Proceedings of the National Academy of Sciences, Vol. 118, No. 15 (2021). https://www.pnas.org/content/118/15/e2022886118.
Co-authors: Charles F. Manski, Alan H. Sanstad, and Stephen J. DeCanio
Abstract
Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including inter-model “structural” uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multi-model ensembles, sometimes with subjective Bayesian assignment of probabilities across models.
Co-authors: Charles F. Manski, Alan H. Sanstad, and Stephen J. DeCanio
Abstract
Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including inter-model “structural” uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multi-model ensembles, sometimes with subjective Bayesian assignment of probabilities across models.
Abstract
Alan Turing’s 1950 paper, “Computing Machinery and Intelligence,” contains much more than its proposal of the “Turing Test.” Turing imagined the development of what we today call AI by a process akin to the education of a child. Thus, while Turing anticipated “machine learning,” his prescience brings to the foreground the yet unsolved problem of how humans might teach or shape AIs to behave in ways that align with moral standards. Part of the teaching process is likely to entail AIs’ absorbing lessons from human writings.
Alan Turing’s 1950 paper, “Computing Machinery and Intelligence,” contains much more than its proposal of the “Turing Test.” Turing imagined the development of what we today call AI by a process akin to the education of a child. Thus, while Turing anticipated “machine learning,” his prescience brings to the foreground the yet unsolved problem of how humans might teach or shape AIs to behave in ways that align with moral standards. Part of the teaching process is likely to entail AIs’ absorbing lessons from human writings.
https://doi.org/10.1007/s00146-018-0851-7
Abstract
Can an Artificial Intelligence make distinctions among major works of politics, philosophy, and fiction without human assistance? In this paper, Latent Semantic Analysis (LSA) is used to find patterns in a relatively small sample of notable works archived by Project Gutenberg. It is shown that an LSA-equipped AI can distinguish quite sharply between fiction and non-fiction works, and can detect some differences between political philosophy and history, and between conventional fiction and fantasy/science fiction. It is conjectured that this capability is a step in the direction of “M-comprehension” (or “machine comprehension”) by AIs.
Keywords: Artificial Intelligence
Abstract
Can an Artificial Intelligence make distinctions among major works of politics, philosophy, and fiction without human assistance? In this paper, Latent Semantic Analysis (LSA) is used to find patterns in a relatively small sample of notable works archived by Project Gutenberg. It is shown that an LSA-equipped AI can distinguish quite sharply between fiction and non-fiction works, and can detect some differences between political philosophy and history, and between conventional fiction and fantasy/science fiction. It is conjectured that this capability is a step in the direction of “M-comprehension” (or “machine comprehension”) by AIs.
Keywords: Artificial Intelligence
Abstract
The effect of the spread of Artificial Intelligence (AI) on wages depends on both the form of aggregate production relationships and the elasticity of substitution between human and robotic labor. With a conventional production function involving labor, robots, and ordinary capital, an increase in robotic labor can have either a positive or a negative effect on wages. Alternatively, it is possible to estimate the aggregate production relationship without measuring capital or other fixed factors explicitly, using the procedure developed by Houthakker in the 1950s.
The effect of the spread of Artificial Intelligence (AI) on wages depends on both the form of aggregate production relationships and the elasticity of substitution between human and robotic labor. With a conventional production function involving labor, robots, and ordinary capital, an increase in robotic labor can have either a positive or a negative effect on wages. Alternatively, it is possible to estimate the aggregate production relationship without measuring capital or other fixed factors explicitly, using the procedure developed by Houthakker in the 1950s.
Abstract
Alexander Wendt’s Quantum Mind and Social Science is an effort to establish foundations of social science based on the ontology of modern physics. The quantum revolution has deservedly had repercussions in many sciences, but it is unwise to ground social science on physical theories, which are subject to constant revision. Additionally, despite its empirical success, there is no agreed-upon interpretation of quantum theory. Finally, even if there were, the random indeterminacy intrinsic to the quantum world cannot account for the intentionality of human action.
Alexander Wendt’s Quantum Mind and Social Science is an effort to establish foundations of social science based on the ontology of modern physics. The quantum revolution has deservedly had repercussions in many sciences, but it is unwise to ground social science on physical theories, which are subject to constant revision. Additionally, despite its empirical success, there is no agreed-upon interpretation of quantum theory. Finally, even if there were, the random indeterminacy intrinsic to the quantum world cannot account for the intentionality of human action.
Abstract
Various potential strategic interactions between a “strong” Artificial intelligence (AI) and humans are analyzed using simple 2 × 2 order games, drawing on the New Periodic Table of those games developed by Robinson and Goforth (The topology of the 2 × 2 games: a new periodic table. Routledge, London, 2005). Strong risk aversion on the part of the human player(s) leads to shutting down the AI research program, but alternative preference orderings by the human and the AI result in Nash equilibria with interesting properties.
Various potential strategic interactions between a “strong” Artificial intelligence (AI) and humans are analyzed using simple 2 × 2 order games, drawing on the New Periodic Table of those games developed by Robinson and Goforth (The topology of the 2 × 2 games: a new periodic table. Routledge, London, 2005). Strong risk aversion on the part of the human player(s) leads to shutting down the AI research program, but alternative preference orderings by the human and the AI result in Nash equilibria with interesting properties.