[ AI ] IBM granite, NVIDIA robotics, IBM Langflow, watsonx, LMCache, LiteRT, Self driving car, Mamba model, RAG
IBM granite: Time series from sensor, stock market * Forcast * Anomaly detection - classification high medium risk, data synthesis Embedding AI - 1M params - edge device - small AI Send insight --- NVIDIA * Jackson Thor robotic * Nemo ---- * IBM Langflow : open source * Watsonx (n8n) Enterprise : guardrails / log, tool using policy ----- LMcache : open source KC cache Prompt -> key value (RAGs) Same context ( e.g. same docs) Reduce operating costs CacheGen : make cache cheap to move and store CacheBlend : reuse chunk, not just prefix ---- Google มี framework ที่ optimize AI บน mobile phone นั่นคือ LiteRT : google pytorch Quantisation เท่าไหร่บน device นี้ Model explorer: quantize แล้ว error < 5% XMNpack ใช้ CPU ลดลง Google AI edge portal : benchmark AI on real device ---- Self driving car * Nvidia Alpamayo 2 model * Carla simulation Random forest -> multiple scenarios -> json -> LLM Car talk to mobile phone (cross the road) ----- Mamba model (recursive) - alte...