Artificial Intelligence (AI) is transforming technology and business, but its jargon can be daunting. Here’s a concise guide to commonly used AI terms to help you navigate the basics:

What is AI?

• Artificial Intelligence (AI): The branch of computer science focused on creating systems that mimic human thinking. Often used as a buzzword, it encompasses tools like OpenAI’s GPT or Google’s Gemini.
• Generative AI: Technology that creates new content like text, images, or code (e.g., ChatGPT, image generators).
• Artificial General Intelligence (AGI): Hypothetical AI as smart—or smarter—than humans, with immense potential and risks.

How AI Works

• Machine Learning (ML): A subset of AI where systems learn from data to make predictions.
• Training: The process where AI learns patterns from vast datasets (e.g., text, images).
• Parameters: Variables learned during training, shaping AI’s decision-making.
• Inference: The process where AI generates results, like responding to a query.

Common AI Models

• Large Language Models (LLMs): AI trained to understand and generate human-like text (e.g., GPT-4).
• Diffusion Models: Used to generate images or videos by reversing noise.
• Foundation Models: Versatile models trained on massive data, serving multiple tasks (e.g., OpenAI’s GPT, Meta’s Llama).
• Frontier Models: Next-gen, experimental AI models with enhanced capabilities.

AI Challenges

• Hallucinations: When AI generates incorrect or nonsensical outputs due to data flaws.
• Bias: AI can inherit biases from its training data, leading to discriminatory results.
• Tokens: Chunks of text analyzed by AI to generate responses (affecting output detail).

Core Technologies

• Neural Networks: Architectures mimicking human brains to identify patterns and learn.
• Transformers: A neural network type powering many modern AI systems, enabling rapid and accurate text generation (e.g., ChatGPT’s foundation).
• Nvidia H100 Chips: High-performance GPUs widely used for training AI systems.

Emerging Concepts

• Natural Language Processing (NLP): Enables machines to understand and generate human language.
• Retrieval-Augmented Generation (RAG): Allows AI to access external sources for improved accuracy.

By grasping these fundamentals, you’ll better understand the innovations and limitations shaping AI today.