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Aral Balkan (aral@mastodon.ar.al)'s status on Monday, 17-Jun-2024 16:10:27 JST Aral Balkan

Hey boy, you’re so full of shit, are you sure you’re not a large language model?

In conversation about 5 months ago from mastodon.ar.al permalink
- Aral Balkan (aral@mastodon.ar.al)'s status on Monday, 17-Jun-2024 16:13:34 JST Aral Balkan
  in reply to
  - witix
  @witix [winks in awkward]
  
  In conversation about 5 months ago permalink
- witix@transfem.social's status on Monday, 17-Jun-2024 16:13:36 JST witix
  in reply to
  
  @aral@mastodon.ar.al Aral please don't do pickup lines :neocat_googly_shocked:
  
  In conversation about 5 months ago permalink
- Aral Balkan (aral@mastodon.ar.al)'s status on Monday, 17-Jun-2024 16:18:06 JST Aral Balkan
  in reply to
  - Maarten Sneep
  @mrtnsnp Yeah, loved it :) Good to see not all academics are suckling at the corporate teet.
  
  In conversation about 5 months ago permalink
- Maarten Sneep (mrtnsnp@mastodon.social)'s status on Monday, 17-Jun-2024 16:18:07 JST Maarten Sneep
  in reply to
  
  @aral I’m sure you’ve seen that there is a peer-reviewed article that argues that “ChatGPT is bullshit”. https://link.springer.com/article/10.1007/s10676-024-09775-5
  In conversation about 5 months ago permalink
  Attachments
  1. ChatGPT is bullshit - Ethics and Information Technology
    
    from Slater, Joe
    
    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.

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