In addition to the resource pages above, below you will find a variety of supplemental materials to help you learn more about artificial intelligence and its applications in different domains. Feel free to browse through our collection of podcasts, research papers, blog posts, articles, links, and videos to deepen your knowledge and skills in AI. 

Generative AI is a branch of artificial intelligence (AI) that involves the use of machine learning algorithms to generate new data that is similar to a given input dataset. The goal of generative AI is to create artificial data that is similar to the real-world data that the algorithm was trained on.

Generative AI algorithms can be used in a variety of applications, including image and video generation, text and speech synthesis, and even music composition. These algorithms work by analyzing a large dataset of input data and then learning patterns and relationships within that data.

Once the algorithm has learned these patterns, it can then generate new data that is similar to the input data but not an exact copy. For example, a generative AI algorithm trained on a dataset of cat images could be used to generate new cat images that have never been seen before.

There are several types of generative AI algorithms, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Autoregressive Models. Each of these algorithms has its strengths and weaknesses and is better suited for certain applications than others.

Overall, generative AI has the potential to revolutionize many industries by enabling the creation of new and realistic artificial data (OpenAI, 2023).

--the above was generated by ChatGPT in response to the following prompt: "explain generative AI"

References

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat 

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