Tim Requarth is director of graduate science writing and research assistant professor of neuroscience at the NYU Grossman School of Medicine, where he studies how artificial intelligence is changing the way scientists think, learn and write. He writes “The Third Hemisphere,” a newsletter that explores AI’s effects on cognition from a neuroscientist’s perspective.His essays and reporting have appeared in The New York Times, The Atlantic, and Slate, where he is a contributing writer.
Tim Requarth
Director of graduate science writing, Research Assistant Professor of Neuroscience and Physiology
NYU Grossman School of Medicine
From this contributor
Betting blind on AI and the scientific mind
If the struggle to articulate an idea is part of how you come to understand it, then tools that bypass that struggle might degrade your capacity for the kind of thinking that matters most for actual discovery.
Betting blind on AI and the scientific mind
From bench to bot: Why AI-powered writing may not deliver on its promise
Efficiency isn’t everything. The cognitive work of struggling with prose may be a crucial part of what drives scientific progress.
From bench to bot: Why AI-powered writing may not deliver on its promise
Many students want to learn to use artificial intelligence responsibly. But their professors are struggling to meet that need.
Effectively teaching students how to employ AI in their writing assignments requires clear guidelines—and detailed, case-specific examples.
Keeping it personal: How to preserve your voice when using AI
To harness the workmanlike prose of artificial intelligence while maintaining a recognizable style, use it as an analyzer rather than as a writer.
Keeping it personal: How to preserve your voice when using AI
From bench to bot: How important is prompt engineering?
To draft the most effective prompt, assume the stance of teacher.
From bench to bot: How important is prompt engineering?
Explore more from The Transmitter
Error equation predicts brain’s ability to generalize
Four statistical measurements of neural network geometry capture how well brains and artificial networks use what they already know to solve new problems, a study suggests.
Error equation predicts brain’s ability to generalize
Four statistical measurements of neural network geometry capture how well brains and artificial networks use what they already know to solve new problems, a study suggests.
Embrace complexity to improve the translatability of basic neuroscience
Researchers must learn to view heterogeneity as an essential feature of the systems they study and a central consideration in experimental design, not a variable to control for or reduce.
Embrace complexity to improve the translatability of basic neuroscience
Researchers must learn to view heterogeneity as an essential feature of the systems they study and a central consideration in experimental design, not a variable to control for or reduce.
Romain Brette reveals fundamental flaws in commonly assumed neuroscience concepts
His new book, “The Brain, In Theory,” offers alternatives to many of the computer science frameworks currently driving theoretical neuroscience.
Romain Brette reveals fundamental flaws in commonly assumed neuroscience concepts
His new book, “The Brain, In Theory,” offers alternatives to many of the computer science frameworks currently driving theoretical neuroscience.