7 AI Terms You Need to Know: Agents, RAG, ASI & More

Artificial intelligence is rapidly evolving, with even everyday items like toothbrushes receiving AI updates. To navigate this fast-paced field, understanding key terminology is crucial. This video highlights seven important AI terms, starting with Agentic AI, which refers to AI systems that can reason and autonomously act to achieve goals, progressing through stages of perception, reasoning, and action. These agents are often built using specialized Large Reasoning Models (LRMs), a type of LLM fine-tuned for step-by-step problem-solving, unlike standard LLMs that provide immediate responses.

The discussion then delves into Vector Databases, which store data as numerical vectors capturing semantic meaning, enabling similarity searches for related content. These databases are integral to RAG (Retrieval Augmented Generation), a technique that enriches LLM prompts with relevant information retrieved from these databases. The Model Context Protocol (MCP) is introduced as a standard for AI to interact with external data sources and tools, simplifying integration for developers.

Further explaining efficiency in AI models, Mixture of Experts (MoE) is presented as a method where large models are divided into specialized subnetworks, with a routing mechanism activating only necessary "experts" for a given task, thus scaling model size without proportional compute cost increases. Finally, the video touches upon ASI (Artificial Superintelligence), a theoretical future state where AI surpasses human intelligence and can recursively self-improve, contrasting it with AGI (Artificial General Intelligence), which would perform all cognitive tasks at a human expert level.

• The field of artificial intelligence is advancing rapidly, making it essential to understand key terminology.
• Agentic AI refers to autonomous AI systems that perceive, reason, and act to achieve goals, often utilizing Large Reasoning Models (LRMs) for step-by-step problem-solving.
• Vector databases store data as numerical vectors, enabling semantic similarity searches, which are foundational for Retrieval Augmented Generation (RAG).
• Model Context Protocol (MCP) standardizes AI interaction with external systems, while Mixture of Experts (MoE) offers an efficient way to scale AI models by using specialized subnetworks.
• The discussion also covers Artificial General Intelligence (AGI) and the theoretical concept of Artificial Superintelligence (ASI), where AI surpasses human intellect.

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Agentic AI [1:07]

• AI agents can reason and act autonomously to achieve goals.
• They operate in stages: perceive environment, reason about next steps, act on the plan, and observe results.
• AI agents can serve various roles, such as travel agents, data analysts, or DevOps engineers.
• They are typically built using Large Reasoning Models.

AI agents, they can reason and act autonomously to achieve goals.

Large Reasoning Models [2:29]

• These are specialized Large Language Models (LLMs) fine-tuned for reasoning.
• They work through problems step-by-step, essential for complex, multi-step tasks.
• LRMs are trained on problems with verifiable correct answers (e.g., math, code).
• Through reinforcement learning, they generate reasoning sequences leading to correct answers.
• The "thinking" pause in chatbots indicates an LRM generating an internal chain of thought.

Well, that's the reasoning model at work, generating an internal chain of thought to break down a problem step by step before generating a response.

Vector Database [3:28]

• Stores data not as raw files but as numerical vectors created by embedding models.
• Vectors capture the semantic meaning of the data.
• Benefits include performing mathematical operations for similarity searches, finding semantically similar content.
• Can be used for images, text, or music, finding items similar to a mountain picture or text articles.

In a vector database, we don't store raw data like text files and like images just as blobs of data. We actually use something called an embedding model. And that embedding model is used to convert that data from these images here into actually a vector.

RAG or Retrieval Augmented Generation [4:20]

• RAG utilizes vector databases to enrich prompts given to an LLM.
• A RAG retriever takes a user prompt, converts it into a vector, and performs a similarity search in a vector database.
• The retrieved relevant information is then embedded into the original LLM prompt.
• This allows LLMs to answer questions by referencing specific external data, like company policies from an employee handbook.

So, I can I can ask a question about let's say company policy. And then this RAG system is going to pull the relevant section from the employee handbook to include in the prompt.

Model Context Protocol or MCP [5:10]

• MCP standardizes how applications provide context to LLMs, enabling interaction with external data sources and services.
• It creates standardized connections for AI to access systems like databases, code repositories, or email servers.
• An MCP server acts as the connection point, allowing AI to understand how to interact with these tools.
• This eliminates the need for developers to build custom, one-off integrations for each new tool.

MCP standardizes how applications provide context to LLMs. So, if you want your large language model here to be able to connect to stuff.

Mixture of Experts or MOE [6:16]

• MOE divides a large language model into multiple specialized neural subnetworks called "experts."
• A routing mechanism activates only the necessary experts for a specific task.
• The outputs from the activated experts are merged through mathematical operations.
• This approach allows for efficient scaling of model size without proportional increases in compute costs.
• For example, a model with billions of parameters might only use a fraction of them at inference time.

You see, MoE divides a large language model into a series of experts. [...] And then it uses a routing mechanism to activate only the agents it needs for a particular task, or only the experts in this case that it needs for a task.

ASI, Artificial Superintelligence [7:30]

• ASI is a theoretical stage of AI where intelligence surpasses human intellect across all domains.
• It is the ultimate goal of many frontier AI labs but does not currently exist.
• ASI systems could possess the capability for recursive self-improvement, continuously becoming smarter.
• This could lead to solving major global problems or creating unimaginable new challenges.
• It is contrasted with AGI (Artificial General Intelligence), which would perform cognitive tasks as well as any human expert.

ASI systems would have an intellectual scope beyond human level intelligence, potentially capable of recursive self-improvement.