The only AI glossary you'll need this year | TechCrunch

Artificial intelligence (AI) is rapidly transforming various sectors, not only by enhancing capabilities but also by generating a new lexicon to describe its functionalities. In today's tech landscape, terms like LLMs, RAG, RLHF, and others are frequently used in discussions, product meetings, and panels. This evolving language can leave even seasoned professionals feeling overwhelmed. To address this challenge, a comprehensive glossary has been compiled, providing straightforward definitions of the most relevant AI terminology. This resource aims to assist individuals engaged in AI development, investment, or simply trying to stay informed through platforms like TechCrunch and related podcasts. The glossary is designed to be a dynamic document, reflecting the ongoing advancements in the AI field.

Understanding Key AI Terms

One of the central concepts in AI is Artificial General Intelligence (AGI), which refers to AI systems that possess capabilities surpassing the average human across a wide range of tasks. OpenAI's CEO, Sam Altman, characterizes AGI as akin to a median human worker that one could employ as a colleague. OpenAI defines AGI as highly autonomous systems excelling in most economically valuable tasks. Google DeepMind offers a slightly different perspective, viewing AGI as AI that matches human cognitive abilities in most tasks. The ambiguity surrounding AGI is acknowledged even by leading experts in AI research, indicating the complexity of this concept.

Another important term is the AI agent, which denotes a tool that utilizes AI technologies to execute various tasks on behalf of users. Unlike basic AI chatbots, AI agents can handle more complex responsibilities, such as managing expenses, booking reservations, or even writing and maintaining software code. The term can vary in meaning among different stakeholders, and the infrastructure necessary to support these agents is still under development. However, the fundamental idea is that an AI agent is an autonomous system capable of leveraging multiple AI technologies to perform multi-step tasks.

API endpoints serve as the interface through which software applications communicate with each other. They can be thought of as “buttons” that developers can use to create integrations, enabling one application to access data from another or allowing an AI agent to interact with third-party services without human intervention. As AI agents become more sophisticated, they are increasingly able to identify and utilize these endpoints independently, creating new opportunities for automation.

In the realm of AI, chain-of-thought reasoning is a method employed by large language models (LLMs) to break down complex problems into smaller, manageable steps. This approach enhances the accuracy of the final output, particularly in logical or coding scenarios. While this method may require more time to arrive at a solution, it significantly improves the likelihood of correctness. Chain-of-thought reasoning is a refinement of traditional LLMs, optimized through reinforcement learning techniques.

Coding agents represent a specialized subset of AI agents focused on software development tasks. These agents can autonomously write, test, and debug code, streamlining the iterative processes that often consume a developer's time. They can navigate entire codebases, identify bugs, run tests, and implement fixes with minimal human oversight, akin to a highly efficient intern.

Compute, a term often used in the AI industry, refers to the computational power necessary for AI models to function. This encompasses various types of hardware, such as GPUs, CPUs, and TPUs, which provide the processing capabilities essential for training and deploying AI models. The term is crucial in understanding the infrastructure that supports the AI industry.

Deep learning is a subset of machine learning that employs artificial neural networks (ANNs) to identify complex patterns in data. These models can learn from vast amounts of data without requiring explicit feature definitions from human engineers. However, deep learning systems necessitate extensive datasets and longer training times, which can elevate development costs.

Diffusion processes are fundamental to many generative AI models, including those that create art, music, and text. These systems work by progressively degrading data through noise until it becomes unrecognizable, then learning to reverse this process to reconstruct the original data. This concept draws inspiration from physical diffusion, which is irreversible in nature.

Distillation is a technique for transferring knowledge from a larger AI model (the teacher) to a smaller, more efficient model (the student). This process allows the student model to approximate the teacher's behavior while minimizing losses. Distillation is a common practice among AI developers, enabling them to create models that are both effective and efficient.

Fine-tuning is the practice of further training an AI model to enhance its performance on specific tasks or domains. This is often achieved by incorporating specialized data into the training process, allowing AI startups to adapt large language models for particular applications.

Generative Adversarial Networks (GANs) are a machine learning framework that has significantly advanced generative AI. GANs consist of two neural networks that compete against each other, with one generating data and the other evaluating it. This competition allows for the production of highly realistic outputs without the need for human intervention.

Hallucinations in AI refer to instances where models generate incorrect or fabricated information. This phenomenon can lead to misleading outputs and poses risks, particularly in sensitive areas like healthcare. Addressing hallucinations is critical for improving AI quality and reliability.

Inference is the process through which an AI model makes predictions based on its training data. This step is essential for the model to apply learned patterns to new data. Different hardware can perform inference, but the efficiency and speed can vary significantly based on the capabilities of the hardware used.

Large language models (LLMs) are the backbone of popular AI assistants, such as ChatGPT and others. These models are built from billions of parameters that learn the relationships between words and phrases, enabling them to generate coherent and contextually relevant responses.

Memory cache is an optimization technique that enhances inference efficiency by storing previous calculations for future use. This reduces the computational load and speeds up response times, making AI interactions more seamless.

The Model Context Protocol (MCP) is an open standard that facilitates connections between AI models and external tools or data sources, streamlining integrations without the need for custom development. This protocol has gained rapid adoption among major AI companies, marking a significant advancement in AI interoperability.

Why it matters

The emergence of a standardized glossary of AI terms is crucial for creators and technologists navigating the increasingly complex landscape of artificial intelligence. As AI continues to evolve, understanding the terminology becomes essential for effective communication, collaboration, and innovation within the industry. For creators, a clear grasp of these concepts can enhance their ability to leverage AI tools effectively, while technologists can benefit from a shared language that fosters collaboration across disciplines. This glossary not only serves as a reference but also as a foundation for ongoing learning and adaptation in a field that is constantly changing. As AI becomes more integrated into various sectors, the importance of a common understanding of its terminology will only grow, paving the way for more informed discussions and advancements in technology.