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RECENT POSTS IN THIS TOPIC

Peter Jackson: on 2/25/18 at 23:38pm UTC, wrote Enrico, Fascinating essay, nicely explained & very informative. I touched...

Enrico Prati: on 2/25/18 at 23:08pm UTC, wrote Dear Vladimir thank you for having found time to read my essay and for...

Enrico Prati: on 2/25/18 at 22:59pm UTC, wrote Dear Cristinel, thank you for your time to read my essay. I appreciate and...

Don Limuti: on 2/25/18 at 20:56pm UTC, wrote Enrico, I am not quite on top of this essay contest as I would like to...

Cristinel Stoica: on 2/25/18 at 14:58pm UTC, wrote Dear Enrico, I think your essay is very good, and I like it. Do you...

Steven Andresen: on 2/22/18 at 6:52am UTC, wrote Dear Enrico If you are looking for another essay to read and rate in the...

Vladimir Fedorov: on 2/22/18 at 6:32am UTC, wrote Dear Enrico, I highly estimate you essay exelent. It is so close to me....

Juan Ramón González Álvarez: on 2/22/18 at 2:11am UTC, wrote Laws of physics aren't mathematical. Physics is not mathematics. Feynman...


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FQXi FORUM
May 23, 2019

CATEGORY: FQXi Essay Contest - Spring, 2017 [back]
TOPIC: The challenge of the deep learning paradigm to the scientific method: hierarchy between models and identification of fundamental laws from algorithmic information theory by Enrico Prati [refresh]
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Author Enrico Prati wrote on Feb. 1, 2018 @ 21:28 GMT
Essay Abstract

Planets do not precisely follow the Newton's law because it is a first order approximation of general relativity. In what sense do we intend that planets move according to general relativity? The laws of physics are extrapolated by complex sets of experiments and they are considered fundamental the more they allow to describe concisely acceptable ideal approximations of real systems. The subsequent mathematical description, combined with initial conditions, enables to predict time evolution (or time-dependent probabilities in quantum physics). Currently, general relativity is the best model to fit the orbit of planets once external perturbation are estimated and subtracted. Recently the deep learning paradigm is challenging scientific method thanks to its Bayesian capability to account for complicated systems when complexity sets in, based on the exploitation of huge amount of data. Deep learning ignores Newton's and Einstein's laws but it is significantly much more efficient for instance to design the best trajectory to send a spacecraft from the Earth to Mars in the shortest time. I define a criterion based on algorithmic information theory to assess a hierarchy between scientific models to define the fundamental one, and to compare how different methods such as scientific method, deep learning, and non-scientific methods perform in the description of a set of experiments.

Author Bio

Enrico Prati is research scientist of Italian CNR in Milan. His main research interests are quantum information processing, deep learning, atom based semiconductor devices physics. He is Visiting Scholar of the Waseda University from 2014. He has been keynote speaker of IEDM 2014, TEDx speaker in Rome in 2016, speaker of the International Conference of Rebooting Computing in 2017. He has published about 80 peer reviewed articles including Nature Quantum Information and Nature Nanotechnology. He is coorganizer of the DICE conference and he received the 4th Jury prize by the FQXI on the Nature of Time Essay Contest of 2008.

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Francesco D'Isa wrote on Feb. 1, 2018 @ 21:56 GMT
Dear Enrico,

thank you for your nice essay, which I found interesting and pleasurable. I really enjoyed how you applied deep learning models to physics theories, I think that's a stimulating point of view.

You write that

>The most fundamental model correspond to the most compact in algorithmic sense when arbitrarily extending the set of experimental data to account for.

I found this idea in some other essay as well, I think you will read them with interest. I wonder: it's for sure a good principle for our purposes, but it's relative to humans' goals and methods. It doesn't invalidate your arguments, but its fundamentality is relative to our knowledge, it's an anthropomorphic one.

In bocca al lupo! ;)

Francesco D'Isa

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Author Enrico Prati replied on Feb. 2, 2018 @ 22:42 GMT
Dear Francesco,

thank you for your comments. I would like to clarify two points: I've decided to avoid any possible influence by other works so I did not read any of the posted Essays before submitting mine. The reason why some ideas could already be mentioned in other works is probably because the theory of algorithmic information is a common background in science and it fits this topic, so I'm not surprised if other Authors involved the same idea in their reasoning.

About the point you raised that the algorithmic approach is "antropomorphic", I would say that more generally it is "observer-related" and that also the concept of "being fundamental" is observer-related, as part of our language. In some sense, all the physical quantities in our models are observer related (they are used to describe experiments done by someone) and language dependent, made formal thanks to a mathematical theory.

