontrollable hoverboards that only work over conductive surfaces.
https://www.kickstarter.com/projects/142464853/hendo-hoverbo...
Ie. currently it can't be formulated as a search problem entirely on a computer.
You might hit on some interesting interactions between known properties that haven't been investigated but I would assume the real interesting results are from things we just don't know to model, or how to model.
Most fields are still left with piles and piles of potential solutions to sort through. They often select candidates that are the cheapest and most practical to approach or they have high suspicion of success and pursue those. At the end of the day though we don’t have full universe simulators at every scale we’d want, we have very specific area simulators within very specific bounds. You have to go out an empirically test these things.
But this is and has already been going on for decades across most disciplines I’ve interacted with, they just weren’t using DNN or LLMs at the time but domains are adopting these as well to leverage where feasible in the search process.
I work with a variety of people interested in leveraging simulation and everyone wants to take the successes they see in LLMs or say RL from AlphaStar or AlphaGo and apply them in their domain. It’s alluring, I get it, the issue is that we often lack enough real understanding in domains and the science isn’t as airtight and people think it is, its too general or narrow, or on some cases we have good suspicion of how to build better more accurate simulations but there’s not enough compute power or energy in the world to make them currently practical, so we need to take some tradeoffs and live with less accurate and detailed simulation which leads to inaccurate representations of reality and ultimately inaccurate solution suggestion candidates.
We did what you described using idle cycles at Google (search for "Exacycle") and we got great results doing large scale parameter explorations (either randomly sampled, or sampled based on where the previous sims suggested looking next)- although, nobody actually did material simulations like this, we did proteins.
Realistically, almost nobody does this because it's just not cost effective (the search space is too large, the loss functions aren't accurate enough, and it uses TONS of energy), and more importantly, somebody else is just going to find a way to generate 75% of the results with 25% of the energy, and that person will get published faster.
Infrared photons are thermal energy. Thermophotovoltaics and thermoelectrics convert work due to the thermal gradient into electricity.
Laser cooling: https://en.wikipedia.org/wiki/Laser_cooling
Cooling with LEDs in reverse: https://issuu.com/designinglighting/docs/dec_2022/s/17923182 :
"Near-field photonic cooling through control of the chemical potential of photons" (2019) https://www.nature.com/articles/s41586-019-0918-8 https://scholar.google.com/scholar?cites=8589611114160282602... :
> This demonstration of active nanophotonic cooling—without the use of coherent laser radiation—lays the experimental foundation for systematic exploration of nanoscale photonics and optoelectronics for solid-state refrigeration and on-chip device cooling.