A recent Reddit post showcased a series of artistic QR codes created with Stable Diffusion. Those QR codes were generated with a custom-trained ControlNet

cory doctorow

a brief academic paper that reviews the situations where automation ends up producing more work for humans, rather than less – posits that this is sort of the norm, because automation almost always adds a layer (or two) of complexity and abstraction on to a problem space. While not directly written about AI (this is from 1983) I feel it is a solid foundation from which to build discussion (in the negative) on AI in modern system.

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in which 94-year-old noam chomsky verbally promenades uninterrupted on artificial intelligence and other things

94-years-old (how does he hold on to all this stuff still?)

maybe it’s zettelkasten

ChatGPT and other AI applications such as Midjourney have pushed “Artificial Intelligence” high on the hype cycle. In this article, I want to focus specifically on the energy cost of training and using applications like ChatGPT, what their widespread adoption could mean for global CO₂ emissions, and what we could do to limit these emissions.

Key points

• Training of large AI models is not the problem
• Large-scale use of large AI models would be unsustainable
• Renewables are not making AI more sustainable

The enormous energy requirement of these brute force statistical models is due to the following attributes:

• Requires millions or billions of training examples
• Requires many training cycles
• Requires retraining when presented with new information
• Requires many weights and lots of multiplication

ChatGPT and other AI applications such as Midjourney have pushed “Artificial Intelligence” high on the hype cycle. In this article, I want to focus specifically on the energy cost of training and using applications like ChatGPT, what their widespread adoption could mean for global CO₂ emissions, and what we could do to limit these emissions.

Key points

• Training of large AI models is not the problem
• Large-scale use of large AI models would be unsustainable
• Renewables are not making AI more sustainable

Appen is among dozens of companies that offer data-labeling services for the AI industry. If you’ve bought groceries on Instacart or looked up an employer on Glassdoor, you’ve benefited from such labeling behind the scenes. Most profit-maximizing algorithms, which underpin e-commerce sites, voice assistants, and self-driving cars, are based on deep learning, an AI technique that relies on scores of labeled examples to expand its capabilities.