How can AI-generated “Top 10” lists of cultural influencers expose the cultural biases and defaults that are built into our most popular language models? I propose and report an initial test using lists of the Top 10 most influential musicians.
I outline four common misconceptions about the use of Generative AI which are widespread in Higher Education debates about the use of these tools: that it is possible and practical to detect the use of AI in writing, that text produced by GenAI is bland, repetitive or predictable, that GenAI tools struggle to cite sources accurately, and that more creative or reflective assessments are harder to complete using AI.
As language models are fine-tuned to acquire more capabilities, we continue to seek new tasks to push the limits of Generative AI. In setting cryptic crossword clues, so far GPT-4 fails the test quite spectacularly.
What skills do social scientists need to adapt to generative AI, and how should educators approach teaching them? A narrow focus on training everyone in technical skills is misguided – what’s needed is authorial voice, leadership and management skills, and the critical force of the social sciences.
Can a language model outperform old Edward Lear in describing philosophers in Limerick form? “There once was a Scotsman named Hume…”
Will large language models acknowledge authorship of their own generated texts? Will a language model claim authorship or ownership of texts which it did not create? A mistaken comprehension of ChatGPT’s abilities throws up a distinctive problem of intellectual property rights.
What is the relationship between perplexity, creativity and novelty? Following on from ‘Perplexing Perplexity’, I set out to demonstrate that high perplexity texts are not always creative, and to showcase ChatGPT’s ability to work with and even generate novel words – culminating in the Tale of Zonkamoozle.
