Artificial Intelligence Glossary
AI Agent
An AI agent refers to a tool that uses AI technologies to perform a series of tasks on your behalf, such as filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. However, the concept implies an autonomous system that may draw on multiple AI systems to carry out multi-step tasks.
Chain-of-Thought Reasoning
In an AI context, chain-of-thought reasoning for large language models means breaking down a problem into smaller, intermediate steps to improve the quality of the end result. It usually takes longer to get an answer, but the answer is more likely to be right, especially in a logic or coding context. So-called reasoning models are developed from traditional large language models and optimized for chain-of-thought thinking thanks to reinforcement learning.
Deep Learning
A subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN) structure. This allows them to make more complex correlations compared to simpler machine learning-based systems, such as linear models or decision trees. The structure of deep learning algorithms draws inspiration from the interconnected pathways of neurons in the human brain.
Fine-Tuning
This means further training of an AI model that’s intended to optimize performance for a more specific task or area than was previously a focal point of its training — typically by feeding in new, specialized (i.e. task-oriented) data.
Large Language Model (LLM)
Large language models, or LLMs, are the AI models used by popular AI assistants, such as ChatGPT, Claude, Google’s Gemini, Meta’s AI Llama, Microsoft Copilot, or Mistral’s Le Chat. When you chat with an AI assistant, you interact with a large language model that processes your request directly or with the help of different available tools, such as web browsing or code interpreters.
Neural Network
Neural network refers to the multi-layered algorithmic structure that underpins deep learning — and, more broadly, the whole boom in generative AI tools following the emergence of large language models. Although the idea to take inspiration from the densely interconnected pathways of the human brain as a design structure for data processing algorithms dates all the way back to the 1940s, it was the much more recent rise of graphical processing hardware (GPUs) — via the video game industry — that really unlocked the power of theory.
Weights
Weights are core to AI training as they determine how much importance (or weight) is given to different features (or input variables) in the data used for training the system — thereby shaping the AI model’s output. Put another way, weights are numerical parameters that define what’s most salient in a data set for the given training task.
Conclusion
In this glossary, we’ve covered some of the most important terms related to artificial intelligence, including AI agents, chain-of-thought reasoning, deep learning, fine-tuning, large language models, neural networks, and weights. These terms are essential for understanding the rapidly evolving landscape of AI.
FAQs
Q: What is an AI agent?
A: An AI agent is a tool that uses AI technologies to perform a series of tasks on your behalf.
Q: What is chain-of-thought reasoning?
A: In an AI context, chain-of-thought reasoning means breaking down a problem into smaller, intermediate steps to improve the quality of the end result.
Q: What is deep learning?
A: Deep learning is a subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN) structure.
Q: What is fine-tuning?
A: Fine-tuning means further training of an AI model that’s intended to optimize performance for a more specific task or area than was previously a focal point of its training.
Q: What is a large language model (LLM)?
A: Large language models, or LLMs, are the AI models used by popular AI assistants, such as ChatGPT, Claude, Google’s Gemini, Meta’s AI Llama, Microsoft Copilot, or Mistral’s Le Chat.
Q: What is a neural network?
A: Neural network refers to the multi-layered algorithmic structure that underpins deep learning — and, more broadly, the whole boom in generative AI tools following the emergence of large language models.
Q: What are weights?
A: Weights are numerical parameters that define what’s most salient in a data set for the given training task, determining how much importance (or weight) is given to different features (or input variables) in the data used for training the system.
