Traditional AI systems are trained on large amounts of data to identify patterns, and they’re capable of performing specific tasks that can help people and organizations. But generative AI goes one step further by using complex systems and models to generate new, or novel, outputs in the form of an image, text, or audio based on natural language prompts. 

Large language Models (ChatGPT, Bing AI, Bard/Gemini, BLOOM, etc.) have been trained on massive quantities, terabytes, of text and code repositories. By learning associations between words in that training data, they can generate answers to natural language prompts and questions.

Adapted from GitHub

What is ChatGPT Doing...and Why Does it Work? By Stephen Wolfram, a clear and simple explanation from an expert.

The number of generative AI-tools is growing constantly. There's an AI for that is an aggregator that categorizes AI-tools to make them easier to find. Specific examples are linked on guide with brief descriptions.

This technology is still in its early days and there are important issues that users should know. 

• Generative AI produces misinformation, and it can sometimes be hard to detect. As OpenAI describes this limitation, "ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers."  This is known as "hallucinating" or confabulating. Generative AI often cannot detect its own errors. Use the techniques on the Factchecking AI page of this guide to verify.
• There are bias, privacy, accountability, and transparency concerns, regarding training dataset, user information data, and more. This is a topic that is constantly and rapidly changing, with international regulators, ethicists, lawmakers, and many others creating guidelines, rules, and laws.  
• Your input becomes part of the available training data available to the company who owns the AI-tool you use. Be aware that you are providing free labor (and this is not the only way that happens!).  
• There are pending legal actions regarding the IP (intellectual property) rights of the data used to train various AI-tools.
• There are substantial environmental impacts- the enormous computing power required by generative AI models generates huge carbon emissions and consumes vast quantities of fresh water for cooling servers. However, It is important to recall that this merely the end of a long process that starts with energy-intensive mining and manufacturing, creating toxic pollution, producing tons of carbon emissions, and relying heavily on inhuman employment practices. 
• There is value in consciously developing and applying ethics to matters related to AI, and following the topic as ethicists and other informed experts consider the multitude implications of AI tools as it relates to misinformation, bias, accountability, transparency, environmental impacts, and IP infringement.