"Abstract: ChatGPT-4 can generate ideas much faster and cheaper than students, and the ideas are on average of higher quality (as measured by purchase-intent surveys) and exhibit higher variance in quality. More important, the vast majority of the best ideas in the pooled sample are generated by ChatGPT and not by the students. Providing ChatGPT with a few examples of highly rated ideas further increases its performance. We discuss the implications of these findings for the management of innovation.
Introduction: Generative artificial intelligence has made remarkable advances in creating life-like images and coherent, fluent text. OpenAI’s ChatGPT chatbot, based on the GPT series of large language models (LLM) can equal or surpass human performance in academic examinations and tests for professional certifications (OpenAI, 2023).
Despite their remarkable performance, LLMs sometimes produce text that is semantically or syntactically plausible but is, in fact, factually incorrect or nonsensical (i.e., hallucinations). The models are optimized to generate the most statistically likely sequences of words with an injection of randomness. They are not designed to exercise any judgment on the veracity or feasibility of the output.
In what applications can we leverage artificial intelligence that is brilliant in many ways yet cannot be trusted to produce reliably accurate results? One possibility is to turn their weaknesses – hallucinations and inconsistent quality – into a strength (Terwiesch, 2023)."
I found this very useful. Please keep doing this sort of commentary.
Thank you, Leslie. Will do.
"Abstract: ChatGPT-4 can generate ideas much faster and cheaper than students, and the ideas are on average of higher quality (as measured by purchase-intent surveys) and exhibit higher variance in quality. More important, the vast majority of the best ideas in the pooled sample are generated by ChatGPT and not by the students. Providing ChatGPT with a few examples of highly rated ideas further increases its performance. We discuss the implications of these findings for the management of innovation.
Introduction: Generative artificial intelligence has made remarkable advances in creating life-like images and coherent, fluent text. OpenAI’s ChatGPT chatbot, based on the GPT series of large language models (LLM) can equal or surpass human performance in academic examinations and tests for professional certifications (OpenAI, 2023).
Despite their remarkable performance, LLMs sometimes produce text that is semantically or syntactically plausible but is, in fact, factually incorrect or nonsensical (i.e., hallucinations). The models are optimized to generate the most statistically likely sequences of words with an injection of randomness. They are not designed to exercise any judgment on the veracity or feasibility of the output.
In what applications can we leverage artificial intelligence that is brilliant in many ways yet cannot be trusted to produce reliably accurate results? One possibility is to turn their weaknesses – hallucinations and inconsistent quality – into a strength (Terwiesch, 2023)."
https://mackinstitute.wharton.upenn.edu/wp-content/uploads/2023/08/LLM-Ideas-Working-Paper.pdf
Good link. Thank you.
Er, what are "purchase-intent surveys"?
https://www.surveymonkey.com/market-research/resources/what-is-purchase-intent/
Aha! Thank you, Sifu.