Detectors such as GPTZero use the property of ‘perplexity’ to try to detect AI authorship of texts. But I show that by writing in a specifically dull style, or engineering the prompt given to a language model, we can easily and systematically fool such detectors to label AI text as human and vice versa.
A recent study by Gao et al. (2022) validates the warning of ‘Machine Evidence’ (Blunt, 2019) that language models would soon become capable of beating detection attempts by human peer reviewers. This piece looks at the near-term steps that journal editors and conference organisers can take to prevent AI-generated abstracts bypassing their screening processes, along with a warning for the long-term viability of those strategies.
Can a new iteration of GPT-3 write pop songs, raps and limericks…about Immanuel Kant’s Categorical Imperative? It is a moral duty to find out.
Can DALL·E 2 create images of ancient philosophers like Plato, Aristotle and Immanuel Kant as they’d look in modern day universities? Sort of. Should it? Definitely not.
Would you choose a black box AI surgeon with a 90% success rate over a human surgeon with 80% success? The answer exposes a fundamental and harmful assumption within dominant models of medical evidence.
DALLE 2 offers a far more powerful image generation AI than the popular open access ‘Craiyon’/’DALLE Mini’ model. How does DALLE 2 compare to DALLE Mini’s visions of hierarchies and pyramids of evidence?
How does a machine learning algorithm picture hierarchies of evidence and evidence-based medicine – and what do these visions of evidence remind us of the way we understand, order and assemble the information we use to guide clinical practice?
AI21 Labs have just released a public demo of their giant language model, Jurassic-1. At 178bn parameters, it rivals GPT-3. Feeding it my own work, it generated some interesting and potentially novel views on evidence hierarchies… and then attributed them to CAM researcher Marc Micozzi! Is Jurassic Micozzi’s critique of evidential pluralism in medicine sound?
In their 2020 paper, Floridi and Chiriatti subject giant language model GPT-3 to three tests: mathematical, semantic and ethical. I show that these tests are misconfigured to prove the points Floridi and Chiriatti are trying to make. We should attend to how such giant language models function to understand both their responses to questions and the ethical and societal impacts.
What does Google have in common with Barbra Streisand? Since Google fired AI ethicists Margaret Mitchell and Timnit Gebru, our attention should turn to what they don’t want us to read: “On Stochastic Parrots”. Will the attempts to suppress this paper lead to it being overlooked, or will Google face Barbra Streisand’s fate?
