They're arguing that it's not a decomposition of the original expressions, but patterns extracted from the non-copyrightable data that's there. Thus, the model is not derivative and outputs too. The copying is a triviality they hope is waived by Fair Use or equivalent exception.
GENERATIVE AI
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Paw Patrol Characters Generated in Stranger Things Style
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“Paw Patrol in Stranger Things” #StableDiffusion2 #AIart
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Model Performance Limited to Benchmarks, Real Version Unavailable
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The disclaimer is that its on benchmarks only and nobody has access to the real model yet…
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Non-expressive Use: Legal Workaround for Copyright Avoidance
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The legal buzzword to use is "non-expressive use" here! It's a workaround for Copyright. Here's how it works: Since Copyright only applies to expressions and not ideas/data, you use semantic & "ontological games with legal implications" to avoid Copyright.
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Training AI Models with Extended 30k Context Windows
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Worth mentioning that we can also train these models with very large context windows — up to 30k instead of 1k like the original.
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Fast Personalization Encoder for Text-to-Image Models
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Designing an Encoder for Fast Personalization of Text-to-Image Models Gal et al.: https://
arxiv.org/abs/2302.12228 #ArtificialIntelligence #DeepLearning #MachineLearning -
Training from Scratch Pricing Table for Language Models
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Here's a screenshot of our training-from-scratch pricing table that is on the webpage posted in the previous thread. We can also train to non-Chinchilla – do you have more information about the dataset?
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Large Language Models Generate Cumulative Ambiguities Not Certainties
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Humans have to generate a lot of words to try and explain the potential & challenges of large language #AI models https://
wsj.com/articles/chatg
pt-heralds-an-intellectual-revolution-enlightenment-artificial-intelligence-homo-technicus-technology-cognition-morality-philosophy-774331c6?mod=opinion_lead_pos5
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"Enlightenment science accumulated certainties; the new AI generates cumulative ambiguities" -
Production Performance vs Academic Benchmarks Eval Correlation
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What production performance are you referring to then? Or are you referring to academic benchmarks being bad in general? Because you would always need some eval benchmark ideally correlated to the prod use case.
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Validating Tech Solutions Through Lived Experience and Expertise
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"The surest cure for BS is exposing it to the scrutiny of people who truly, deeply understand a problem through years of lived experience." +100! Plus "the problem" isn't crypto or #ai but what those techs are trying to solve in that use case. Thx @EthanZ for a def recommend read