no code implementations • 30 Jan 2024 • David Almog, Romain Gauriot, Lionel Page, Daniel Martin
We structurally estimate the psychological costs of being overruled by AI using a model of rational inattentive umpires, and our results suggest that because of these costs, umpires cared twice as much about Type II errors under AI oversight.
no code implementations • 19 Nov 2023 • Nir Chemaya, Daniel Martin
The emergent abilities of Large Language Models (LLMs), which power tools like ChatGPT and Bard, have produced both excitement and worry about how AI will impact academic writing.
no code implementations • 24 Sep 2022 • Letian Chen, Sravan Jayanthi, Rohan Paleja, Daniel Martin, Viacheslav Zakharov, Matthew Gombolay
Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics.
no code implementations • 10 May 2022 • Andrew Caplin, Daniel Martin, Philip Marx
A much studied issue is the extent to which the confidence scores provided by machine learning algorithms are calibrated to ground truth probabilities.
no code implementations • 20 Apr 2022 • Daniel Martin, Diego Gutierrez, Belen Masia
Human visual attention is a complex phenomenon that has been studied for decades.
no code implementations • 25 Mar 2021 • Daniel Martin, Ana Serrano, Alexander W. Bergman, Gordon Wetzstein, Belen Masia
Generative adversarial approaches could alleviate this challenge by generating a large number of possible scanpaths for unseen images.
no code implementations • 1 Feb 2021 • Robin Hanson, Daniel Martin, Calvin Mccarter, Jonathan Paulson
We fit this three-parameter model of loud aliens to data: 1) birth power from the number of hard steps seen in Earth history, 2) birth constant by assuming a inform distribution over our rank among loud alien birth dates, and 3) expansion speed from our not seeing alien volumes in our sky.