- cross-posted to:
- technology@lemmit.online
- cross-posted to:
- technology@lemmit.online
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.
Problem is that it doesn’t automate away the bullshit in our lives. We’re creating even more bullshit that we’re forced to deal with online and at our jobs. Sure we can use the bullshit generator to respond to bullshit, but how do you know what’s bullshit in the first place, are you going to ask your bullshit generator to sort that out for you as well?