Also, I believe there is an entire world of proto results leading to better outcomes that we haven't even started to explore, like when they are saying they can prototype at smaller resolutions and then upscale to a higher one. I can see how we could apply this same concept to
MACHINE LEARNING
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Magic raises $117M for frontier AI code models toward AGI
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Magic announced a $117M raise led by former Github CEO Nat Friedman. The company is building frontier-scale AI code models, seeking to build an ‘AI software engineer’ on the path towards AGI.
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Meta’s V-JEPA Model Predicts Video Content with Limited Information
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Meta released V-JEPA, a new learning model for AI to understand and predict what is going on in a video, even with limited information. It learns by predicting missing or obscured parts of a video in its internal feature space.
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Game Engines for Data Generation, Diffusion Models for Inference
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several people have pointed out that it's a lot more plausible that the data is generated using a game engine, but inference is likely just a diffusion model. that probably makes more sense.
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AI-Driven Animation and Deep Learning in Cinematic Arts
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Join Kathy Smith, Artist and Professor at @USC's School of Cinematic Arts, as she explores AI-driven animation and deep learning, bridging the gap between art and science > https://t.co/QMy4xkY2rF #GTC24 pic.twitter.com/iNwfd9mZS7
— NVIDIA AI (@NVIDIAAI) 16 février 2024Join Kathy Smith, Artist and Professor at @USC
's School of Cinematic Arts, as she explores AI-driven animation and deep learning, bridging the gap between art and science > https://
nvda.ws/3uxnfKd #GTC24 -
Global and Local Variables in Python: Programming Fundamentals
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Global and Local Variables in Python: Introduction Variables are an essential part of any programming language, including Python. They allow us to store… https://
analyticsvidhya.com/blog/2024/01/g
lobal-and-local-variables-in-python/?utm_source=dlvr.it&utm_medium=twitter
… #DataAnalytics #DataScience #DataDriven #CTO #ML #BigData #MachineLearning #ArtificialIntelligence -
Rethinking Machine Unlearning for Large Language Models
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Rethinking Machine Unlearning for Large Language Models Liu et al.: https://
arxiv.org/abs/2402.08787 #ArtificialIntelligence #DeepLearning #LargeLanguageModels -
VQVAE Stack Architecture Differs Fundamentally from Transformers
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also a “VQVAE stack” is not a transformer! it would not look like this haha
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Fine-tuning GPT2 with additional layers avoids pretraining scratch
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he’s pointing out we can just add the two layers I drew here and train those starting from pretrained GPT2 or something; don’t have to pretrain from scratch
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Discrete Tokens Recover AI Model Performance Effectively
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if you use enough discrete tokens you could def recover performance eventually, unless you’re doin somethin wrong