Answers.Body: <p>Yes.</p>
  
  <p>Some good examples of this are <a href="https://www.wired.com/2009/04/newtonai/" rel="nofollow noreferrer">Lipson's</a> work using evolutionary models, and Wu &amp; Tegmark's work on a <a href="https://arxiv.org/pdf/1810.10525.pdf" rel="nofollow noreferrer">theory-based life-long learner</a>, and <a href="https://arxiv.org/pdf/1807.10300.pdf" rel="nofollow noreferrer">Iten et al.'s</a> work with deep neural networks. There are many, many, other research papers in this area, and there is a lot more work that is ongoing. The endgame for a lot of this work is the hope that we can synthesize new theories automatically.</p>
  
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Answers.CreationDate: 2019-03-31T16:20:56.823Z
Answers.Text: It depends on what is assumed to be known. For instance Newton had to invent differential calculus along the way.
Answers.UserDisplayName: Mathieu Bouville
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Answers.CreationDate: 2019-03-09T17:47:01.493Z
Answers.LastActivityDate: 2019-03-09T17:47:01.493Z
Answers.UserDisplayName: John Doucette
Body: <p>I would love to know if an AI model could come up with certain theories of the old like Pythagoras' theorem, Euclid's formulations,
  Newton's gravity, Einstein's theories if provided and trained with sufficient amount of observable data available at those period of time. If this is possible can unsolved conjectures be proved by AI? Or even better can AI develop new theories or will it  fail to come up with even basic mathematical operations by itself?</p>
  
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Comments.CreationDate: 2019-03-09T17:49:55.633Z
Comments.Text: @DuttaA I'd argue that Pearl's work handles the cause/effect side pretty well, but if the goal is just to _propose_ theories from data, then we don't need to establish causality.
Comments.UserDisplayName: John Doucette
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Comments.CreationDate: 2019-03-09T22:11:44.220Z
Comments.Text: @DuttaA Hmm. That's true. Usually the techniques I mentioned in my answer either include a temporal component (which lets you infer the direction of causality for a lot of physical phenomena). Judea Pearl's work actually goes further than that though, and lets you rule out a lot (perhaps all but one) casual story from data without a temporal component.
Comments.UserDisplayName: John Doucette
Tags: <machine-learning><philosophy><automated-theorem-proving>
Title: Can AI come up with scientific theories of past when provided with sufficient data available at that time?
UserDisplayName: katipra
ThreadText: # Can AI come up with scientific theories of past when provided with sufficient data available at that time?
  
  (Asked by: username_0 on 2019-03-09)
  
  I would love to know if an AI model could come up with certain theories of the old like Pythagoras' theorem, Euclid's formulations,
  Newton's gravity, Einstein's theories if provided and trained with sufficient amount of observable data available at those period of time. If this is possible can unsolved conjectures be proved by AI? Or even better can AI develop new theories or will it fail to come up with even basic mathematical operations by itself?
  
  **Tags:** machine-learning, philosophy, automated-theorem-proving
  
  > **Comments:**
  > **username_1**: @DuttaA I'd argue that Pearl's work handles the cause/effect side pretty well, but if the goal is just to _propose_ theories from data, then we don't need to establish causality.  
  > **username_1**: @DuttaA Hmm. That's true. Usually the techniques I mentioned in my answer either include a temporal component (which lets you infer the direction of causality for a lot of physical phenomena). Judea Pearl's work actually goes further than that though, and lets you rule out a lot (perhaps all but one) casual story from data without a temporal component.  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2019-03-09. Score: 1
  
  Yes.
  
  Some good examples of this are [Lipson's](https://www.wired.com/2009/04/newtonai/) work using evolutionary models, and Wu & Tegmark's work on a [theory-based life-long learner](https://arxiv.org/pdf/1810.10525.pdf), and [Iten et al.'s](https://arxiv.org/pdf/1807.10300.pdf) work with deep neural networks. There are many, many, other research papers in this area, and there is a lot more work that is ongoing. The endgame for a lot of this work is the hope that we can synthesize new theories automatically.
  
  > **Comments:**
  > **username_2**: It depends on what is assumed to be known. For instance Newton had to invent differential calculus along the way.  
  
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