Zusammenfassungen
We present the results of the NLP Community Metasurvey. Run from May to June 2022, the survey elicited opinions on controversial issues, including industry influence in the field, concerns about AGI, and ethics. Our results put concrete numbers to several controversies: For example, respondents are split almost exactly in half on questions about the importance of artificial general intelligence, whether language models understand language, and the necessity of linguistic structure and inductive bias for solving NLP problems. In addition, the survey posed metaquestions, asking respondents to predict the distribution of survey responses. This allows us not only to gain insight on the spectrum of beliefs held by NLP researchers, but also to uncover false sociological beliefs where the community’s predictions don’t match reality. We find such mismatches on a wide range of issues. Among other results, the community greatly overestimates its own belief in the usefulness of benchmarks and the potential for scaling to solve real-world problems, while underestimating its own belief in the importance of linguistic structure, inductive bias, and interdisciplinary science.
Von Julian Michael, Ari Holtzman, Alicia Parrish, Aaron Mueller, Alex Wang, Angelica Chen, Divyam Madaan, Nikita Nangia, Richard Yuanzhe Pang, Jason Phang, Samuel R. Bowman im Text What do NLP researchers believe? (2022) Dieser Text erwähnt ...
Personen KB IB clear | Sandhini Agarwal , Dario Amodei , Amanda Askell , Emily M. Bender , Christopher Berner , Tom B. Brown , Mark Chen , Benjamin Chess , Rewon Child , Jack Clark , Kewal Dhariwal , Prafulla Dhariwal , Timnit Gebru , Scott Gray , Tom Henighan , Ariel Herbert-Voss , Christopher Hesse , John P. A. Ioannidis , Jared Kaplan , Gretchen Krueger , Mateusz Litwin , Benjamin Mann , Sam McCandlish , Angelina McMillan-Major , Arvind Neelakantan , Alec Radford , Aditya Ramesh , Nick Ryder , Girish Sastry , Shmargaret Shmitchell , Pranav Shyam , Eric Sigler , Melanie Subbiah , Ilya Sutskever , Rich Sutton , Clemens Winter , Jeffrey Wu , Daniel M. Ziegler | ||||||||||||||||||||||||||||||||||||
Aussagen KB IB clear | Allgemeine künstliche Intelligenz (AGI) könnte zu einem Weltuntergang führen
Machine Learning kann bestehende Vorurteile/Ungerechtigkeiten verstärken/weitertragen | ||||||||||||||||||||||||||||||||||||
Begriffe KB IB clear | AGI , deep learning , Ethikethics , Generative Machine-Learning-Systeme (GMLS)computer-generated text , Generative Pretrained Transformer 3 (GPT-3) , Künstliche Intelligenz (KI / AI)artificial intelligence , Linguistiklinguistics , machine learning , Prognose , Sprachelanguage , Wissenschaftscience | ||||||||||||||||||||||||||||||||||||
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Nicht erwähnte Begriffe | Chat-GPT, Generative Pretrained Transformer 4 (GPT-4), GMLS & Bildung, Intelligenz |
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1 Erwähnungen
- Künstliche Intelligenz - Dem Menschen überlegen - wie KI uns rettet und bedroht (Manfred Spitzer) (2023)
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