Transforming the future of music creation https://
bit.ly/3SYIDBZ
#AI #MachineLearning #DeepLearning #LLMs #DataScience
AI
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AI Transforms Music Creation with Machine Learning and LLMs
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Amazon Trains Next-Gen Titan LLM with NVIDIA NeMo
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Explore how @amazon leveraged the NVIDIA NeMo framework, GPUs, and EFA from @awscloud to train its next-generation LLM, giving some of the largest Amazon Titan foundation models customers a faster, more accessible solution for #generativeAI. #AWSreinvent
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SDXL Turbo: Single-Step Real-Time Text-to-Image Generation
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Introducing SDXL Turbo: A real-time text-to-image generation model.
— Stability AI (@StabilityAI) 28 novembre 2023
SDXL Turbo achieves state-of-the-art performance with a new distillation technology, enabling single-step image generation with unprecedented quality, reducing the required step count from 50 to just one.
The… pic.twitter.com/0NA4aUqKkDIntroducing SDXL Turbo: A real-time text-to-image generation model. SDXL Turbo achieves state-of-the-art performance with a new distillation technology, enabling single-step image generation with unprecedented quality, reducing the required step count from 50 to just one. The
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Self-Supervised Learning Transforms Deep Learning Beyond Supervised Methods
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Sorry but your picture of deep learning is badly out of date.
The limitation you are pointing out is a limitation of *supervised* learning (regardless of the architecture). But deep Self-Supervised Learning has taken over the world in the last few years: you pre-train with SSL -
Fine-tuning and derivatives of core AI models
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What the situation with derivatives of core models, like fine tunes for example?
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Guest lecture on intuitions for understanding large language models
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It was an honor to give a guest lecture yesterday at Stanford’s CS330 class, "Deep Multi-Task and Meta-Learning"! I discussed a few very simple intuitions for how I personally think about large language models. Slides: https://
docs.google.com/presentation/d
/1hQUd3pF8_2Gr2Obc89LKjmHL0DlH-uof9M0yFVd3FA4/edit?usp=sharing
… Here are the six intuitions: (1) -
GPU Memory Limits for Large Model Training on A100
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No I’ve gone higher, 2k would OOM error on an 80gb A100
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Financial institutions address verification practices scrutiny
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(Just commenting on “The actual reason we do this is because we want to pretend that financial institutions are uniformly Extremely Serious about checking that someone actually lives at the address they typed into account settings in their mobile app.”)
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Untether AI Partners UCI Express for Chiplet AI Acceleration
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@UntetherAI has joined the @UCIexpress to propel chiplet technology & energy-centric AI acceleration. UAI will enable the development of compact + high-performance compute systems across domains ranging from high-performance computing to edge applications. https://
tinyurl.com/4c8tzf96 -
Best LLM Explanation: How Large Language Models Work
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I've watched the whole thing twice now (myself – No AI). It is probably the best explanation I've seen so far on how LLMs work.