I was motivated to write this story for two reasons.
First, I think that there is a lack of clear visual metaphors, stories, or other easy accessible analogies for concepts in AI and its impacts on society. I am often speaking with intelligent non-technical people—including potential users or “micro-regulators” (e.g., organisational policymaker) of AI tools—who have read about AI in the news but don’t have good handles on how to think about these tools and how they interact with existing organisational processes or social understandings.
Second, this specific story was motivated by a discussion with a highly qualified non-technical user of LLMs who expressed skepticism about the capabilities of LLMs (in this case, chatGPT 3.5) because when they prompted the LLM to provide citations for a topic area that the user was an expert in, the research citations provided in the LLM response were wrong or misleading / hallucinations.
One insight that came from our follow-up conversation was that the user were imagining that writing prompts for an LLM to be similar to writing a Google search query. In their understanding, they were requesting a pre-existing record that was stored in the LLM’s database, and so for the LLM to respond with an incorrect list of records indicated that the LLM was fundamentally incapable of a ‘basic’ research task.
I was motivated to write this story for two reasons.
First, I think that there is a lack of clear visual metaphors, stories, or other easy accessible analogies for concepts in AI and its impacts on society. I am often speaking with intelligent non-technical people—including potential users or “micro-regulators” (e.g., organisational policymaker) of AI tools—who have read about AI in the news but don’t have good handles on how to think about these tools and how they interact with existing organisational processes or social understandings.
Second, this specific story was motivated by a discussion with a highly qualified non-technical user of LLMs who expressed skepticism about the capabilities of LLMs (in this case, chatGPT 3.5) because when they prompted the LLM to provide citations for a topic area that the user was an expert in, the research citations provided in the LLM response were wrong or misleading / hallucinations.
One insight that came from our follow-up conversation was that the user were imagining that writing prompts for an LLM to be similar to writing a Google search query. In their understanding, they were requesting a pre-existing record that was stored in the LLM’s database, and so for the LLM to respond with an incorrect list of records indicated that the LLM was fundamentally incapable of a ‘basic’ research task.