While you guys kind of have a point, the specific argument you put forward is rather weak. Transportation accounts for an almost negligible part of the overall emissions of a product. Bulk freight cargo is super efficient. If you want to moan about transportation emissions, look at single people sitting in tons of steel making short trips.

The point you still have is that emissions are caused in the process of satisfying a demand. Consumers do have a partial responsibility. However I would object in that the problem cannot be solved from the consumer’s position. It is a market failure. Markets have no incentive to internalize their externalities, that has to come from a different place; e.g. politics. Carbon pricing is an interesting mechanic, since it utilizes that same argument for good.

That’s true. A lot more could be said about this, on various levels in various directions. Ultimately I don’t think this systemic crisis can be solved on a consumer level. The attempt leads to the status quo; different subcultures with some people paying extra to calm their consciousness, while most don’t care or cannot afford. I’m afraid if we try to work with individual sacrifice against economic incentives, the latter will win.

It’s also true that some companies use their economic power as a political lever, to influence legislation in their favor. Or as a societal lever, to sway public opinion in their favor. I guess this meme here tries to address that. I honor the motive. Just the chosen vehicle is broken. With mountains of evidence supporting the cause, however, there are plenty of other, perfectly fine vehicles available.

This meme is so wrong it is deliberate misinformation. The Guardian made an article which is probably this meme’s source. It even linked to the original source, the Carbon Majors Report. But blatantly misquoted the CMR. For example, CMR says something like “100 fossil fuel producers responsible for 71% of industrial GHG emissions”, but The Guardian (and meme posters) omit the italic bits.

What do they mean with producers? Not companies like Apple or Heinz, but simply organizations which produce fossil fuels. Duh. Shell, BP, but also entities like China’s coal sector (which they count as one producer, although it consists of many entities). CMR also states 3rd type emissions are included. Which means emissions caused by “using” their “products”, e.g. you burning gasoline in your car.

So yes, the downvoted guy saying “Consumer emissions and corporate emissions are the same emissions” is pretty spot on in this case, albeit most likely by accident. Rejected not for being wrong, but for not fitting into a narrative, which I call the wrong reasons. Please check your sources before posting. We live in a post-factual world where only narratives count and truth is just another feeling, because of “journalism” and reposts like this. Which is the infuriating part in this particular case. I guess you want to spread awareness about the climate crisis, which is good, but you cannot do so by propagandizing science and spreading lies.

All that from the top of my head. Both the ominous TG article and the fairly short report are easy to find. In just a couple of minutes you can check and confirm how criminally misquoted it was.

Eventually, things settle at almost perfect ratios. Everything between creates some kind of friction.

What does it even mean to bruteforce creating art? Trying all the possible prompts to some image model?

Doesn’t have to be that random, but can be. Here, I wrote: “throw loads of computation power, gazillions of try & error, petabytes of data including human opinions”.

The approach people take to learning or applying a skill like painting is not bruteforcing, there is actual structure and method to it.

Ok, but isn’t that rather an argument that it can eventually be mastered by a machine? They excel at applying structure and method, with far more accuracy (or the precise amount of desired randomness) and speed than we can.

The idea of brute forcing art comes down to philosophical questions. Do we have some immaterial genie in us, which cannot be seen and described by science, which cannot be recreated by engineers? Engeniers, lol. Is art something which depends on who created it, or does it depend on who views it?

Either way what I meant is that it is thinkable that more computation power and better algorithms bring machines closer to being art creators, although some humans surely will reject that solely based on them being machines. Time will tell.

That depends on things we don’t know yet. If it can be brute forced (throw loads of computation power, gazillions of try & error, petabytes of data including human opinions), then yes, “lots of work” can be an equivalent.

If it does not, we have a mystery to solve. Where does this magic come from? It cannot be broken down into data and algorithms, but still emerges in the material world? How? And what is it, if not dependent on knowledge stored in matter?

On the other hand, how do humans come up with good, meaningful art? Talent Practice. Isn’t that just another equivalent of “lots of work”? This magic depends on many learned data points and acquired algorithms, executed by human brains.

There also is survivor bias. Millions of people practice art, but only a tiny fraction is recognized as artists (if you ask the magazines and wallets). Would we apply the same measure to computer generated art, or would we expect them to shine in every instance?

