Understanding AI Terminology: A Modern Glossary

As artificial intelligence reshapes our world, it also introduces a unique lexicon. During meetings, panels, or pitches, you may encounter terms like LLMs, RAG, and RLHF, along with newly coined phrases such as “opaque recurrence,” which recently arose from OpenAI’s Astra model. The speed of this evolving vocabulary can leave even tech veterans feeling overwhelmed. This glossary aims to clarify the most relevant AI terms for individuals involved in building, investing, or engaging with AI technologies.

Artificial General Intelligence (AGI)

Artificial General Intelligence, or AGI, refers to AI systems that can perform tasks with greater capability than an average human. OpenAI’s CEO, Sam Altman, describes it as a “median human co-worker,” while OpenAI’s charter states that AGI consists of highly autonomous systems excelling in economically significant tasks. In contrast, Google DeepMind considers AGI to be AI that matches human cognitive abilities across most tasks. If you’re feeling confused, you’re not alone; even experts in AI are still grappling with its definition.

AI Agents

An AI agent is a sophisticated tool that utilizes AI technologies to carry out complex tasks on behalf of users—far beyond the capabilities of standard chatbots. These tasks may include filing expenses, booking travel, or even writing and maintaining code. The term’s meaning can vary based on context, and infrastructure is still being developed to realize its full potential. Essentially, an AI agent represents an autonomous system capable of executing multi-step tasks using various AI systems.

API Endpoints

Think of API endpoints as “buttons” that software uses to communicate and perform actions. Developers leverage these interfaces to integrate applications, allowing systems to pull data or enabling AI agents to operate third-party services autonomously. While users may never see these endpoints, smart home devices and connected platforms utilize them internally. As AI agents become more advanced, they increasingly identify and utilize these endpoints, unlocking new automation possibilities.

Chain-of-Thought Reasoning

In AI, chain-of-thought reasoning involves breaking down complex problems into smaller, manageable steps, thereby enhancing the quality of outputs. In contrast to straightforward questions like “Which animal is taller, a giraffe or a cat?” more complex questions may require intermediate calculations. This method typically provides more accurate results, especially in logical or coding tasks, although it may take longer. Models designed for chain-of-thought reasoning are fine-tuned through reinforcement learning techniques.

Coding Agents

Distinct from general AI agents, coding agents are specialized programs that can autonomously write, debug, and test code. These agents elevate productivity by performing iterative tasks usually requiring significant input from a human developer. Think of them as remarkably efficient interns—always focused and ever-available—although final code review is still necessary.

Compute

Compute refers to the essential computational power that enables AI models to function, involving hardware like GPUs, CPUs, and TPUs. This processing capability is the foundation of the AI industry, allowing for the training and deployment of robust models.

Deep Learning

Deep learning is a subset of machine learning characterized by artificial neural networks (ANNs) that can make complex correlations in data. Inspired by the human brain, deep learning allows algorithms to identify features in data without human intervention. However, it typically requires substantial amounts of data and longer training times, leading to higher development costs.

Diffusion

Diffusion technology underlies various generative AI models by mimicking physical diffusion to “destroy” data through noise. The goal is to develop a reverse diffusion process for data recovery. This technique is particularly applied in fields like art, music, and text generation.

Distillation

Distillation simulates a “teacher-student” model where a larger AI model conveys knowledge to a smaller, more efficient model. Outputs from the teacher are recorded and used to train the student. This method can enhance model efficiency and has applications in creating faster models like GPT-4 Turbo.

Fine-Tuning

Fine-tuning involves further training an AI model to enhance its performance in a specific task area, usually by incorporating specialized, task-oriented data. AI startups commonly start with large language models and then fine-tune them for particular applications in their target sector.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs, consist of two neural networks—the generator and the discriminator—that work in opposition. While the generator creates data outputs, the discriminator evaluates their authenticity. This competitive framework allows for the production of realistic data, such as deepfakes, without additional human intervention.

Hallucination

In AI, hallucination refers to the generation of incorrect or misleading information. This phenomenon poses significant challenges for reliability and can lead to serious real-world consequences, particularly in areas like healthcare. The rise of hallucinations highlights a push for more specialized AI models to mitigate misinformation risks.

Inference

Inference is the action of applying a trained AI model to make predictions based on previously learned data patterns. This process requires hardware, ranging from smartphones to powerful cloud servers, for effective execution. While inference is crucial, it will only work if the model has undergone successful training.

Large Language Models (LLMs)

Large language models, or LLMs, are the engines driving popular AI assistants like ChatGPT and others. Comprising billions of parameters, these deep neural networks learn language relationships from extensive text data, dynamically generating contextually relevant responses.

Memory Cache

Memory cache is a process that optimizes inference by storing specific calculations for future reference, reducing computational demand. Techniques like key-value (KV) caching enhance efficiency in transformer-based models, allowing for quicker responses to user queries.

Model Context Protocol (MCP)

MCP is an open standard that facilitates the connection between AI models and external tools, databases, or applications. Its introduction allows seamless data management without the need for custom integrations, simplifying interactions across platforms.

Mixture of Experts (MoE)

MoE architecture divides a neural network into smaller specialized sub-networks, activating only the relevant “experts” for a given task. This efficiency optimizes processing by ensuring that only a portion of the network is engaged at one time, aiding in scalability and performance.

Neural Network

Neural networks are multi-layered structures that form the basis of deep learning and generative AI technologies. The rise of powerful GPUs has significantly improved the capabilities of neural networks, enabling breakthroughs in various domains.

Recursive Self-Improvement (RSI)

This term denotes a hypothetical scenario where AI models autonomously enhance themselves, potentially accelerating their capabilities dramatically. While some fear it poses existential risks, many startups view it as a new frontier for AI development.

Reinforcement Learning (RL)

Reinforcement learning involves training AI through feedback, allowing it to explore actions and adapt its behavior based on received rewards. This method is highly effective in various applications, including gaming and robotics.

Tokens

Tokens are the fundamental units of data that AI models process and communicate. They are generated through tokenization, breaking down text into manageable segments. Understanding tokens is crucial for commercial use, as they often determine costs based on usage.

Training

Training refers to feeding data into AI systems, allowing them to learn and generate valuable outputs. This fundamental process can be resource-intensive, with ongoing trends leaning towards more efficient hybrid training methods.

Transfer Learning

Transfer learning utilizes a pre-trained AI model as a foundation for a new, related task. By repurposing previously acquired knowledge, this approach fosters efficiency, especially when data is limited in the target domain.

Validation Loss

Validation loss is a crucial metric that indicates how well an AI model is learning during its training phase. Lower validation loss signifies better learning and helps detect problems like overfitting.

Weights

Weights are numerical parameters in AI models that quantify the significance of input features during training. They adjust throughout training to enhance the model’s outputs, impacting its accuracy based on the dataset and task.

This glossary serves as a comprehensive resource for understanding the rapidly evolving world of AI terminology, ensuring you can navigate this landscape with confidence.

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Emmanuel Kesse

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