Antes que nada decir que me parece bien que quien considere que las IAs generadoras de imágenes incurren en algún tipo de delito, denuncien. El problema es que si esas denuncias están basadas en interpretaciones incorrectas, creo que su recorrido va a ser corto.
AI
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Stable Diffusion Lawsuit Technical Explanation Inaccuracies
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Ufff, I was reading the website about the litigation against Stable Diffusion where they lay out the problem of the artists who are suing, and it worries me to see that their explanation of the technical side is incorrect. In this paragraph, the entire interpretation is wrong.
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Karpathy Recommends Lambda API for Easy On-Demand GPU Access
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I like and use @LambdaAPI cloud GPUs, I think the easiest way to spin up an on-demand GPU instance that I'm currently aware of.
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Excitement About Future of Generative AI and Dan Jeffries Essay
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Pumped about the future and what it holds after a catch up with @Dan_Jeffries1 about #GenerativAI this morning! If you haven't read Dan's essay recently, highly recommend taking a look: https://
danieljeffries.substack.com/p/the-age-of-i
ndustrialized-ai
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MIT OpenCourseWare: Free CS, AI and Algorithms Courses
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Free MIT videos & online materials from more than 2,400 courses, including intro classes in computer science, AI and algorithms. Browse our open CS courses here: https://
bit.ly/39jH8DV (v/
@MITOCW
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Cerebras CSL Graph500 BFS Algorithm Research Presentation
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Cerebras is starting the year off with a guest presentation! @simula_research
's Luk Burchard will be sharing research on the Graph500 BFS algorithm that was ported over to the Cerebras Software Language (CSL). Register for this community session here: https://
us02web.zoom.us/webinar/regist
er/WN_s23U5O64T5K9H8h0LhnFJg
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Training a 10M Parameter GPT Model on Shakespeare in 15 Minutes
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We get a ~10M parameter model trained for about 15 minutes on 1 GPU on all of Shakespeare concatenated into one 1MB file. We then sample infinite fake Shakespeare from our baby GPT. Can you spot which one is real? At only 10M params on 1M characters, from-scratch, I hope so 🙂
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Building Transformers: Self-Attention, Training, and GPT-3 Comparison
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The second ~1hr builds up the Transformer: multi-headed self-attention, MLP, residual connections, layernorms. Then we train one and compare it to OpenAI's GPT-3 (spoiler: ours is around ~10K – 1M times smaller but the ~same neural net) and ChatGPT (i.e. ours is pretraining only)
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Transformer Attention Mechanism: Baseline Model and Message Passing Introduction
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First ~1 hour is 1) establishing a baseline (bigram) language model, and 2) introducing the core "attention" mechanism at the heart of the Transformer as a kind of communication / message passing between nodes in a directed graph.
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ChatGPT Money Making Resources Free for 48 Hours
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Making money using ChatGPT is a once-in-a-lifetime opportunity. We organized ALL of the best resources to use ChatGPT into one page. Free for 48 hrs. To get it:
1. Retweet, Follow, Comment 'me'
2. Subscribe to free newsletter in bio (optional) 🙂 & We'll DM it to you, free.