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Topic: "long-form-rag"
World_sim.exe
gpt-4 gpt-4o grok-1 llama-cpp claude-3-opus claude-3 gpt-5 nvidia nous-research stability-ai hugging-face langchain anthropic openai multimodality foundation-models hardware-optimization model-quantization float4 float6 retrieval-augmented-generation text-to-video prompt-engineering long-form-rag gpu-optimization philosophy-of-ai agi-predictions jensen-huang yann-lecun sam-altman
NVIDIA announced Project GR00T, a foundation model for humanoid robot learning using multimodal instructions, built on their tech stack including Isaac Lab, OSMO, and Jetson Thor. They revealed the DGX Grace-Blackwell GB200 with over 1 exaflop compute, capable of training GPT-4 1.8T parameters in 90 days on 2000 Blackwells. Jensen Huang confirmed GPT-4 has 1.8 trillion parameters. The new GB200 GPU supports float4/6 precision with ~3 bits per parameter and achieves 40,000 TFLOPs on fp4 with 2x sparsity.
Open source highlights include the release of Grok-1, a 340B parameter model, and Stability AI's SV3D, an open-source text-to-video generation solution. Nous Research collaborated on implementing Steering Vectors in Llama.CPP.
In Retrieval Augmented Generation (RAG), a new 5.5-hour tutorial builds a pipeline using open-source HF models, and LangChain released a video on query routing and announced integration with NVIDIA NIM for GPU-optimized LLM inference.
Prominent opinions include Yann LeCun distinguishing language from other cognitive abilities, Sam Altman predicting AGI arrival in 6 years with a leap from GPT-4 to GPT-5 comparable to GPT-3 to GPT-4, and discussions on the philosophical status of LLMs like Claude. There is also advice against training models from scratch for most companies.