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

The field of artificial intelligence is rapidly evolving, introducing a new lexicon of terms that can be daunting even for tech-savvy individuals. This glossary aims to demystify some of the most frequently encountered AI concepts, providing clear and concise explanations. Understanding these terms is crucial for anyone looking to grasp the current state and future direction of AI. Artificial General Intelligence (AGI), for instance, refers to AI that can perform a wide range of human-level tasks, though its precise definition remains a subject of debate among experts. Some define it as AI capable of outperforming humans in most economically valuable work, while others view it as being at least as capable as humans in most cognitive tasks. AI agents are tools that utilize AI to perform complex, multi-step tasks autonomously. Unlike basic chatbots, these agents can handle activities like expense filing, booking appointments, or even writing and maintaining code. Their capabilities are continually expanding as the infrastructure to support them develops, allowing for sophisticated automation across various domains. API endpoints act as digital "buttons" that allow different software programs to communicate and interact. Developers use these interfaces to build integrations, enabling applications to exchange data or allowing AI agents to control third-party services directly. As AI agents become more advanced, they are increasingly able to discover and utilize these endpoints independently, leading to powerful and often unexpected automation possibilities. Chain-of-thought reasoning is an AI technique where large language models break down complex problems into smaller, sequential steps to arrive at a more accurate solution. This method, inspired by human problem-solving, improves the reliability of AI outputs, particularly in logical or coding contexts, despite potentially requiring more processing time. Coding agents are specialized AI agents focused on software development. Beyond merely suggesting code, they can autonomously write, test, and debug code. These agents streamline the development process by handling iterative tasks, working across entire codebases to identify bugs, run tests, and implement fixes with minimal human intervention. Compute refers to the computational power essential for training and deploying AI models. This power, often provided by hardware like GPUs, CPUs, and TPUs, forms the backbone of the AI industry. Deep learning, a subset of machine learning, employs multi-layered artificial neural networks. This structure allows deep learning algorithms to identify more complex patterns and correlations compared to simpler machine learning models, taking inspiration from the intricate connections of neurons in the human brain.