Merge pull request #27 from noramcgregor/main

Update ml-ai.qmd

book/ml-ai.qmd

@@ -24,6 +24,10 @@ Sometimes folks may speak of or refer to AI as systems and machines that actuall
 
 You might also sometimes hear people talk about **Traditional AI** vs **Generative AI**. **Traditional AI** refers to using machine learning based systems for doing tasks like classifying data (e.g., assigning labels to images, automatically transcribing handwritten texts, or identifying genre of digitised texts). This is the type of AI we make a whole lot of use of in the library world. **Generative AI** on the other hand refers broadly to systems whose primary function is to generate new content (e.g., conversation, books, art). This is where conversation generating AI systems like ChatGPT (Generative Pre-trained Transformer) fall under for example and we’re only just now exploring the potential applications for these new powerful Generative AI systems in library work.
 
+Generative artificial intelligence (generative AI, GenAI,[1] or GAI) is artificial intelligence capable of generating text, images, videos, or other data using generative models,[2] often in response to prompts.[3][4] Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.[5][6]
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+Improvements in transformer-based deep neural networks, particularly large language models (LLMs), enabled an AI boom of generative AI systems in the early 2020s. These include chatbots such as ChatGPT, Copilot, Gemini and LLaMA, text-to-image artificial intelligence image generation systems such as Stable Diffusion, Midjourney and DALL-E, and text-to-video AI generators such as Sora.[7][8][9][10] Companies such as OpenAI, Anthropic, Microsoft, Google, and Baidu as well as numerous smaller firms have developed generative AI models.[3][11][12]
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 Whenever AI is being discussed you may often hear the term **Machine Learning (ML)** mentioned, and sometimes they’re used interchangeably which can be confusing! 
 
 **Machine Learning (ML)** is more specifically a main subfield of AI and core technology underpinning the other subfields of AI, that focuses on the development of algorithms and models that allow computers to learn patterns and relationships from data and make predictions on new data. Instead of being explicitly programmed for specific tasks, ML algorithms use data to learn and improve their performance over time.