Very nice results from a @GoogleAI research effort on a general purpose time-series prediction model that gives good zero-shot performance to new forecasting tasks.
MACHINE LEARNING
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Democratizing AI: Everyone Should Train and Run Their Own Models
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Great first three days in the Bay Area, so much energy here! My current messages to the ecosystem: – Everyone (including software engineers, small startups, academia, indie developers,…) should train, optimize and run their own models. Time for AI builders, not AI users! –
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Evaluating AI Systems: The Overlooked Evaluation Pipeline Problem
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A big issue I see with AI systems is that people aren't spending enough time evaluating their evaluation pipeline. 1. Most teams use more than one metrics (3-7 metrics in general) to evaluate their applications, which is a good practice. However, very few are measuring the
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How Uber Predicts Ride ETA with Deep Learning
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How #Uber predicts rides ETA with #deeplearning V/ @sonu_monika / @debashis_dutta #AI #ArtificialIntelligence #GenerativeAI #ChatGPT #chatbots #chatgpt4 #chatgpt3 #MachineLearning #ML
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Why Does Speech Company’s Music Model Outperform Speech Model?
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Ayudadme a entender cómo una empresa de generación de habla tiene un modelo de generación musical que habla (y canta, y rapea) muy por encima de su modelo de habla. https://t.co/MXfueLVPbR
— Carlos Santana (@DotCSV) 10 mai 2024Ayudadme a entender cómo una empresa de generación de habla tiene un modelo de generación musical que habla (y canta, y rapea) muy por encima de su modelo de habla.
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Monday’s AI News: OpenAI, Neuralink, Elevenlabs Music, Tools and Research
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Comic from today's newsletter: Monday's OpenAI announcement, Neuralink setbacks, Elevenlabs Music, 10 new AI Tools and the latest AI research papers:
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Integrating Model Evaluations with Hugging Face Public Leaderboards
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This is very cool, thanks for sharing openly! Wonder if there’s a way to integrate with http://
hf.co/models to evaluate the million models there or to create a public leaderboard with results of the evals (ex: https://
huggingface.co/collections/cl
efourrier/leaderboards-and-benchmarks-64f99d2e11e92ca5568a7cce
…) cc @IreneSolaiman @clefourrier -
Daily Content on Python, Data Science, Machine Learning and MLOps
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That's a wrap! If you are interested in any of these below topics: – Python – Data Science – Machine Learning – Data Analysis – LLMs – MLOps Find me → @Sumanth_077 I'm sharing daily content over here.
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AutoTrain API Launch: Deploy and Train Models on Hub
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AutoTrain API is here With AutoTrain API, you can deploy your own version of AutoTrain and train models on compatible datasets from Hugging Face Hub on Hugging Face Spaces infra (local training coming soon!). All tasks supported by AutoTrain are supported in the API. Starting
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Implementation at Scale Matters More Than Brilliant Ideas
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Also let's not forget the ability to actually implement your ideas in an efficient, at-scale manner, means you can demonstrate that they work on benchmarks people care about. You'll probably see a lot less interest if you can only prove your brilliant ideas on MNIST. Sometimes