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pro vyhledávání: '"Tessler, Michael"'
Autor:
El-Sayed, Seliem, Akbulut, Canfer, McCroskery, Amanda, Keeling, Geoff, Kenton, Zachary, Jalan, Zaria, Marchal, Nahema, Manzini, Arianna, Shevlane, Toby, Vallor, Shannon, Susser, Daniel, Franklin, Matija, Bridgers, Sophie, Law, Harry, Rahtz, Matthew, Shanahan, Murray, Tessler, Michael Henry, Douillard, Arthur, Everitt, Tom, Brown, Sasha
Recent generative AI systems have demonstrated more advanced persuasive capabilities and are increasingly permeating areas of life where they can influence decision-making. Generative AI presents a new risk profile of persuasion due the opportunity f
Externí odkaz:
http://arxiv.org/abs/2404.15058
Autor:
Bakker, Michiel A., Chadwick, Martin J., Sheahan, Hannah R., Tessler, Michael Henry, Campbell-Gillingham, Lucy, Balaguer, Jan, McAleese, Nat, Glaese, Amelia, Aslanides, John, Botvinick, Matthew M., Summerfield, Christopher
Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static and homogeneous across individuals, so that aligning to a a single "g
Externí odkaz:
http://arxiv.org/abs/2211.15006
Although approximately 50% of medical school graduates today are women, female physicians tend to be underrepresented in senior positions, make less money than their male counterparts and receive fewer promotions. There is a growing body of literatur
Externí odkaz:
http://arxiv.org/abs/2206.00234
Autor:
Lampinen, Andrew K., Dasgupta, Ishita, Chan, Stephanie C. Y., Matthewson, Kory, Tessler, Michael Henry, Creswell, Antonia, McClelland, James L., Wang, Jane X., Hill, Felix
Language Models (LMs) can perform new tasks by adapting to a few in-context examples. For humans, explanations that connect examples to task principles can improve learning. We therefore investigate whether explanations of few-shot examples can help
Externí odkaz:
http://arxiv.org/abs/2204.02329
Autor:
Tessler, Michael Henry, Madeano, Jason, Tsividis, Pedro A., Harper, Brin, Goodman, Noah D., Tenenbaum, Joshua B.
Knowledge built culturally across generations allows humans to learn far more than an individual could glean from their own experience in a lifetime. Cultural knowledge in turn rests on language: language is the richest record of what previous genera
Externí odkaz:
http://arxiv.org/abs/2107.13377
Human reasoning can often be understood as an interplay between two systems: the intuitive and associative ("System 1") and the deliberative and logical ("System 2"). Neural sequence models -- which have been increasingly successful at performing com
Externí odkaz:
http://arxiv.org/abs/2107.02794
Autor:
Acquaviva, Samuel, Pu, Yewen, Kryven, Marta, Sechopoulos, Theodoros, Wong, Catherine, Ecanow, Gabrielle E, Nye, Maxwell, Tessler, Michael Henry, Tenenbaum, Joshua B.
The Abstraction and Reasoning Corpus (ARC) is a set of procedural tasks that tests an agent's ability to flexibly solve novel problems. While most ARC tasks are easy for humans, they are challenging for state-of-the-art AI. What makes building intell
Externí odkaz:
http://arxiv.org/abs/2106.07824
Recent advances in computational cognitive science (i.e., simulation-based probabilistic programs) have paved the way for significant progress in formal, implementable models of pragmatics. Rather than describing a pragmatic reasoning process in pros
Externí odkaz:
http://arxiv.org/abs/2105.09867
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Akademický článek
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