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Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial | Clinical Decision Support | JAMA Network Open | JAMA Network
An unexpected secondary result was that the LLM alone performed significantly better than both groups of humans, similar to a recent study with different LLM technology.31 This may be explained by the sensitivity of LLM output to prompt formulation.32 There are numerous frameworks for prompting LLMs and an emerging consensus on prompting strategies, many of which focus on providing details on the task, context, and instructions; our prompt was iteratively developed using these frameworks. Training clinicians in best prompting practices may improve physician performance with LLMs. Alternatively, organizations could invest in predefined prompting for diagnostic decision support integrated into clinical workflows and documentation, enabling synergy between the tools and clinicians. Prior studies on AI systems show disparate effects depending on the component of the diagnostic process they are used in.33,34 Given the conversational nature of chatbots, changes in how the LLM interacts with humans, for example by specifically pointing out features that do not fit the differential diagnosis, might improve diagnostic and reflective performance.35,36 More generally, we see opportunity with deliberate consideration and redesign of medical education and practice frameworks that adapt to disruptive emerging technologies and enable the best use of computer and human resources to deliver optimal medical care.
Why A.I. Isn’t Going to Make Art | The New Yorker
I’m not saying that art has to involve tedium. What I’m saying is that art requires making choices at every scale; the countless small-scale choices made during implementation are just as important to the final product as the few large-scale choices made during the conception. It is a mistake to equate “large-scale” with “important” when it comes to the choices made when creating art; the interrelationship between the large scale and the small scale is where the artistry lies.
Believing that inspiration outweighs everything else is, I suspect, a sign that someone is unfamiliar with the medium.
Why A.I. Isn’t Going to Make Art | The New Yorker
Art is notoriously hard to define, and so are the differences between good art and bad art. But let me offer a generalization: art is something that results from making a lot of choices. This might be easiest to explain if we use fiction writing as an example. When you are writing fiction, you are—consciously or unconsciously—making a choice about almost every word you type; to oversimplify, we can imagine that a ten-thousand-word short story requires something on the order of ten thousand choices. When you give a generative-A.I. program a prompt, you are making very few choices; if you supply a hundred-word prompt, you have made on the order of a hundred choices.
ChatGPT is bullshit | Ethics and Information Technology
In this paper, we argue against the view that when ChatGPT and the like produce false claims they are lying or even hallucinating, and in favour of the position that the activity they are engaged in is bullshitting, in the Frankfurtian sense (Frankfurt, 2002, 2005). Because these programs cannot themselves be concerned with truth, and because they are designed to produce text that looks truth-apt without any actual concern for truth, it seems appropriate to call their outputs bullshit.
