group: model-efficiency-compression title: “LLMs”
LLMs
Large Language Models (LLMs) are a subset of Artificial Intelligence trained on massive datasets to understand, interpret, and generate human-like language.
Multimodal Capabilities
- Evolution from text-centric models toward multimodal-ai.
- Modality refers to distinct data types processed by the model, including:
- text
- images
- audio
- lidar
- thermal imaging
- Multimodal models are distinguished by their capacity to both ingest and generate content across these multiple data modalities.
Related Notes
- 2026 04 10 Multimodal AI Concepts Approaches and Data Processing by LLMs
- 2026 04 10 Multimodal AI Concepts Approaches and Data Processing by LLMs
Agentic & Reasoning Advancements
- Recent developments focus on moving “Towards Real World Agents” by enhancing agentic-ai and multimodal-ai (e.g., Alibaba Qwen 2.0).
Related Notes
- 2026 04 10 Alibaba Qwen 36 Plus Agentic Coding and Multimodal Reasoning Towards
- 2026 04 10 AI Guided Software Development Leveraging Claude Code Agent Skills for
- 2026 04 10 Anthropics Project Glasswing AIs Dual Role in Software Cybersecurity
Summary
The video, presented by Martin Keen of IBM, introduces and explains the concept of Multimodal AI. It begins by defining “modality” in the context of AI as a data modality, referring to different types of data such as text, images, audio, lidar, and thermal imaging. Multimodal AI models are distinguished by their ability to ingest and/or generate content across these multiple data modalities.
Related Notes
- 2026 04 10 Multimodal AI Concepts Approaches and Data Processing by LLMs
Anthropic
Source Notes
- 2026-04-14: [[lab-notes/2026-04-14-Optimizing-AI-Costs-and-Privacy-with-Local-Open-Source-Models-and-Hybr|“But OpenClaw is expensive…“]]
- 2026-04-14: [[lab-notes/2026-04-14-Optimizing-AI-Costs-and-Privacy-with-Local-Open-Source-Models-and-Hybr|“But OpenClaw is expensive…“]]
- 2026-04-14: [[lab-notes/2026-04-14-Optimizing-AI-Costs-and-Privacy-with-Local-Open-Source-Models-and-Hybr|“But OpenClaw is expensive…“]]
- 2026-04-23: Engine Survival: The Critical Role of Oil Pressure and Warning Lights
- 2026-04-23: Engine Survival: The Critical Role of Oil Pressure and Warning Lights
- 2026-04-23: [[lab-notes/2026-04-23-Anthropics-Compute-Miscalculation-Claude-Demand-and-Strategic-Impact|Anthropic’s Compute Miscalculation: Claude Demand and Strategic Impact]]
- 2026-04-23: [[lab-notes/2026-04-23-Claude-Routines-Action-Based-AI-Automation-for-Business-Event-Response|Claude Routines: Action-Based AI Automation for Business Event Response]]
- 2026-04-23: Engine Survival: The Critical Role of Oil Pressure and Warning Lights
- 2026-04-23: [[lab-notes/2026-04-23-Claude-Routines-Action-Based-AI-Automation-for-Business-Event-Response|Claude Routines: Action-Based AI Automation for Business Event Response]]
- 2026-04-14: [[lab-notes/2026-04-14-Optimizing-AI-Costs-and-Privacy-with-Local-Open-Source-Models-and-Hybr|“But OpenClaw is expensive…“]]
- 2026-04-14: [[lab-notes/2026-04-14-Optimizing-AI-Costs-and-Privacy-with-Local-Open-Source-Models-and-Hybr|“But OpenClaw is expensive…“]]
- 2026-04-07: LiteParse - The Local Document Parser
- 2026-04-08: LiteParse - The Local Document Parser
- 2026-04-29: # Optimizing LLM Agent Token Usage with MCP and Code Execution Generated: 2026-04-29 · API: Gemini 2.5 Flash · Modes: Summary --- Optimizing LLM Agent Token Usage with MCP and Code Execution Clip title: Save 98% on AI Agent Tokens With This One Trick Author / channel: (Optimizing LLM Agent Token Usage with MCP and Code Execution)