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Text-to-CAD: Risks and Opportunities

thegradient.pub · 2,948 words · saved by 1 readers

The dust has hardly formed, much less settled, when it comes to AI-powered text-to-image generation. Yet the result is already clear: a tidal wave of crummy images. There is some quality in the mix, to be sure, but not nearly enough to justify the damage done to the signal-to-noise ratio – for every artist who benefits from a Midjourney-generated album cover, there are fifty people duped by a Midjourney-generated deepfake. And in a world where declining signal-to-noise ratios are the root cause of so many ills (think scientific research, journalism, government accountability), this is not good. It’s now necessary to view all images with suspicion. (This has admittedly long been the case, but the increasing incidence of deepfakes warrants a proportional increase in vigilance, which, apart from being simply unpleasant, is cognitively taxing.) Constant suspicion - or failing that, frequent misdirection - seems a high price to pay for a digital bauble that no one asked for, and offers as y

The dust has hardly formed, much less settled, when it comes to AI-powered text-to-image generation . Yet the result is already clear: a tidal wave of crummy images. There is some quality in the mix, to be sure, but not nearly enough to justify the damage done to the signal-to-noise ratio – for every artist who benefits from a Midjourney-generated album cover, there are fifty people duped by a Midjourney-generated deepfake. And in a world where declining signal-to-noise ratios are the root cause of so many ills (think scientific research, journalism, government accountability), this is not goo

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