Key AI Terms Explained: Understanding the Language of Artificial Intelligence

Artificial intelligence is transforming industries and creating new terminology that can be difficult to follow. From product meetings to industry panels, terms like LLMs, RAG, RLHF, and opaque recurrence are becoming common. To help clarify, here is a straightforward guide to some of the most relevant AI concepts. Artificial General Intelligence (AGI) refers to AI systems that can perform a wide range of tasks at or above human level. Definitions vary slightly among experts, but generally, AGI implies highly autonomous systems capable of outperforming humans in economically valuable work. An AI agent is a tool that can autonomously perform multi-step tasks such as managing schedules, booking services, or writing code. These agents often integrate multiple AI systems and rely on evolving infrastructure to deliver on their capabilities. API endpoints act like hidden controls within software that allow other programs to interact with it. AI agents increasingly use these interfaces to automate complex workflows without human intervention. Chain-of-thought reasoning involves breaking down problems into intermediate steps to improve accuracy, especially in logic or coding tasks. This approach is used to enhance large language models (LLMs), which are deep neural networks trained on vast amounts of text data to understand and generate human language. Coding agents are specialized AI programs that autonomously write, test, and debug code across entire projects, reducing the manual workload for developers. Compute refers to the computational power—provided by hardware like GPUs and TPUs—that enables AI models to train and operate. Deep learning is a subset of machine learning that uses multi-layered neural networks inspired by the human brain. These models learn features directly from data but require large datasets and significant training time. Diffusion models generate content by learning to reverse a process that adds noise to data, enabling the creation of images, music, or text. Distillation is a technique where a smaller AI model is trained to mimic a larger one, improving efficiency without significant loss of performance. This method is believed to be behind faster versions of models like GPT-4 Turbo. Fine-tuning involves additional training of an AI model on specialized data to optimize it for specific tasks or sectors. Generative Adversarial Networks (GANs) use two neural networks competing against each other to produce realistic outputs, commonly used in image and video generation. Hallucination describes when AI models generate incorrect or fabricated information, posing challenges for reliability and safety. Inference is the process of running a trained AI model to make predictions or generate outputs. The hardware used can range from smartphones to specialized AI accelerators. Large language models (LLMs) power popular AI assistants by processing language through billions of parameters learned from extensive text corpora. Memory caching optimizes inference by storing intermediate calculations to speed up responses and reduce computational load. Model Context Protocol (MCP) is an open standard enabling AI models to connect with external tools and data sources without custom integration, facilitating broader interoperability. Mixture of Experts (MoE) architectures divide a neural network into specialized sub-networks, activating only a subset for each task to improve efficiency. Neural networks are multi-layered algorithmic structures foundational to deep learning and modern AI capabilities. Opaque recurrence is a reasoning technique where an AI model repeatedly processes the same query internally rather than generating step-by-step explanations. While efficient, it raises concerns about transparency and safety. Open source AI models allow public access to their code, fostering collaboration and independent safety reviews, contrasting with closed-source models that keep their code private. Parallelization enables simultaneous processing of many calculations, critical for training and running large AI models efficiently. RAMageddon refers to the current shortage of RAM chips driven by high demand from AI data centers, impacting various tech sectors through increased costs and supply constraints. Recursive self-improvement describes AI systems that autonomously enhance their own capabilities, a concept that some startups are exploring as a future research frontier. Reinforcement learning trains AI models through trial and error, using feedback signals to improve performance, notably applied in gaming and refining language models. Tokens are the basic units of language AI models process, similar to words or word parts, and are central to measuring usage and cost in AI services. Throughput measures how much data an AI system can process over time, influencing response speed and user capacity. Training is the process of feeding data to AI models so they can learn patterns and generate useful outputs, often requiring substantial computational resources. Transfer learning leverages knowledge from one trained model to accelerate development on related tasks, improving efficiency when data is limited. Validation loss is a metric used during training to assess how well a model is learning, helping to avoid overfitting and guide adjustments. Weights are numerical parameters in AI models that determine the importance of different input features, adjusting during training to improve accuracy. This glossary is regularly updated to reflect ongoing developments in AI technology and terminology.