But they're not astronauts, so DALL-E did something nontrivial.
@pmddomingos
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Astronaut Riding Horse: DALL-E’s Remarkable Pose Accuracy
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What's remarkable about this image is that the astronaut is in the correct riding pose, even though DALL-E has presumably never seen "an astronaut riding a horse" as prompted.
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Deep Learning and AGI: Expectations vs Reality
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Complaining deep learning is not AGI is like complaining a car doesn't fly.
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Inductive Biases: Making Implicit Assumptions About Data Explicit
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Inductive biases are always assumptions about the world generating the data. They may, however, be expressed very opaquely as network structure or even arbitrary hacks (easy and common but bad; you should make your assumptions explicit and then test/modify/refute them).
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Data, Causality, and Action: Beyond Philosophical Definitions
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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).
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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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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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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.