> As I understand it, a big part of produced clothing just goes straight to waste to begin with.
My niece runs a business that relies on the way we discard clothes. She buys clothes from suppliers in India who source them from the bales of discarded clothes sent to them from Europe. Her suppliers have in effect sorted through the mountain of discards to find the ones that have sufficient value to sell back to us. She specifically buys clothes that have 'vintage' appeal (think tailored jackets rather than hoodies) and sells them primarily to students in a northern English city. Her business has done well enough to move from market stalls to a dedicated high street store and she is just branching out into 'vintage' kids clothes.
I was in Bishop Auckland last summer! But only long enough to have lunch at the delightful Fifteas teashop. If I'm passing that way again I'll make a point of staying longer and have a look around.
Robert has a whole set of youtube channels on electric vehicles and other renewable technology. It’s quite impressive what he does now but I always see Kryten in him, strange.
He's done a lot of things, but for my money the "Scrapheap Challenge" show was his best.
(This is a show where two teams are left inside a scrapheap and given a day, or so, to build a contraption/device.)
He was just so enthusiastic about all the teams, and seemed genuinely interested in both the design, the building, and the performance of whatever it was they were being challenged to build.
But I can't say I've ever heard of that before, or seen any. I used to watch the UK series while having lazy breakfasts every Sunday morning with my then-partner.
In the UK, if a homeowner (customer) pays a company to clear domestic rubbish, and the company illegally fly-tips it, it's the homeowner who gets chased. The law requires them to check that the company is legit.
See 'genetic programming' for techniques that are sort of based on this idea. Typical approach is to have a problem representation (gene analogues) that can be used to create a population of different individual solutions. Test them all against a fitness function and retain those that are 'best' according to some metric. Then create (breed) some new individuals who have some of the characteristics of the winners, perhaps mutated somewhat, insert these into the population. Repeat until you have solved the problem or have a good enough solution.
Challenges (apart from the time taken) are coming up with a good enough gene representation that captures the essence of the problem, building an efficient fitness function, and avoiding local maxima - i.e. a solution that is almost but not quite good enough, but from where you can't breed a better solution.,
Yeah I vaguely remember learning about those in grad school because Minsky deigned to cross the Charles and guest lecture for that one. It always seemed like you had to embed so many expectations in the reinforcement discriminators that you were basically still writing the program, just in an obnoxiously inconvenient way. Though I suppose you could make the argument that this is what diffusers are.
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