Perspectives & discussion material on AI in learning/teaching

This post is the second in the Beyond ChatGPT series about generative AI’s impact on learning.

Recently, there has been considerable interest in large language models: machine learning systems which produce human-like text and dialogue. Applications of these systems have been plagued by persistent inaccuracies in their output; these are often called “AI hallucinations”. We argue that these falsehoods, and the overall activity of large language models, is better understood as bullshit in the sense explored by Frankfurt (On Bullshit, Princeton, 2005): the models are in an important way indifferent to the truth of their outputs. We distinguish two ways in which the models can be said to be bullshitters, and argue that they clearly meet at least one of these definitions. We further argue that describing AI misrepresentations as bullshit is both a more useful and more accurate way of predicting and discussing the behaviour of these systems.

Visit the post for more.

Torrey Trust facilitates the MYFest session held on 27 June 2024 with the full title: Thinking Machines? Or Lying, Cheating, and Stealing Machines? This inte...