Of course nature's "theory" could be beyond a human's comprehension.
It is assumed that there all that's needed can be reduced to human (mathematical) language that can be expressed in a few lines of LaTeX Math. @philipthrift On Friday, May 29, 2020 at 7:45:58 PM UTC-5, Lawrence Crowell wrote: > > On Thursday, May 28, 2020 at 11:20:49 AM UTC-5, Philip Thrift wrote: >> >> >> https://www.facebook.com/461616050561921/posts/3107668729289960/ >> >> >> We just posted a new AI paper on how to automatically discover laws of >> physics from raw video with machine learning. For example, we feed in the >> video below of a rocket moving in a circles in a magnetic field, seen >> through a distorting lens, and our code automatically discovers the Lorentz >> Force Law. It took Silviu and me about a year to get this working, by using >> ideas inspired by general relativity and the the theory of knots in >> 5-dimensional space, so we're excited to be done! >> https://arxiv.org/abs/2005.11212 >> >> @philipthrift >> > > > The preprint address is below. I would like to think the big question on > quantum gravitation is resolved by basic human thought. Maybe AI systems > can verify the theoretical result(s) and give some support. > > LC > > https://arxiv.org/abs/2005.11212 > > Symbolic Pregression: Discovering Physical Laws from Raw Distorted Video > Silviu-Marian Udrescu > <https://arxiv.org/search/cs?searchtype=author&query=Udrescu%2C+S> (MIT), Max > Tegmark <https://arxiv.org/search/cs?searchtype=author&query=Tegmark%2C+M> > (MIT) > > We present a method for unsupervised learning of equations of motion for > objects in raw and optionally distorted unlabeled video. We first train an > autoencoder that maps each video frame into a low-dimensional latent space > where the laws of motion are as simple as possible, by minimizing a > combination of non-linearity, acceleration and prediction error. > Differential equations describing the motion are then discovered using > Pareto-optimal symbolic regression. We find that our pre-regression > ("pregression") step is able to rediscover Cartesian coordinates of > unlabeled moving objects even when the video is distorted by a generalized > lens. Using intuition from multidimensional knot-theory, we find that the > pregression step is facilitated by first adding extra latent space > dimensions to avoid topological problems during training and then removing > these extra dimensions via principal component analysis. > > Comments: 12 pages, including 6 figs > Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine > Learning (cs.LG); Machine Learning (stat.ML) > Cite as: arXiv:2005.11212 <https://arxiv.org/abs/2005.11212> [cs.CV] > (or arXiv:2005.11212v1 <https://arxiv.org/abs/2005.11212v1> [cs.CV] for > this version) > Bibliographic data > [Enable Bibex (What is Bibex? <https://labs.arxiv.org/>)] > Submission historyFrom: Max Tegmark [view email > <https://arxiv.org/show-email/27c9cd48/2005.11212>] > *[v1]* Tue, 19 May 2020 18:00:52 UTC (6,098 KB) > Download: > > - PDF <https://arxiv.org/pdf/2005.11212> > - Other formats <https://arxiv.org/format/2005.11212> > > (license <http://arxiv.org/licenses/nonexclusive-distrib/1.0/>) > Current browse context: > cs.CV > < prev > <https://arxiv.org/prevnext?id=2005.11212&function=prev&context=cs.CV> > | next > > <https://arxiv.org/prevnext?id=2005.11212&function=next&context=cs.CV> > new <https://arxiv.org/list/cs.CV/new> | recent > <https://arxiv.org/list/cs.CV/recent> | 2005 > <https://arxiv.org/list/cs.CV/2005> > Change to browse by: > cs <https://arxiv.org/abs/2005.11212?context=cs> > cs.LG <https://arxiv.org/abs/2005.11212?context=cs.LG> > stat <https://arxiv.org/abs/2005.11212?context=stat> > stat.ML <https://arxiv.org/abs/2005.11212?context=stat.ML> > References & Citations > > - NASA ADS <https://ui.adsabs.harvard.edu/abs/arXiv:2005.11212> > - Google Scholar > > <https://scholar.google.com/scholar?q=Symbolic%20Pregression%3A%20Discovering%20Physical%20Laws%20from%20Raw%20Distorted%20Video.%20arXiv%202020> > - Semantic Scholar <https://api.semanticscholar.org/arXiv:2005.11212> > > Export citation > Bookmark > [image: BibSonomy logo] > <https://arxiv.org/ct?url=http%3A%2F%2Fwww.bibsonomy.org%2FBibtexHandler%3FrequTask%3Dupload%26url%3Dhttps%3A%2F%2Farxiv.org%2Fabs%2F2005.11212%26description%3DSymbolic+Pregression%3A+Discovering+Physical+Laws+from+Raw+Distorted+Video&v=d773ebe1>[image: > > Mendeley logo] > <https://arxiv.org/ct?url=https%3A%2F%2Fwww.mendeley.com%2Fimport%2F%3Furl%3Dhttps%3A%2F%2Farxiv.org%2Fabs%2F2005.11212&v=f44ef558>[image: > > Reddit logo] > <https://arxiv.org/ct?url=https%3A%2F%2Freddit.com%2Fsubmit%3Furl%3Dhttps%3A%2F%2Farxiv.org%2Fabs%2F2005.11212%26title%3DSymbolic+Pregression%3A+Discovering+Physical+Laws+from+Raw+Distorted+Video&v=aa8671f9>[image: > > ScienceWISE logo] > <https://arxiv.org/ct?url=http%3A%2F%2Fsciencewise.info%2Fbookmarks%2Fadd%3Furl%3Dhttps%3A%2F%2Farxiv.org%2Fabs%2F2005.11212&v=2d5a1457> > Which authors of this paper are endorsers? > <https://arxiv.org/auth/show-endorsers/2005.11212> | Disable MathJax (What > is MathJax? <https://arxiv.org/help/mathjax>)Browse v0.3.0 released > 2020-04-15 <https://github.com/arXiv/arxiv-browse/releases/tag/0.3.0> > Feedback? > > - About arXiv <https://arxiv.org/about> > > -- You received this message because you are subscribed to the Google Groups "Everything List" group. 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