The artificial intelligence industry continues to develop its own specialized vocabulary, with new terms emerging regularly to describe evolving technologies and techniques. Understanding this language has become essential for anyone working with, investing in, or following developments in the field.
One of the most discussed concepts is artificial general intelligence, or AGI, though experts disagree on its precise definition. Sam Altman has described AGI as the equivalent of a median human that you could hire as a co-worker. OpenAI’s charter defines it more formally as highly autonomous systems that outperform humans at most economically valuable work. Google DeepMind takes a slightly different view, considering AGI as AI that’s at least as capable as humans at most cognitive tasks.
AI agents represent another important category of technology. These tools can perform multi-step tasks autonomously, going beyond basic chatbot capabilities to handle activities like filing expenses, booking tickets, and writing code. The infrastructure supporting these systems continues to develop as the technology matures.
OpenAI’s new Astra model introduced the term “opaque recurrence” on September 2nd, describing a reasoning technique that has drawn attention from AI safety researchers. This follows the broader concept of chain-of-thought reasoning, which breaks down problems into smaller intermediate steps to improve the quality of AI outputs. Much like humans might use pen and paper to solve a problem involving 40 heads and 120 legs belonging to chickens and cows, AI systems can work through intermediate steps to reach correct answers of 20 chickens and 20 cows.
Coding agents represent a specialized application of AI agent technology focused on software development. These systems can write, test, and debug code autonomously, handling iterative work that typically consumes developer time with minimal human oversight.
The term “compute” refers to computational power that allows AI models to operate, including the hardware infrastructure of GPUs, CPUs, and TPUs that form the foundation of the modern AI industry. This processing power is essential for both training and deploying AI systems.
Deep learning represents a subset of machine learning that uses multi-layered artificial neural networks. These systems can identify important characteristics in data independently, learning from errors and improving their outputs through repetition and adjustment. However, deep learning systems typically require millions of data points to yield good results, making development costs higher and training times longer compared to simpler machine learning algorithms.
As the AI field continues to evolve rapidly, understanding these fundamental terms becomes increasingly important for professionals across industries. The vocabulary serves as a foundation for discussing the capabilities, limitations, and implications of AI technologies as they reshape various sectors of the economy.