Artificial intelligence, or AI for short, involves using computers to do tasks which mimic human intelligence in some way. There are a vast range of methods of creating artificial intelligence and applications for AI.

One area in which AI has been used is for generating content that is similar to other content, sometimes known as generative AI. This is often done using a branch of artificial intelligence known as machine learning, which focuses on getting computers to appear to learn how to do something, like recognise or generate certain kinds of content. Machine learning can be supervised, where it is given datasets and told to learn from them, or unsupervised, where the computer is given a goal and parameters and has to try and reach that goal without example data.

AI generation tools are applications, usually web-based, which allow you to harness the power of this AI generation without needing to know how to create AI or machine learning apps yourself. These tools can do a range of things (you've probably seen some of them in action, especially text generation tools like ChatGPT or Microsoft Copilot), but also come with many caveats and restrictions. Typically, you give these tools some kind of prompt, maybe using text and/or images, and it 'generates' content in return.

One important thing to be aware of with AI generation tools is that they are all based on datasets. The data that the AI tool has been 'trained' on impacts what results you will get and what it can do. For example, generating images requires a large dataset of existing images and the AI will create images based on what it learns from these images, so certains styles or types of image in the dataset will mean you get particular results. The data that these tools are trained on might be copyrighted or someone's intellectual property (IP), which introduces other issues about what people do with the outputs of generative AI tools.

As we'll explore further on this page, think about what you use generative AI tools for. There is University guidance for students and postgraduate researchers around the use of generative AI in assessment and other work.

Tools powered by artificial intelligence are everywhere, and they are not just generative AI - for example, everyday applications like Gmail use a range of AI techniques to do things like highlight important emails or suggest how you might want to reply to an email. There's a whole range of tasks that AI has been used to help with, so often you'll have to explore the tools out there to see what might be possible.

Generative AI tools are being used in a huge range of ways. Text generation, for example, is being used to search for information, to create outlines and plans, to proofread text, to explore ideas, and many other things. People are constantly finding new ways to use (and abuse) these tools, so we couldn't ever write an exhaustive list of how you can use any type of generative AI!

Be careful with the limitations of these tools. Always test what you want to do with the tool and the outputs you get before investing too much time in it. Lots of these tools are rapidly changing due to technological change, so keep an eye out for functionality changing. Also be aware of pricing models: many free tools have limitations like watermarks or limits on creation, as they want you to pay for the full version. Sometimes tools changing from being free to being paid for due to popularity, so make sure you download or export any creations once you've made them.

To explore more about what AI tools can do in terms of finding information and being used as a reference source, see our Searching for Information guide page on AI tools:

How artificial intelligence is developed, used, and viewed in society are big areas of discussion and debate. For example, within XR Stories at York there's work going on around challenging AI stereotypes around how we view AI and how it is represented, the kinds of images and sounds we use when we think of AI. The world of artificial intelligence has been changing a lot recently with developments making it possible to have better generative AI, so it can be useful to read around these topics and think more broadly about the societal impacts of AI as well as the technological ones. Lots of research is being done in these areas, including at the University of York.

There are many other ethical considerations in the world of AI. For example, some kinds of AI like machine learning can be based upon existing datasets, with the computer 'taught' from this existing data. What data is chosen as the training data is crucial: using datasets that contain inequality and bias will replicate those inequalities and biases in the AI tool. The data often contains copyrighted material or material that is someone's intellectual property, so they might not want people to be generating new work that is very similar to theirs (though copying work isn't something new to generative AI!).

When you use AI tools, it is good to be critical of artificial intelligence at the same time, and even how we view the tools themselves. Do we see them as 'magic' applications that can create something out of nothing, or complex code that has been designed and written by humans making choices? Does this make a difference to how we use the outputs of these tools? The 'people' side of generative AI is important: not just the people who create the tools, and the people whose work might have been used when the AI tool was trained, but also people whose work is more hidden, like those who label the huge datasets that generative AI is trained on. People run and work at the technology companies who build and sell these AI tools. Basically, there's a lot going on behind the scenes, and responsible use of AI means being aware of this!