Curious About AI? Supercharge Your Productivity With AI Life Hacks
#AI #AIio #BigData #ML #NLU #Futureofwork @chrismessina @ChristopherIsak @davidwkenny @debashis_dutta @petitegeek @fabiomoioli @GaryMarcus @asokan_telecom http://
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MACHINE LEARNING
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Supercharge Productivity With AI Life Hacks and Tips
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The Limits of Fine-Tuning and the Need for AI Supervision
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It still feels that with current capabilities, even if every individual will be able to fine tune the model for themselves and just for one specific task, it still will remain to be “high risk” / require supervision. In other words, ppl are expecting “delegation level 4+” while
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Data Engineering’s Critical Role in AI Revolution and Real-Time Insights
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Data Engineering has a critical role in the AI revolution – deliver the right data, at the right time, at any scale to help organizations gain real-time insights and harness the power of AI. Explore the Data Intelligence Platform's capabilities: https://
dbricks.co/3A5bRb5 -
Visual guide to copulas for machine learning applications
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while my_mcmc: gently(samples) – An intuitive, visual guide to copulas https://
bit.ly/3Y7zzwL
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
The Rapid Adoption of Generative AI in Business
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The Rapid Adoption of Generative AI
#AI #AIio #BigData #ML #NLU #Futureofwork @briansolis @Xbond49 @VitalikButerin @DataScienceCtrl @Benioff @johnnosta @IrmaRaste @Tiffani_Bova http://
ow.ly/WkRA30sH8ZY -
CoTracker3: Semi-Supervised Point Tracking Model with Pseudo-Labels
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10). CoTracker3 – proposes a new point tracking model and a new semi-supervised training recipe; enables usage of real videos without annotations during training by generating pseudo-labels using off-the-shelf teachers.https://t.co/vCbJzcXSPj
— DAIR.AI (@dair_ai) 20 octobre 202410). CoTracker3 – proposes a new point tracking model and a new semi-supervised training recipe; enables usage of real videos without annotations during training by generating pseudo-labels using off-the-shelf teachers.
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Model Kinship Strategy for Improved LLM Merging
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8). Model Kinship for Merging LLMs – proposes model kinship to measure the degree of similarity between LLMs; model kinship is used to build a model merging strategy (Top-k Greedy Merging with Model Kinship) which yields better performance.
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Agent S: Open Framework Enables Autonomous Computer Interaction
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7). Agent S – a new open agentic framework that enables autonomous interaction with computers through a GUI; Agent S tackles challenges such as acquiring knowledge, planning over long-task horizons, and handling dynamic interfaces.
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Janus: Unified Autoregressive Framework for Multimodal Understanding
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5). Janus – proposes a unified autoregressive framework for multimodal understanding and generation; it decouples visual encoding into independent pathways and leverages a single transformer architecture to improve flexibility and performance on both visual understanding and
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Inference Scaling Laws for Long-Context RAG Systems
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6). Inference Scaling for Long-Context RAG – uses two strategies to investigate scaling laws for RAG: in-context learning and iterative prompting; RAG performance improves with the expansion of the effective context length under optimal configurations.