IBM's Analog AI Chip 🧠 // New Google Search AI Features 🕵️‍♂️ // Multimodal LLMs with LCL 🔗

IBM has unveiled a new prototype of an analog AI chip that works like a human brain, promising to be more efficient and less battery-draining for computers and smartphones. Google has rolled out new search AI features, including the ability to see definitions within AI-generated responses and color-coded syntax highlighting for coding. The paper "Learning to Identify Critical States for Reinforcement Learning from Videos" explores how videos can be used to extract implicit information about rewarding action sequences in deep reinforcement learning, with potential applications in robotics. "Link-Context Learning for Multimodal LLMs" proposes a new approach called Link-Context Learning (LCL) that emphasizes "reasoning from cause and effect" to augment the learning capabilities of Multimodal Large Language Models (MLLMs), with the potential to significantly improve their performance. Contact:  [email protected] Timestamps: 00:34 Introduction 01:46 IBM unveils an analog AI chip that works like a human brain 03:06 Google Rolls Out New Search AI features 04:53 The Mathematics of Training LLMs — with Quentin Anthony of Eleuther AI 05:54 Fake sponsor 07:38 Learning to Identify Critical States for Reinforcement Learning from Videos 09:27 RAVEN: In-Context Learning with Retrieval Augmented Encoder-Decoder Language Models 10:55 Link-Context Learning for Multimodal LLMs 13:01 Outro