Artificial intelligence is revolutionizing numerous aspects of our lives, from how we interact with devices to how businesses operate. However, the field of AI is also inventing a new language, with terms like LLMs, RAG, RLHF, and many more that can leave even the most tech-savvy individuals feeling bewildered. To bridge this knowledge gap, a comprehensive glossary has been compiled to explain these terms in an accessible way.
At the forefront of AI research is the concept of Artificial General Intelligence (AGI), which refers to AI systems that surpass human capabilities in most tasks. OpenAI CEO Sam Altman describes AGI as akin to a median human coworker, while Google DeepMind views it as AI that matches human cognitive abilities. Despite these definitions, experts acknowledge the complexity and nuance of AGI.
Another crucial term is the AI agent, which denotes a tool that utilizes AI technologies to perform tasks autonomously. These agents can file expenses, book tickets, or even write and maintain code, operating beyond the capabilities of basic AI chatbots. The concept of an AI agent is still evolving, with infrastructure being developed to support its envisioned capabilities.
Deep learning is a subset of machine learning that involves artificial neural networks (ANNs) to make complex correlations. Inspired by the human brain's interconnected pathways, deep learning algorithms can identify important data features without human intervention. However, these systems require vast amounts of data and longer training times, resulting in higher development costs.
The glossary also covers terms like diffusion, distillation, fine-tuning, and reinforcement learning, which are essential for understanding the latest advancements in AI. Diffusion refers to the technology behind art-, music-, and text-generating AI models, while distillation is a technique used to extract knowledge from large AI models. Fine-tuning involves optimizing AI models for specific tasks, and reinforcement learning enables AI systems to learn through trial and error.
In addition to these terms, the glossary explains concepts like large language models (LLMs), neural networks, and open source. LLMs are the AI models used by popular AI assistants, such as ChatGPT and Google's Gemini. Neural networks are the algorithmic structures that underpin deep learning, and open source refers to software or AI models with publicly available code.
The guide also touches on the challenges facing the AI industry, including the shortage of random access memory (RAM) chips, which is driving up costs and affecting various sectors. Moreover, it highlights the importance of techniques like parallelization, which enables AI systems to perform multiple calculations simultaneously, and tokenization, which breaks down text into smaller units for AI processing.
By providing a comprehensive explanation of these terms and concepts, the glossary aims to make AI more accessible and understandable for everyone. As the field continues to evolve, this guide will be updated regularly to reflect the latest developments and advancements in AI.
This article was written with the assistance of AI.
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