The promise of 3D LLM goes beyond just novelty – it paves the way for practical applications that sharpen AI understanding and interaction with our world. Real estate walkthroughs, interactive learning, remote exploration – the list goes on.
GENERATIVE AI
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LLM Transforms Text to Tangible 3D Models
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This new work enhances models and their understanding of our world. Offering opportunities for innovation, it's beneficial for businesses and careers. Want to learn more? Read the newsletter: https://
louisbouchard.substack.com/p/from-text-to
-tangible-3d-llm-unleashes
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3D-LLM: Expanding Language Models Into Three-Dimensional Understanding
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Large language models (LLMs) are expanding their horizons with 3D-LLM, moving beyond text, code, and images, and delving into our 3D world for a more comprehensive understanding.
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3D-LLM Enhances AI Perception and Environmental Interaction Capabilities
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3D-LLM significantly upgrades AI's perception of the world. It interacts with the environment, locating objects, and commonsense reasoning. Beyond computation, it integrates ChatGPT to train the model.
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Open Source AI Platforms Against Catastrophe Risk Claims
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An article about my vociferous support of open source AI platforms.
Demis Hassabis, Dario Amodei, and Sam Altman (among others) have scared governments about what they claim are risks of AI-fueled catastrophes. I know that Demis, at least, is sincere in his claims, but I think -
DefenseGPT: Revolutionary Cybersecurity AI Solution Unveiled
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𝗨𝗻𝘃𝗲𝗶𝗹𝗶𝗻𝗴 𝗗𝗲𝗳𝗲𝗻𝘀𝗲𝗚𝗣𝗧: 𝗔 𝗡𝗲𝘄 𝗗𝗮𝘄𝗻 𝗶𝗻 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 By @ingLiguori CEO @Kenovy_it Learn more https://
bit.ly/47j4ibX #𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 #𝗗𝗲𝗳𝗲𝗻𝘀𝗲𝗚𝗣𝗧 #AI #GPT -
Fine-tuning Embedders vs Embeddings: Key Differences Explained
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What is the difference between fine-tuning an embedder vs. fine-tuning embeddings? Fine-tuning embeddings would be like Deep Memory from Activeloop where you have an additional model transforming embeddings in a better representation for your dataset?
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Model Transparency and Training Data Release Critical for AI Accountability
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They really need to release their model and full transcripts to see what happened, but I'd be extremely shocked if the model described anything not already in the training data.
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Model Fine-tuning and Data Memorization in AI Systems
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I'm not wrong on methodology – but perhaps I did overstate this. The model was fine-tuned on papers describing exactly the thing they then asked questions about. In my experience models remember fine-tuning data precisely, and regurgitate it on demand.
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Sparse Packed FP64 Models: Efficiency and Safety Trade-offs
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Sparse packed fp64 models might become the new hotness… So few FLOPS — must be safe! 😀