I spent my afternoon coding with #ChatGPT at work to develop AI. In short: – Great for getting standard functions (plots, cross-validation, etc.)
– Useless for adapting to the latest library versions and often gives nonsense
– Single file only 🙁
@dfintelligence
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Experimenting with ChatGPT for coding
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ChatGPT refuses to acknowledge the mistake
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I've been struggling with #ChatGPT for 15 minutes trying to explain it's wrong lol, but it just won't get it. It keeps telling me I can use an argument in a function, but that's not true. Even the most powerful AI conversation model on the planet doesn't read the docs lol.
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Explanation of Behavior Cloning and Reinforcement Learning
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Yes, this explains how it works. The primary method for training this model is behavior cloning, and I explain what it is along with reinforcement learning (RL) and its limitations. And I don’t mention bias at all, sorry.
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Multimodal Learning for Objective Representation
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To address these issues, it is necessary to learn a vast amount of multimodal information (sound, image, text, etc.) using A LOT of data, in the hope of achieving the most objective representation of the world possible—something that is nearly impossible, yet still feasible with an 80-20 rule.
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ChatGPT: Human Evaluation of Solutions
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But with models like #ChatGPT, it goes beyond that. We don’t just want to know if it works or not. Instead, we aim to select the best-performing solutions from multiple options by having a human assess them.
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Reinforcement Learning for Machines
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This type of machine is trained using, in part, Reinforcement Learning algorithms. Essentially, the computer trains in simulations to learn how to solve the given problem.
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Technical risks of prolonged machine learning model training
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Nice ! Mais attention a l’incompréhension que tu suggère , c’est pas parce que on entraîne plus longtemps que c’est forcément mieux. Ton gradient peut diverger et aller dans les cactus . Ou juste stagner et là tu as juste gaspiller de l’électricité et un GPU.
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The Importance of Explainable AI for User Adoption
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Le problème que je regrette, c'est qu'on arrivera pas à embarquer la masse avec des plateformes pensé par des devs. Les algo de reco c'est ouf. Il faut bien s'en servir et pas juste les jeter à la poubelle mais développer des approches d'XAI par exemple. https://
en.wikipedia.org/wiki/Explainab
le_artificial_intelligence
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Addressing data privacy and algorithmic issues in AI
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Mais on fait quoi ? Est ce que je suis en train de cracher sur des intiatives open source et faire l'apologie des plateformes tech, NON. J'alerte constamment sur la viligance de vos données et sur les dérives des algos d'IA, comme dans cette vidéo