From LLMs to hallucinations, here’s a simple guide to common AI terms

The realm of artificial intelligence is intricate and often filled with specialized terminology. As researchers and developers navigate this complex landscape, they frequently employ specific jargon that can be confusing to outsiders. To aid understanding, we have compiled an evolving glossary that outlines some of the most significant terms used in the AI sector. This glossary will be regularly updated to incorporate new definitions as advancements in AI continue to emerge, along with the identification of safety challenges. One of the key concepts is Artificial General Intelligence (AGI), a term that generally denotes AI systems that can perform tasks at a level surpassing the average human. OpenAI's CEO, Sam Altman, likened AGI to a median human who could serve effectively as a colleague. In contrast, Google DeepMind characterizes AGI as AI capable of matching human cognitive abilities across a variety of tasks. This discrepancy in definitions illustrates the ongoing debate among experts about what AGI truly encompasses. Another significant term is the AI agent, which refers to a sophisticated tool that utilizes AI technology to handle various tasks autonomously. Unlike basic chatbots, AI agents can perform actions such as managing expenses, booking reservations, or even writing code. However, interpretations of what constitutes an AI agent vary, as the field is still developing its infrastructure and capabilities. Chain-of-thought reasoning is another important concept, particularly for large language models (LLMs). This method involves breaking down complex problems into manageable steps, which ultimately enhances the accuracy of outcomes. While this process may require more time, it often yields more reliable results, especially in logical reasoning or programming contexts. The term 'compute' refers to the computational power essential for AI model operations, underpinning the industry's ability to train and deploy advanced systems. It encompasses the hardware components, such as GPUs and CPUs, that are foundational to modern AI operations. Deep learning, a subset of machine learning, employs a layered artificial neural network (ANN) to discern complex patterns in data. Drawing inspiration from the human brain's interconnected neurons, these deep learning models can autonomously identify critical features in data, requiring substantial amounts of training data to produce effective outcomes. Diffusion processes, integral to many generative AI applications, involve systematically adding noise to data until its structure is obscured. The goal is to learn how to reverse this process, effectively restoring the data from its distorted state. Distillation is a technique utilized in AI to transfer knowledge from a larger, more complex model (the teacher) to a simpler, more efficient one (the student). This process allows for the creation of smaller models that retain much of the performance of their larger counterparts. The phenomenon of 'hallucination' in AI refers to instances where models generate incorrect or misleading information, posing risks in applications requiring high accuracy, such as healthcare. This issue arises from gaps in the training data and has led to a growing interest in developing more specialized AI systems to mitigate such risks. Inference, the operational phase of an AI model, involves using learned patterns to make predictions based on new data. This process requires a well-trained model and can be executed on various hardware, with performance varying significantly based on the system's capabilities. Finally, the terms 'tokens' and 'weights' are crucial in understanding AI communication and training. Tokens serve as the basic units of data that both humans and AI use to interact, while weights determine the significance of different features in training datasets, ultimately shaping the model's output. As the AI landscape continues to evolve, staying informed about these terms is essential for understanding the technology's current and future implications.