I have to disagree here. While it's been proven that a lot of things can be done by just searching a very big solution space very fast (either by brute compute or by a sophisticated algo or both), hardware solution spaces are not that easily searched.
For a lot of applications where signal integrity is not critical, high frequency switching is not required and power density is moderate, yeah. Anyone can do that, even an AI. But you don't even need to make a pcb for those cases, just throw the components in a protoboard and fire away.
When you start adding variables to the search space (emi interference, matching routing impedances, heat management, mechanical integrity, etc) the search space grows so big that any non-expert will become lost in it.
Will future cheap compute power be able to overcome this? I'm not betting on it.
Also did you not see the quoted post at the end, they are already training ai on running material science labs so we are quickly moving away from the non-expert.
We can probably find people saying the search space for Go is too large and that we'll never be able to do protein folding and if we can we'll never manage protein interactions and then well never manage to simulate a whole cell (tbc). I just think at some point these machines become smarter than us, if it's 2 years or 20 years the self improvement part will happen on both the hardware and software side. Judgement day is inevitable.
It struck me as anachronistic that the 1915 worlds fair would demonstrate the steam locomotive, a technology that had been around for decades.
Reality turns out to be more interesting, it was an exhibit of Southern Pacific's first steam locomotive, the CP Huntington, built in 1863.
So while it's true to say that the 1915 worlds' fair exhibited Southern Pacific's first steam locomotive, it's also very misleading to suggest it was new technology of the time.
~85 years prior to the 1915 fair, Stephenson's Planet 2-2-0 steam loco was operating commercially between Manchester and Liverpool (5 built in 1830). The planet was a version 2 of his earlier Rocket already operating between the cities since 1829/30 - utilizing a very design with the boiler between the wheels layout that almost all steam locomotives eventually adopted.
There are so many processes involved in making a high-precision bolt that goes into an F1 racing car. Well, hardware is always limited by laws of physics, it's hard to accelerate. Maybe in the future, AI will do this too, through robotics. I am still waiting for the robotics ChatGPT moment.
I've been a bit surprised this hasn't been happening already. I don't know if the eventual trigger is going to be ICE murders or what, but with as accessible as the tech is, as much unrest as currently exists, and no shortage of people willing to inflict lethal violence via other mechanisms, I would've thought we'd have had at least a little drone violence by now.
Guns don't scale like drones. No matter how many guns you own, you can only shoot two at a time, and only one if you care about aim.
Building an autonomous drone swarm capable of nuclear-weapon-tier kill count is far easier than building a nuclear weapon. We already know how dangerous military drones are, and those are still mostly bottlenecked by relying on human-powered target identification. Building drones to indiscriminately kill unarmored civilians is an easier task.
But the tooling required for guns and rifling in particular is pretty hard to come by, whereas the tooling required to assemble a DIY drone is basically indistinguishable from R/C hobby equipment. You can (try) to regulate where/who/how/when can fly a drone, but regulating drone building seems almost impossible to me.
For a lot of applications where signal integrity is not critical, high frequency switching is not required and power density is moderate, yeah. Anyone can do that, even an AI. But you don't even need to make a pcb for those cases, just throw the components in a protoboard and fire away.
When you start adding variables to the search space (emi interference, matching routing impedances, heat management, mechanical integrity, etc) the search space grows so big that any non-expert will become lost in it.
Will future cheap compute power be able to overcome this? I'm not betting on it.
Famous last words. https://en.wikipedia.org/wiki/Bitter_lesson
Also did you not see the quoted post at the end, they are already training ai on running material science labs so we are quickly moving away from the non-expert.
Reality turns out to be more interesting, it was an exhibit of Southern Pacific's first steam locomotive, the CP Huntington, built in 1863.
So while it's true to say that the 1915 worlds' fair exhibited Southern Pacific's first steam locomotive, it's also very misleading to suggest it was new technology of the time.
There are so many processes involved in making a high-precision bolt that goes into an F1 racing car. Well, hardware is always limited by laws of physics, it's hard to accelerate. Maybe in the future, AI will do this too, through robotics. I am still waiting for the robotics ChatGPT moment.
Life of Bolt(2016): https://www.youtube.com/watch?v=iptAkpqjtMQ
Dan Gelbart recently appeared on a podcast where he shared valuable insights into precision engineering. It was really great!
Title: Solving Impossible Problems for Fun and Profit - Dan Gelbart
https://www.youtube.com/watch?v=UTgrWmOk4q8
Drones should be handled the same way as weapons! Strict licenses and mandatory insurance!
https://www.lesswrong.com/posts/bGoo3NWzMAzsLQceJ/drone-wmds...
You know it's easy to get illegal guns right?
Building an autonomous drone swarm capable of nuclear-weapon-tier kill count is far easier than building a nuclear weapon. We already know how dangerous military drones are, and those are still mostly bottlenecked by relying on human-powered target identification. Building drones to indiscriminately kill unarmored civilians is an easier task.