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G real risk: AI smarter than us, is not a threat. AI will always need a 'human in … ytc_UgzisIKCJ… G There are to many labourer jobs that cannot be done by AI. Not just 5… ytc_UgwRgpsSr… G The whole premise of this video is dumb. This guy asked ChatGPT to roleplay as… ytc_UgxFi_qhn… G I know im not supposed to be be siding on the opposite team but its feels wrong … ytc_UgzV11Dfj… G As long as you have a driver in the truck that has a cdl license, then it is not… ytc_UgwOXp99w… G Where's the qualia? This is a little sensationalistic. Doing an algorithmic sear… ytc_Ugx2zef8B… G I’m really glad that you told Alice’s story, with all AI bs flooding what seems … ytc_Ugxyl0syt… G IDEA; regulation must be put in place that restricts any Ai system from having c… ytc_Ugyt2I-ZZ…
Comment
2:09 - I will admit that I don't exactly trust Stable Diffusion models for uniqueness, but as a person studying in AI, I will tell you now; most models cannot reproduce an artwork 1:1. That is a statistical improbability by design. AIs generalize knowledge, so unless its prone to overfitting and a lack of randomness exists, then it cannot ever produce 1:1 the same artwork that already exists. Go back to AI first principles for this: Precision and Recall. A high recall and low precision (for classification tasks) generally represents overfitting. What we want for the generative models is a high precision to what the user requests and a low recall so as to generalize the results. And fundamentally there is the problem: If a person prompts the AI very specifically for the features in their image - then the result is heavily biased to generate from a constrained world view. This is fundamentally a user problem: not an AI dataset problem.
youtube Viral AI Reaction 2023-01-01T05:1…
Coding Result
DimensionValue
Responsibilitynone
Reasoningmixed
Policyindustry_self
Emotionapproval
Coded at2026-04-27T06:24:53.388235
Raw LLM Response
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