It’s not 10,000 hours, it’s 10,000 iterations.
GENERATIVE AI
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AI Performance Limitations on Unconstrained Web Search Tasks
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It works well when it’s force constrained to sites like reddit twitter etc. it just can’t be trusted to find good sites
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Stanford Microsoft Method Enables Natural Language Model Bug Fixes
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Talking to Models: Stanford U & Microsoft Method Enables Developers to Correct Model Bugs via Natural Language Patches https://
syncedreview.com/2022/11/21/tal
king-to-models-stanford-u-microsoft-method-enables-developers-to-correct-model-bugs-via-natural-language-patches/
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Train and Deploy Custom DreamBooth Models on Replicate
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Train and deploy DreamBooth models on Replicate. With just a handful of images and a single API call, you can train your own custom Stable Diffusion, publish it to Replicate, and run predictions on it in the cloud.
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PyTorch Optimum Achieves 4.5x Speedup for Transformer Models
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A collaboration with @PyTorch to make transformer-based models faster using optimum library! Up to 4.5x speedup for text, vision and audio models using a one liner! Try it out now: https://
huggingface.co/docs/optimum/b
ettertransformer/overview
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NVIDIA Riva ASR TTS Free Trial for Conversational AI
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Prototype, test, and fine-tune your conversation-based AI solutions with NVIDIA Riva’s automatic speech recognition and text-to-speech skills in this free trial. #ASR #TTS #NVIDIARiva https://
nvda.ws/3OnlTHz -
Whisper Paper Reading: OpenAI’s Speech Recognition Model
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Don't forget to read the paper ahead of the reading: https://
openai.com/blog/whisper/ -
Transformers Finally Added: Critical AI Architecture Recognition
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Thank you for adding Transformers! Many miss it which is wrong in 2022!
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Comparison of GPT-3 and GPT-4 Parameter Counts
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Currently, GPT-3 has 175 billion parameters, which is 10x faster than any of its closest competitors. GPT-4 is rumored be about 100 trillion parameters.
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Magic3D: High-Resolution Text-to-3D Content Generation
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Magic3D: High-Resolution Text-to-3D Content Creation Lin et al.: https://
arxiv.org/abs/2211.10440 #ArtificialIntelligence #DeepLearning #MachineLearning