As “good, meaningful art” still lacks a good, meaningful definition, I can see humans moving the goalpost as technology progresses, so that it always remains a human domain. We just like to feel special and have a hard time accepting humiliations like being pushed out of the center of the solar system, or placed on one random planet among billion others, or being just one of many animal species.

Or maybe we are unique in this case. We’ll probably be wiser in a few decades.

This ambiguity is what I had in mind when I read “let me be clear”. Though now I get it.

You can use more debug outputs (log(…)) to narrow it down. Challenge your assumptions! If necessary, check line by line if all the variables still behave as expected. Or use a debugger if available/familiar.

This takes a few minutes tops and guarantees you to find at which line the actual behaviour diverts from your expectations. Then, you can make a more precise search. But usually the solution is obvious once you have found the precise cause.

Sustainability loses it’s meaning in space, or needs a whole different meaning. There are no ecosystems on other planets or moons which we could tip over with unsustainable practices.

Just throwing this out without addressing the glaring differences looks like uninformed romanticism.

Claims it is a literature movement. Fails to give a single example of a book that can be defined as solarpunk or that self-defines as such.

Really!? Wow, that is poor. Thanks for saving my time!

Edit: And for the update.

Romantically speaking, aren’t we all just genitals with attached life support?

I didn’t know about that [under this name], so thanks for bringing it up. But no, I meant something slightly different.

Colors of noise describes how to generate different distributions. What I meant was how to transform distributions.

Many of the examples in the article start with a random number distribution, and then transform it to reduce discrepancy.

This reminded me of audio/video signal processing. For example, one can take a picture and transform it to reduce discrepancy (so that neither very bright parts nor very dark parts overshoot). Or you can take an audio sample and transform it to reduce discrepancy in loudness.

So the idea was that maybe techniques of either field (RNG, audio, video) could be applied to both other fields.

That’s a very long list of different techniques with examples, very cool!

Though I wonder, is there some connection to image processing, high dynamic range?

Or audio compression, the kind which brings out the kick in the mix, not the kind which saves disk space?

The similarity I see between all three fields is, they try to bring down extreme values, outliers, to level the field, while still retaining characteristics.

Thank you, great post!

I recognized the constellation. Though it’s good to have a refresher, and I learned something new.

Consider these two pixel-oval portals:

  xx         oo
x    x     o    o
x    x     o    o
x    x     o    o
x    x     o    o
  xx         oo

They are the same size, and you can easily make a bijective mapping for each of their pixels.

Rotate one two times in 3D space by 90°, and it fits through the other. If you want more wiggle room, make them taller.

I think that’s one of the best use cases for AI in programming; exploring other approaches.

It’s very time-consuming to play out how your codebase would look like if you had decided differently at the beginning of the project. So actually comparing different implementations is very expensive. This incentivizes people to stick to what they know works well. Maybe even more so when they have more experience, which means they really know this works very well, and they know what can go wrong otherwise.

Being able to generate code instantly helps a lot in this regard, although it still has to be checked for errors.

There’s a very naive, but working approach: Ask it how :D

Or pretend it’s a colleague, and discuss the next steps with it.

You can go further and ask it to write a specific snippet for a defined context. But as others already said, the results aren’t always satisfactory. Having a conversation about the topic, on the other hand, is pretty harmless.

Those LLMs are great fools, but I am just paranoid to use it in that manner.

Exquisite typo. I also agree to everything else you said.

You can do that when you control the frontend UI. Then, you can set up the input field for their name, applying input validation.

But I would rather not rely on telling the user, in hopes they understand and comply. If they have ways to do it wrong, they will.

If you’ve ever tried to hold in a sneeze, this new medical case report might make you think twice.

“In world’s 1st known case”

Contrary to what other people seem to take from this, it rather shows how unrisky holding in a sneeze is. If it was significantly dangerous, we’d know more than one case.

I actually had a relative who died from drinking a sip of water from a glass. Got it in the wrong pipe. I still would not warn people from drinking water after I learned about it. Again, after all, the overwhelming majority of evidence points to the opposite; it’s perfectly safe.

It’s not a great comparison, since there is a good alternative to holding in a sneeze, whereas there is no good alternative to drink water.