But data can tell us what happens for every possible configuration of things that affect the barometer and the weather, including your actions. In this view, "causes" is a philosophical statement that is not required to guide your actions (or control a robot).
AI
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Musk Twitter verification: identity verification without clear communication
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No me hacía falta saberlo. Si Musk hubiera dicho: vamos a dar el check a quien pague pero al mismo tiempo vamos a implementar un riguroso sistema de verificación para garantizar que quien paga es quien dice ser (y lo hubiera explicado), el aluvión de críticas no habría sido tal.
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Strong innate priors in machine learning systems
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that’s learning that the set of relevant cases is empty; it’s still a strong innate prior
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Inductive bias guides learner function search in neural networks
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Not exactly. The learner could well discover that none of the symmetries in the space hold. It's an inductive bias in the precise sense of the term: it causes the learner to search for some functions over others.
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BIG-Bench Benchmarks Face Validity Scrutiny for AGI Predictions
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Trouble in BIG-Bench paradise? – @ErnestSDavis looks at 48 of the benchmarks within and finds problems with most: https://
cs.nyu.edu/~davise/Benchm
arks/BigBenchDiscussion.html
… – Many project AGI timelines based on performance on these benchmarks. If the benchmarks aren’t valid, consequent timelines are problematic -
Algorithm discovers specific symmetries from defined possibility space
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We tell them the space of possible symmetries (e.g., affine); the algorithm discovers which (e.g., translation, or some translations).
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Learning Symmetries: Discovering Constraints in Phenomenon Modeling
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The prior is that there's a space of symmetries the phenomenon being modeled may obey (e.g., affine), and the learning task is to discover which (e.g., only translations, or only some translations).
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Verification Badge Monetization Undermines Platform Trust and Security
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El error es regalar un símbolo azul que significa “cuenta verificada” a cambio de dinero, sin importar que quien lo compra sea quien dice ser. Por cierto, muy poco elegante tu falta de respeto. Y lo de que nadie sabía ni esperaba esto no es cierto.
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Controlled Perturbation: Function Application as Risk Factor
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But in reality you may fail when you try to do f(X), so doing it is effectively just another perturbation, albeit under your control.
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Rung 3 Model: Conditioning on Evidence and Counterfactuals
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All rung 3 requires is a model that lets you condition on arbitrary evidence, including counterfactuals. To learn the model you need inductive biases / prior assumptions, which are necessary and sufficient for all rungs and may or may not include a theory of causality.