Using generative AI

Generative AI refers to a type of artificial intelligence that is capable of generating new data or content that's similar to the data it was trained on.

The training process involves consuming large amounts of text from books, articles, and websites, then analysing the text to find patterns and relationships in human language. Once it is trained, it can create new text based on an understanding of human language.

It can produce essays, blogs, scripts, news articles, reflective statements and even poetry. 

Some examples of generative AI that can create text content include: ChatGPT and Perplexity AI.

This type of AI learns through analysing datasets of images with captions or text descriptions. Therefore, if it knows what two different concepts are, such as a cat and roller skates, it can merge those concepts together when prompted to create an image of a cat wearing roller skates.

Generative AI image tools can produce diverse images in a range of mediums, everything from photorealistic oil painting style to anime.   

Some examples of generative AI that can create imagery include: Dall.E 2, Midjourney and Stable Diffusion.

AI music generators analyse music tracks and metadata (artist name, albums name, genre, year song was released, associated playlists) to identify patterns and features in particular music genres. They have also been trained on lyrics associated with songs. Keep in mind, if it has only been exposed to one type of music such as Mozart, then the music it generates will sound somewhat similar to his works.

Some examples of generative AI that can create audio content include: AIVA, Soundful and Murf.ai.
 

Learning to code is similar to learning a language. Commonly generative AI is exposed to large datasets of open access code in a variety of program languages (e.g. Python, Java, etc.). Through this exposure it can find common patterns, practices and structures within program languages. This leads to generative AI being used to write and improve code in a variety of ways, such as:

• create code from scratch
• predict the code you are creating
• review code you have created and find errors

Some examples of generative AI that can create code include: ChatGPT, CodeT5 and Tabnine.

Creating a video typically requires the use of audio, visual and text elements. There are a variety of generative AI video programs, some have harvested existing videos to learn how to create new ones, others have sourced the three elements to creating video from audio, visual and text sources. There are even generative AI video programs that have been trained on how to use video editing software, so they are able to apply effects to a video that you have created, such as adding captions, transitions, animations, etc.

Some examples of generative AI that can create videos include: Gen-1 Runway and Invideo.

There are many generative AI tools that can automate parts of the research process and make long, complex texts easier to decipher. This type of AI often analyses research papers that users upload to extract key information or summarise a paper.

Some examples of generative AI that can support research discovery and generate explanations include: Elicit, Scite and Raxter.