Artificial intelligence (AI) is no longer a futuristic concept; it’s a present reality that’s rapidly transforming our world. From the AI update in your toothbrush to the complex algorithms driving global tech innovation, AI is omnipresent and evolving at an unprecedented pace. To stay informed and prepared for the future, it’s crucial to understand the key terms and concepts that are currently shaping the field of AI. Here are seven essential AI terms that you should know.
Agentic AI: The Autonomous Agents
Agentic AI refers to AI agents that can perceive their environment, reason, act autonomously, and observe the outcomes to achieve specific goals. Unlike simple chatbots that respond to prompts one at a time, AI agents operate continuously, cycling through perception, reasoning, action, and observation. These agents can fulfill various roles, from booking your travel to analyzing data trends or managing DevOps tasks. The development of AI agents is a significant step towards more sophisticated and autonomous AI systems.
Large Reasoning Models: The Thoughtful AI
Large reasoning models are specialized large language models (LLMs) that have been fine-tuned to process problems step by step. These models are trained on data with verifiable correct answers, such as mathematical problems or compilable code. Through reinforcement learning, they learn to generate reasoning sequences that lead to accurate conclusions. When you encounter a chatbot that takes a moment to respond with “thinking,” it’s the reasoning model at work, internally breaking down the problem before providing a response.
Vector Database: The Semantic Search Engine
A vector database stores data not as raw files but as vectors—long lists of numbers that capture the semantic meaning of content. This approach allows for searches to be conducted as mathematical operations, finding semantically similar content by locating close vectors in the embedding space. For example, a vector database can find items similar to a mountain vista image by searching for the closest vectors that represent similar semantic content.
Retrieval Augmented Generation (RAG): Enriching AI Responses
Retrieval Augmented Generation (RAG) leverages vector databases to enhance prompts to LLMs. It begins with a RAG Retriever component that converts an input prompt into a vector using an embedding model. A similarity search is then performed in the vector database, and the results are used to enrich the original prompt. This system can, for instance, pull relevant sections from an employee handbook when asked a question about company policy, providing more accurate and context-rich responses.
Model Context Protocol (MCP): Standardizing AI Interactions
The Model Context Protocol (MCP) is a protocol that standardizes how applications provide context to LLMs. It enables AI to interact with external data sources, services, and tools in a uniform manner. MCP serves as a bridge between AI and various systems, allowing for seamless integration and communication. This standardization is crucial for developers, as it eliminates the need to create unique connections for each new tool or service.
Mixture of Experts (MOE): The Efficient AI Scaling
Mixture of Experts (MOE) is a concept that divides a large language model into specialized neural subnetworks, or “experts.” A routing mechanism activates only the necessary experts for a given task, and their outputs are then merged. This method allows for the scaling up of model size without a proportional increase in computational costs. MOE models, such as IBM’s Granite 4.0 series, can have numerous experts but only utilize a fraction of their parameters at any given time, making them highly efficient.
Artificial Super Intelligence (ASI): The Ultimate AI Goal
Artificial Super Intelligence (ASI) represents the pinnacle of AI development and remains a theoretical concept. ASI would surpass human intelligence, capable of recursive self-improvement, and could potentially resolve humanity’s most significant challenges or pose new, unimaginable ones. While ASI is still purely theoretical, the pursuit of Artificial General Intelligence (AGI)—AI that can perform any cognitive task as well as a human—is a step towards this ultimate goal.
The landscape of AI is vast and complex, but understanding these seven terms will provide a solid foundation for grasping the current state and future potential of AI. As AI continues to advance, it’s essential to keep up with the terminology and concepts that are driving innovation in the field.
Will you be attending the IBM Tech Exchange Conference in Orlando this October? It promises to be an exciting event filled with workshops, sessions, live demos, and more, all centered around the fascinating world of AI. Join us to learn more about these AI concepts and how they’re being applied in the real world.