Generative AI can learn from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it. It can produce a variety of novel content, such as images, video, music, speech, text, software code and product designs.
Generative AI uses a number of techniques that continue to evolve. Foremost are AI foundation models, which are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms.
Today, generative AI most commonly creates content in response to natural language requests — it doesn’t require knowledge of or entering code — but the enterprise use cases are numerous and include innovations in drug and chip design and material science development. (Also see “What are some practical uses of generative AI?”)
For more information from Gartner (an American technological research and consulting firm), please visit:
From students to professionals, this resource is designed to empower every reader with a solid and clear understanding of the critical concepts that drive generative AI.
From exploring foundational terms such as neural networks and deep learning to understanding the nuances of GANs, this glossary has been curated to offer a structured and comprehensive pathway to becoming proficient in the language of generative AI.
Use this easy-to-understand glossary to confidently navigate the dynamic world of generative AI. Think of each term as a tool, helping you build a stronger understanding and fostering meaningful discussions in this fast-paced field. Discover and take command with the unmatched guidance provided by this resource, ready at your fingertips.
https://www.aiprm.com/resources/guides/generative-ai-glossary/
Thanks to the Fuller Library Girl's STEM Club for highlighting this resource!
ChatGPT, created by the research company OpenAI, is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with the chatbot. The language model can answer questions and assist you with tasks, such as composing emails, essays, and code. The company launched ChatGPT on November 30, 2022.
OpenAI is also responsible for creating DALL-E 2, a popular AI art generator, and Whisper, an automatic speech recognition system.
Ortiz, S. (2023, August 14). What is ChatGPT and why does it matter? Here's what you need to know. ZDNET. https://www.zdnet.com/article/what-is-chatgpt-and-why-does-it-matter-heres-everything-you-need-to-know/
Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs) is a quick-start guide to help people use and launch LLMs like GPT, T5, and BERT at scale. It shows a step-by-step approach to building and deploying LLMs, with real-world case studies to illustrate the concepts. The video covers topics such as building recommendation engines with siamese BERT architectures, launching an information retrieval system with OpenAI embeddings and GPT3, and building an image captioning system with the vision transformer and GPT-J. This guide provides clear instructions and best practices for using LLMs. It fills a gap in the market by providing a guide to using LLMs and will be a valuable resource for anyone looking to use LLMs in their projects.
OpenAI ChatGPT is a powerful language model that uses advanced ML techniques to generate human-like text responses. With a vast knowledge base and the ability to understand and respond to a wide range of topics, ChatGPT provides detailed and insightful answers, making it a valuable tool for applications such as customer support, content creation, and virtual assistance. Its NLP capabilities and conversational skills make it versatile for text-based interactions.
The course begins with an introduction to OpenAI ChatGPT and explains its technology and features/use cases. We will set up an OpenAI account. We will explore how to write a speech and create a PowerPoint presentation and resume. We will write computer programs and debug using ChatGPT. We will build a machine learning model using ChatGPT and “word cloud” using Streamlit.
We will create a blog post summary, hashtags, and suggest titles for YouTube videos. We will examine VLookUp in Excel and analyze the net promoter score (NPS). We will explore 10 cool applications of ChatGPT. We will explore DALL-E and Whisper AI and look at other alternatives to ChatGPT.
We will independently use ChatGPT to create summaries, speeches, lesson plans, and resumes upon completion. We will develop programs, debug them with ChatGPT, and use other OpenAI platforms such as Whisper and DALL-E.