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Person: "rohan-paul"
not much happened today
llama mistral openai decagon sierra togethercompute vertical-saas funding protein-structure-prediction lora self-supervised-learning model-optimization neural-architecture-search model-evaluation ethics transformers multi-agent-systems long-context mira-murati demis-hassabis clement-delangue john-o-whitaker yann-lecun francois-chollet ajeya-cotra rohan-paul adcock-brett
Vertical SaaS agents are gaining rapid consensus as the future of AI applications, highlighted by Decagon's $100m funding and Sierra's $4b round. OpenAI alumni are actively raising venture capital and forming new startups, intensifying competition in the AI market. Demis Hassabis celebrated the Nobel Prize recognition for AlphaFold2, a breakthrough in protein structure prediction. Advances in AI models include techniques like LoRA projectors and annealing on high-quality data, while discussions emphasize the need for high-bandwidth sensory inputs beyond language for common sense learning. New methods like LoLCATs aim to optimize transformer models such as Llama and Mistral for efficiency. Ethical concerns about AI agents performing harmful tasks remain under investigation. The AI community continues to explore model evaluation challenges and optimization frameworks like LPZero for neural architecture search.
Summer of Code AI: $1.6b raised, 1 usable product
ltm-2 llama-3-1-405b gemini-advanced cognition poolside codeium magic google-deepmind nvidia google-cloud long-context model-efficiency custom-hardware cuda training-stack gpu-scaling neural-world-models diffusion-models quantization nat-friedman ben-chess rohan-paul
Code + AI is emphasized as a key modality in AI engineering, highlighting productivity and verifiability benefits. Recent major funding rounds include Cognition AI raising $175M, Poolside raising $400M, Codeium AI raising $150M, and Magic raising $320M. Magic announced their LTM-2 model with a 100 million token context window, boasting efficiency improvements over Llama 3.1 405B by about 1000x cheaper in sequence-dimension algorithm and drastically lower memory requirements. Magic's stack is built from scratch with custom CUDA and no open-source foundations, partnered with Google Cloud and powered by NVIDIA H100 and GB200 GPUs, aiming to scale to tens of thousands of GPUs. Google DeepMind revealed updates to Gemini Advanced with customizable expert "Gems." Neural Game Engines like GameNGen can run DOOM in a diffusion model trained on 0.9B frames. The content also references LLM quantization research by Rohan Paul.