Erik P. Verlinde
2010 · arXiv
Starting from first principles and general assumptions Newton's law of gravitation is shown to arise naturally and unavoidably in a theory in which space is emergent through a holographic scenario. Gravity is explained as an entropic force caused by changes in the information associated with the positions of material bodies. A relativistic generalization of the presented arguments directly leads to the Einstein equations. When space is emergent even Newton's law of inertia needs to be explained. The equivalence principle leads us to conclude that it is actually this law of inertia whose origin is entropic.
Metadata: arXiv. Abstracts retain their original authors’ rights.
Original HN comments, with labels from simple text rules. A label describes the whole comment and may miss context or irony. Read the discussion to make up your own mind. About the labels
7 selected excerpts · 7 total mentions
As referenced in the paper, Verlinde proposed that gravity is an entropic force: http://en.wikipedia.org/wiki/Entropic_gravity I highly recommend Verlinde's brilliant paper, it's an easy read: http://arxiv.org/abs/1001.0785 The theory remains controversial, but it's a valuable attempt to put holographic principles center stage.
He's well-qualified. His background is in string theory. His theory is described in this paper: https://arxiv.org/pdf/1001.0785v1.pdf And the Wikipedia article about it is here: https://en.wikipedia.org/wiki/Entropic_gravity It's argued here that there's observational evidence against it: https://www.technologyreview.com/s/425220/experiments-show-g... I'm not familiar with the areas of physics the theory draws on. I am familiar with General Relativity, which agrees very well with observations, and (I think) makes fewer assumptions.
Useful to note that parent's Ref. 0 has been peer-reviewed and is published in JHEP, a respectable high energy physics journal. Sadly, articles like those linked often do not check the authors' credentials or whether the research being summarised has been successfully published somewhere rather than just put on the arXiv. The BBC (and possibly others) go further, asking rival or independent researchers to comment on new work; this approach (check for peer review and also seeking critical comment from third parties) should be the expected standard in scientific news reporting for a lay audience. DOI link: http://dx.doi.org/10.1007/JHEP04(2011)029
Original paper at [0], some corrections/comments at [1,2]. [3] argues that the proposed new ideas and the classical (Newtonian, If I understood this correctly) concept of gravity are actually identical, given suitable identifications of various entities. [0] http://arxiv.org/abs/1001.0785 [1] http://arxiv.org/abs/1012.5858 [2] http://arxiv.org/abs/1002.3876 [3] http://arxiv.org/abs/1003.1015
Gravitons are the kookiest theoretical particle I have ever heard of. I had an intuition that they were kooky and contrived when I first heard of them as a child, and now that I know more about them and gravity, I have even more disbelief in them. Why people still believe in them I will never know. Here's the latest thing I have seen, btw: http://arxiv.org/abs/1001.0785 Pop science interpretation of it: http://www.nytimes.com/2010/07/13/science/13gravity.html
Original paper (degree in Physics needed): http://arxiv.org/abs/1001.0785 Discusion of the paper: http://badphysics.wordpress.com/2010/01/11/%E2%80%9Con-the-o... Reply(s) by the author, Verlinde: http://staff.science.uva.nl/~erikv/page20/page18/page18.html
I haven't had a chance to go through the paper yet (http://arxiv.org/abs/1001.0785), but it seems that he does make some derivations and proof sketches. Can someone verify this?
More from the same topics.
Tom B. Brown, Benjamin Mann, Nick Ryder +28
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task.
Chiyuan Zhang, Samy Bengio, Moritz Hardt +2
Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance.
Scott Aaronson
In honor of Alan Turing's hundredth birthday, I unwisely set out some thoughts about one of Turing's obsessions throughout his life, the question of physics and free will.
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski +10
We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands.