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Lesson 6: The AI Frontier Terms

Topics Covered
  • Agentic AI: The shift from "Chat" to "Work."
  • Reasoning Models: "System 2" thinking for complex logic.
  • MCP: The universal standard for connecting AI to tools.
  • MoE: How models get bigger and faster simultaneously.
  • ASI: The theoretical horizon beyond human intelligence.

The field is moving fast. To stay relevant, you need to look beyond "LLM" and "Generative AI" to the terms that are defining the next 12 months of development.

1. Agentic AI (The Worker)​

Most people use AI as a Chatbot: You ask, it answers. The interaction is linear. Agentic AI is circular. It creates a feedback loop to accomplish a goal without constant human holding.

  • The Loop:
    1. Perceive: Read the environment (e.g., check new emails).
    2. Reason: Decide what needs to be done (e.g., "This email needs a meeting invite").
    3. Act: Execute the task (e.g., call the Calendar API).
    4. Observe: Check the result (e.g., "Did the invite send?").
  • Why it matters: Agents move AI from "Generating Text" to "Doing Work."

2. Large Reasoning Models (System 2)​

Standard LLMs are like System 1 thinking: fast, intuitive, and prone to "gut feeling" errors. Reasoning Models (like OpenAI's o1 or DeepSeek-R1) are System 2: slow, deliberate, and logical.

  • Chain of Thought: Before answering, the model generates a hidden internal monologue where it plans, critiques its own logic, and backtracks if it makes a mistake.
  • Trade-off: High latency (they "think" for seconds) for high accuracy on math, coding, and science.

3. Model Context Protocol (MCP) (The Standard)​

Until recently, connecting an LLM to your database or Slack required writing custom code for every single integration. It was messy and unscalable.

  • The Analogy: MCP is like USB for AI.
  • The Function: It provides a universal standard for how data systems (Servers) talk to AI tools (Clients). If your internal tool is "MCP Compliant," any MCP-enabled AI agent can instantly connect to it and start working, with no custom glue code required.

4. Mixture of Experts (MoE) (The Efficiency)​

How do we make models smarter without making them incredibly slow and expensive? MoE architectures divide the model into specialized sub-networks called Experts.

  • Sparse Activation: When you ask a question about coding, the model effectively "wakes up" only the Coding Expert and the Logic Expert, leaving the History and Poetry experts asleep.
  • Result: You get the intelligence of a massive model with the speed and cost of a much smaller one.

5. RAG & Vector Databases (The Memory)​

Recap from previous lessons: These are the "Long Term Memory" of AI.

  • Vector Database: Stores data as semantic numbers (Embeddings) rather than keywords.
  • RAG: The process of fetching that data to answer questions accurately.

6. Artificial Superintelligence (ASI) (The Horizon)​

This is the theoretical end-goal.

  • ANI (Artificial Narrow Intelligence): Good at one thing (Chess, Protein Folding). Where we were.
  • AGI (Artificial General Intelligence): As good as a human at any intellectual task. Where we are headed.
  • ASI (Artificial Superintelligence): Vastly smarter than the best human brains in practically every field. The theoretical future.

ASI implies Recursive Self-Improvement: an AI capable of rewriting its own code to become smarter, triggering an explosion in intelligence capabilities.