AI Foundation Learning — Channel Summaries
AI-powered summaries of 17 videos about AI Foundation Learning.
17 summaries
What is Agentic AI? Explained for AI Enthusiasts, Beginners, and Professionals
Agentic AI is artificial intelligence built to act like an autonomous agent—making decisions, taking actions, and interacting with its environment...
LLM Parameters Explained : Unlocking the secrets of LLM | AI Foundation Learning
Large language model performance hinges on “parameters”—the internal numeric settings that determine how the model learns language patterns and...
Top 10 Agentic AI Use Cases in Healthcare | Transforming Patient Care with AI
Agentic AI in healthcare is moving beyond single-purpose tools toward multi-agent systems that coordinate to diagnose, treat, monitor, and support...
What is Chunking in AI? The Beginners Guide. The Power of Chunking in LLMs & RAG Explained!
Chunking is the practical technique that lets AI systems handle information that’s too large to process in one go—by breaking text into smaller,...
Agentic AI in Financial Services: Autonomous Trading & Risk Management l Autonomous AI Agents
Agentic AI is moving finance toward systems that can decide and act with minimal human supervision—especially in autonomous trading and risk...
What is crewAI? | The Future of AI Agents and Multiagent Systems Explained
CrewAI is an open-source, Python-based multi-agent orchestration framework that lets multiple AI agents collaborate like a team to complete tasks...
Reinforcement Learning from Human Feedback (RLHF) - Beginners Guide | AI Foundation Learning
Reinforcement learning from human feedback (RLHF) is a training approach that steers AI agents toward better decisions by using human evaluations as...
Fundamentals of AI Agents: Guide for AI Enthusiasts, Beginners and Professionals #AIAgents #aitrends
AI agents are software systems that can carry out tasks on their own by continuously observing their environment, weighing predefined goals, and...
Innovations in AI Agents Architecture : Deep Dive | AI Agents Explained
AI agent architectures are moving from simple “chatbots” toward systems that can reason, plan, and use tools to complete real tasks—often by...
AutoGen Explained: The Future of AI Agents | How Multi-Agent Systems Will Change Everything!
AutoGen is an open-source framework built for creating AI “teams” rather than single, isolated chatbots—agents that communicate, collaborate, and...
Decision-Making in Agentic AI: Algorithms and Models | AI Foundation Learning AI Agents Explained
Agentic AI decision-making is the process of picking the best action an autonomous system can take from the information it has—then doing it fast...
Microsoft Magentic-One Explained. The future of AI Agents!
Microsoft’s Magentic 1 is positioned as a “generalist” multi-agent AI system built to handle open-ended, multi-step tasks across domains like file...
How to Build Agentic AI Systems: Core Components & Architecture Explained
Agentic AI systems—software entities that can perceive, decide, plan, and act toward goals without constant human input—are built by combining four...
Agentic AI and the Workforce: Automation, Augmentation, and Transformation | Agentic AI Explained
Agentic AI is reshaping work by taking actions on behalf of people—often with a degree of autonomy—so the workforce is being pulled in two directions...
AI + RPA: The Future of Work and Intelligent Automation | Latest Tools & Agentic Systems
AI + RPA is moving work automation beyond “click-and-copy” bots into systems that can read messy information, detect patterns, and make...
Types of Chunking : Top 10 Techniques Explained !
Chunking is the core technique of splitting large datasets into smaller, manageable “chunks” so AI systems can process information...
Top AI Agent Frameworks You Should Know | LangGraph, IBM Bee, CrewAI, AutoGen, AutoGPT
Five agent frameworks are positioned as practical building blocks for autonomous AI systems—each optimized for a different kind of complexity, from...