The ugly truth behind ChatGPT: AI is guzzling resources at planet-eating rates

https://lemmy.ca/post/22239178

The ugly truth behind ChatGPT: AI is guzzling resources at planet-eating rates - Lemmy.ca

> Despite its name, the infrastructure used by the “cloud” accounts for more global greenhouse emissions than commercial flights. In 2018, for instance, the 5bn YouTube hits for the viral song Despacito used the same amount of energy it would take to heat 40,000 US homes annually. > Large language models such as ChatGPT are some of the most energy-guzzling technologies of all. Research suggests, for instance, that about 700,000 litres of water could have been used to cool the machines that trained ChatGPT-3 at Microsoft’s data facilities. > Additionally, as these companies aim to reduce their reliance on fossil fuels, they may opt to base their datacentres in regions with cheaper electricity, such as the southern US, potentially exacerbating water consumption issues in drier parts of the world. > Furthermore, while minerals such as lithium and cobalt are most commonly associated with batteries in the motor sector, they are also crucial for the batteries used in datacentres. The extraction process often involves significant water usage and can lead to pollution, undermining water security. The extraction of these minerals are also often linked to human rights violations and poor labour standards. Trying to achieve one climate goal of limiting our dependence on fossil fuels can compromise another goal, of ensuring everyone has a safe and accessible water supply. > Moreover, when significant energy resources are allocated to tech-related endeavours, it can lead to energy shortages for essential needs such as residential power supply. Recent data from the UK shows that the country’s outdated electricity network is holding back affordable housing projects. > In other words, policy needs to be designed not to pick sectors or technologies as “winners”, but to pick the willing by providing support that is conditional on companies moving in the right direction. Making disclosure of environmental practices and impacts a condition for government support could ensure greater transparency and accountability.

Yes it does, and wait until you hear about literally every other industry.
This is the same excuse crypto bros make. Though that makes sense because the venn diagram between AI evangelists that blow up like the Hindenburg the moment you levy any critique against AI and its usage is basically a circle with crypto bros who assure us that any day now it will stop being treated like penny stocks and actually be useful “because they just like the tech.”
Cryptocurrencies have no real world applications. AI does.
Such as?

As a professional editor (video/audio) AI has drastically altered my work in amazing, productive ways.

I’m still a critic of course. For some industries it’s clearly a solution in search of a problem so they can hype investment. A tool being useful doesn’t mean I’m unable to critique it!

What about this?
ToonCrafter: Generative Cartoon Interpolation - Divisions by zero

# Abstract >We introduce ToonCrafter, a novel approach that transcends traditional correspondence-based cartoon video interpolation, paving the way for generative interpolation. Traditional methods, that implicitly assume linear motion and the absence of complicated phenomena like dis-occlusion, often struggle with the exaggerated non-linear and large motions with occlusion commonly found in cartoons, resulting in implausible or even failed interpolation results. To overcome these limitations, we explore the potential of adapting live-action video priors to better suit cartoon interpolation within a generative framework. ToonCrafter effectively addresses the challenges faced when applying live-action video motion priors to generative cartoon interpolation. First, we design a toon rectification learning strategy that seamlessly adapts live-action video priors to the cartoon domain, resolving the domain gap and content leakage issues. Next, we introduce a dual-reference-based 3D decoder to compensate for lost details due to the highly compressed latent prior spaces, ensuring the preservation of fine details in interpolation results. Finally, we design a flexible sketch encoder that empowers users with interactive control over the interpolation results. Experimental results demonstrate that our proposed method not only produces visually convincing and more natural dynamics, but also effectively handles dis-occlusion. The comparative evaluation demonstrates the notable superiority of our approach over existing competitors. Paper: https://arxiv.org/abs/2405.17933v1 [https://arxiv.org/abs/2405.17933v1] Code: https://github.com/ToonCrafter/ToonCrafter [https://github.com/ToonCrafter/ToonCrafter] Project Page: https://doubiiu.github.io/projects/ToonCrafter/ [https://doubiiu.github.io/projects/ToonCrafter/] ## Limitations Input starting frame [https://doubiiu.github.io/projects/ToonCrafter/05limitation/74845_304_frame1.png] Input ending frame [https://doubiiu.github.io/projects/ToonCrafter/05limitation/74845_304_frame3.png] Our failure case Your browser does not support playing HTML5 video. You can download a copy of the video file [https://doubiiu.github.io/projects/ToonCrafter/05limitation/74845_304_WOurs.mp4] instead. Input starting frame [https://doubiiu.github.io/projects/ToonCrafter/05limitation/74244_465.mp4_00-00.png] Input ending frame [https://doubiiu.github.io/projects/ToonCrafter/05limitation/74244_465.mp4_00-01.png] Our failure case Your browser does not support playing HTML5 video. You can download a copy of the video file [https://doubiiu.github.io/projects/ToonCrafter/05limitation/74244_465.mp4] instead.

I’ve used it to improve selected paragraphs of my writing, provide code snippets and find an old comic based on a crude description of a friend.

I feel like these interactions were valuable to me and only one (code snippets) could have been easily replaced with existing tools.