A two-part reflection on knowledge generation and our technological future.
Before getting into today’s essay, a brief note about some events I will be doing related to the launch of my new book, Why Health? What We Need to Think About When We Think About Health. On September 24, I will join Jessica Steier, of Unbiased Science, for a Substack live conversation about the book. On October 5, I will join Katelyn Jetelina, of Your Local Epidemiologist, for a conversation in Washington, DC, at Busboys and Poets. On October 22, I will be at The Novel Neighbor in St. Louis, MO, for a reading and Q and A. I look forward to these events, to continuing the conversations that shaped the ideas in Why Health?
Now, on to today’s topic.
This is the second part of a two-part series on AI and the future of knowledge generation. Last week’s essay addressed the intersection of AI and universities, touching on how higher education can survive and thrive in the AI era. Today, I would like to move from what AI means for knowledge generation at the institutional level to what it means for individuals. How should those of us who spend our careers in the idea space approach AI in our own lives and work? What does a healthy engagement with AI look like? What standards and best practices should facilitate this engagement in academia and beyond? These are difficult questions, not to be answered lightly, but rather through a process of conversation, of trial and error, of a collective working out of how to best navigate this technological moment. In the spirit of contributing to this conversation, some thoughts on thinking and writing in the AI era, and what this new technology may mean for those who work in the academic space.
Over the last few years, AI has increasingly dominated the conversation about writing and ideas (as well as conversations about much else). Within academia, AI is the elephant in many rooms, raising questions about what this new technology means for the fundamentals of how we do what we do. Questions like: what does AI mean for the practice of research, peer review, and other core academic tasks? Should AI be used exclusively for technical work, or does it have a role to play in the actual generation of ideas? Perhaps at the more existential level, should we be afraid AI might replace us—replace the individuals and institutions whose job is to think, teach, and engage in the idea space? Is AI a tool, even a partner, to be welcomed, or a rival to be warily regarded?
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