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7 Essential AI Terms Explained
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The big takeaway
A breakdown of seven critical AI concepts: agentic AI (autonomous reasoning agents), large reasoning models (step-by-step LLMs), vector databases (semantic search via embeddings), RAG (retrieval-augmented generation), Model Context Protocol (standardized tool access), mixture of experts (efficient scaling), and ASI (artificial superintelligence).
Agentic AI & Reasoning Models
What Are AI Agents
AI agents perceive their environment, reason about the best next steps, act on their plan, and observe results—repeating this cycle autonomously. Unlike chatbots that respond to single prompts, agents run continuously and can take on roles like travel booking, data analysis, or DevOps engineering.
1
Perceive environment
2
Reason about next steps
3
Act on plan
4
Observe results
5
Repeat cycle
Autonomous agent feedback loop
Large Reasoning Models
Specialized LLMs trained through reasoning-focused fine-tuning to work through problems step-by-step rather than generating immediate responses. They learn via reinforcement learning on verifiably correct problems (math, compilable code) to generate internal chains of thought before answering.
Regular LLMs
Generate responses immediately
Reasoning Models
Work through problems step-by-step
How reasoning models differ from standard LLMs
Data & Retrieval Systems
Vector Databases
Instead of storing raw text or images as blobs, vector databases use embedding models to convert data into vectors—long lists of numbers capturing semantic meaning. This enables mathematical similarity searches to find semantically related content (similar images, articles, music) by finding closest vectors in embedding space.
1
Input raw data (text, image, music)
2
Embedding model converts to vector
3
Store as multidimensional numbers
4
Perform similarity search
5
Return semantically similar results
How vector databases enable semantic search
Retrieval-Augmented Generation (RAG)
RAG enriches LLM prompts by using vector databases to retrieve relevant context. When a user asks a question, the RAG retriever converts it to a vector, searches the database for similar content, and embeds those results into the prompt sent to the LLM—enabling accurate answers grounded in specific data like employee handbooks.
1
User input prompt
2
Convert to vector via embedding model
3
Similarity search in vector database
4
Retrieve relevant documents
5
Embed results into LLM prompt
6
Generate grounded response
RAG workflow for context-aware responses
Model Architecture & Integration
Model Context Protocol (MCP)
MCP standardizes how applications provide context and tools to LLMs, replacing one-off custom connections. It enables LLMs to reliably connect to external systems—databases, code repositories, email servers, or any external tool—through a standardized MCP server interface.
1
LLM needs external data/tool access
2
MCP server standardizes connection
3
Connect to database, repository, email, etc.
4
Developers avoid building custom integrations
MCP enables standardized tool integration
Mixture of Experts (MoE)
MoE divides an LLM into specialized neural subnetworks (experts) and uses a routing mechanism to activate only the experts needed for each task, then merges their outputs. This efficiently scales model size without proportional compute increases—models like IBM Granite 4.0 may have dozens of experts but activate only a fraction per token.
Billions of parameters
Total model size, but only a fraction activated per inference
MoE efficiency: scale without proportional compute cost
Future AI Frontiers
Artificial Superintelligence (ASI)
ASI is a theoretical future state where AI systems possess intellectual capability beyond human-level intelligence, potentially capable of recursive self-improvement—redesigning and upgrading themselves in an endless cycle. Unlike AGI (artificial general intelligence, which matches human expert performance), ASI represents an existential leap with unknown consequences.
1
Current AI
Narrow, task-specific
2
AGI (theoretical)
All cognitive tasks at human expert level
3
ASI (theoretical)
Beyond human intelligence, self-improving
AI capability progression toward superintelligence
Worth quoting
"AI agents can reason and act autonomously to achieve goals."
— Martin (presenter), at [0:31]
"ASI systems could redesign and upgrade itself, becoming ever smarter in an endless cycle."
— Martin (presenter), at [8:42]
"I came up with seven, but I could have come up with 70. There's so much going on in this space."
— Martin (presenter), at [9:14]
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7 Essential AI Terms Explained

Summary of the video “7 AI Terms You Need to Know: Agents, RAG, ASI & More” by IBM Technology.

A breakdown of seven critical AI concepts: agentic AI (autonomous reasoning agents), large reasoning models (step-by-step LLMs), vector databases (semantic search via embeddings), RAG (retrieval-augmented generation), Model Context Protocol (standardized tool access), mixture of experts (efficient scaling), and ASI (artificial superintelligence).

Agentic AI & Reasoning Models

What Are AI Agents

AI agents perceive their environment, reason about the best next steps, act on their plan, and observe results—repeating this cycle autonomously. Unlike chatbots that respond to single prompts, agents run continuously and can take on roles like travel booking, data analysis, or DevOps engineering.

Large Reasoning Models

Specialized LLMs trained through reasoning-focused fine-tuning to work through problems step-by-step rather than generating immediate responses. They learn via reinforcement learning on verifiably correct problems (math, compilable code) to generate internal chains of thought before answering.

Data & Retrieval Systems

Vector Databases

Instead of storing raw text or images as blobs, vector databases use embedding models to convert data into vectors—long lists of numbers capturing semantic meaning. This enables mathematical similarity searches to find semantically related content (similar images, articles, music) by finding closest vectors in embedding space.

Retrieval-Augmented Generation (RAG)

RAG enriches LLM prompts by using vector databases to retrieve relevant context. When a user asks a question, the RAG retriever converts it to a vector, searches the database for similar content, and embeds those results into the prompt sent to the LLM—enabling accurate answers grounded in specific data like employee handbooks.

Model Architecture & Integration

Model Context Protocol (MCP)

MCP standardizes how applications provide context and tools to LLMs, replacing one-off custom connections. It enables LLMs to reliably connect to external systems—databases, code repositories, email servers, or any external tool—through a standardized MCP server interface.

Mixture of Experts (MoE)

MoE divides an LLM into specialized neural subnetworks (experts) and uses a routing mechanism to activate only the experts needed for each task, then merges their outputs. This efficiently scales model size without proportional compute increases—models like IBM Granite 4.0 may have dozens of experts but activate only a fraction per token.

Future AI Frontiers

Artificial Superintelligence (ASI)

ASI is a theoretical future state where AI systems possess intellectual capability beyond human-level intelligence, potentially capable of recursive self-improvement—redesigning and upgrading themselves in an endless cycle. Unlike AGI (artificial general intelligence, which matches human expert performance), ASI represents an existential leap with unknown consequences.

Notable quotes

AI agents can reason and act autonomously to achieve goals. — Martin (presenter)
ASI systems could redesign and upgrade itself, becoming ever smarter in an endless cycle. — Martin (presenter)
I came up with seven, but I could have come up with 70. There's so much going on in this space. — Martin (presenter)

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