So you’ve heard these AI terms and nodded along; let’s fix that

The world of artificial intelligence is rapidly evolving, bringing with it a unique lexicon that can often leave even the most knowledgeable tech enthusiasts feeling perplexed. In just a few minutes of reading about AI, terms like LLMs, RAG, and RLHF can create a sense of confusion. This glossary aims to clarify these terms, serving as a dynamic resource that will be updated as the technology progresses. Artificial General Intelligence (AGI) is a concept that refers to AI systems that can outperform humans in various tasks. OpenAI's CEO Sam Altman describes it as comparable to hiring a median human worker, while OpenAI characterizes AGI as highly autonomous systems excelling in economically valuable tasks. Google DeepMind adds that AGI should be capable of most cognitive tasks at a human level. It’s important to note that even experts grapple with these definitions as the field continues to evolve. An AI agent is a more advanced tool utilizing AI technologies to tackle complex tasks on behalf of a user—far beyond the capabilities of simple chatbots. These agents can handle everything from managing expenses to coding, drawing upon various AI systems to execute multi-step processes autonomously. In the realm of software development, API endpoints function as 'buttons' that allow different programs to communicate and execute commands. Through these interfaces, developers can enable applications to share data or allow AI agents to manage external services without human input. As AI agents gain sophistication, they increasingly autonomously discover and utilize these endpoints, unlocking unexpected automation possibilities. Chain-of-thought reasoning in AI refers to breaking down complex problems into simpler, manageable steps, akin to solving a puzzle. This method enhances the accuracy of AI responses, particularly in logical or coding contexts, despite requiring more time to derive a solution. A coding agent represents a specialized version of AI focused on software development. Unlike traditional AI that merely suggests code, a coding agent can autonomously write, test, and debug code, functioning like a highly efficient intern that requires minimal oversight. Compute power is essential for AI models, referring to the hardware infrastructure—like GPUs and CPUs—that enables their operation. Deep learning, a subset of machine learning, employs a multi-layered neural network structure to recognize complex data patterns, requiring vast amounts of data for effective training. Diffusion technology is crucial in creating AI-generated content, such as art and music, by methodically adding noise to data until it loses its structure, then learning to reverse that process to recover the original data. Distillation is a technique in which knowledge is extracted from larger AI models to train smaller, more efficient ones. This method can accelerate the development of AI systems and is integral to creating models like OpenAI's GPT-4 Turbo. Additionally, the concept of hallucination in AI describes when models generate incorrect information, a significant issue that can lead to real-world consequences, such as providing harmful medical advice. Inference is the process of applying an AI model to draw conclusions from data it has previously learned, requiring extensive training beforehand. Hardware performance can significantly influence inference speed, with more powerful systems yielding quicker results. With terms like tokens and throughput becoming increasingly relevant, understanding their roles in AI communication and processing efficiency is vital for navigating the landscape of artificial intelligence. This glossary serves as a foundational resource to better grasp the evolving language of AI as technology continues to advance.