Thank you for your comments.

My best regards

E.

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Francesco D'Isa replied on Feb. 3, 2018 @ 10:05 GMT
Dear Enrico,

I had no doubt, I just pointed your attention there because I think it could be useful for your studies. In the essays there are many common trends, it happened with mine as well, I think it's normal and quite a good sign.

Yes, "observer-related" suits it better, I made a text about Nagarjuna's philosophy and absolute relativism that explore this issue as well.

Bests,

Francesco

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Branko L Zivlak wrote on Feb. 2, 2018 @ 09:45 GMT
Dear Enrico,

I also think there are plenty of extrapolations in the meaning of physical laws. But it does not exist, in the best TOE, written in your town Milan in Latin language. You did not even mention that theory.

With best wishes,

Branko

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Domenico Oricchio wrote on Feb. 4, 2018 @ 12:12 GMT
I am thinking that a deep learning and human knowledge are not commensurable: if I have a deep learning law, and an human law, the level of understanding are different; only if the knowledge is transferable, or intelligible, it is possible understand the Nature (I am thinking there is the same problem of ABC proof of Mochizuchi).

A question, if the Einstein field equation admit a low energy linearization, and the low energy approximation would be quantizable, then would the general relativity be the ultimate theory?

Could the problem of the best trajectory be solved using the deep learning like an oracle (of which we do not want know the reasoning), and to improve the solution solving the Newton’s law for trajectories with little perturbation (for example different random fuel injections along the trajectory, using a genetic algorithm like evolution strategies)?

I have a problem with the most fundamental model: if are there more equivalent theory? If I have two theories with the same solutions, what is the most fundamental? I have the idea that it is the one that simplifies the calculations, but it is only an opinion.

A good essay

Regards

Domenico

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Author Enrico Prati replied on Feb. 4, 2018 @ 15:26 GMT
Dear Domenico

thank you for reading my Essay and starting this discussion.

>> I am thinking that a deep learning and human knowledge are not commensurable: if I have a deep learning law, and an human law, the level of understanding are different; only if the knowledge is transferable, or intelligible, it is possible understand the Nature (I am thinking there is the same problem...

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Steven Andresen wrote on Feb. 6, 2018 @ 05:18 GMT
Dear Enrico Prati

Just letting you know that I am making a start on reading of your essay, and hope that you might also take a glance over mine please? I look forward to the sharing of thoughtful opinion. Congratulations on your essay rating as it stands, and best of luck for the contest conclusion.

My essay is titled

“Darwinian Universal Fundamental Origin”. It stands as a novel test for whether a natural organisational principle can serve a rationale, for emergence of complex systems of physics and cosmology. I will be interested to have my effort judged on both the basis of prospect and of novelty.

Thank you & kind regards

Steven Andresen

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Satyavarapu Naga Parameswara Gupta wrote on Feb. 13, 2018 @ 19:44 GMT
Prof Enrico Prati

Very nice words..... "The laws of physics are extrapolated by complex sets of experiments and they are considered fundamental the more they allow to describe concisely acceptable ideal approximations of real systems. The subsequent mathematical description, combined with initial conditions, enables to predict time evolution (or time-dependent probabilities in quantum...

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Juan Ramón González Álvarez wrote on Feb. 22, 2018 @ 02:11 GMT
Laws of physics aren't mathematical. Physics is not mathematics. Feynman devoted some effort to discuss differences between both disciplines.

"Therefore heat has been reduced as a kind of energy". Heat is not a kind of energy. Energy is a state function. Heat isn't. Heat is a mechanism of change of energy.

"the square of both electric and magnetic field have been identified with...

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Vladimir Nikolaevich Fedorov wrote on Feb. 22, 2018 @ 06:32 GMT
Dear Enrico,

I highly estimate you essay exelent.

It is so close to me. «Planets do not precisely follow the Newton's law»

I hope that my modest achievements can be information for reflection for you.

Vladimir Fedorov

https://fqxi.org/community/forum/topic/3080

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Author Enrico Prati replied on Feb. 25, 2018 @ 23:08 GMT
Dear Vladimir

thank you for having found time to read my essay and for your warm words.

You touch a major point by mentioning that sentence: indeed strictly speaking, classical orbits refer to the center of mass, an ideal point to which we attribute physical properties. We have limited vocabulary and limited representations in our brain to describe the physical world, so we should agree with Einstein that it is surprising is that world is comprehensible, as you quoted in your essay, but perhaps we need to do a further step and recognize that this is only apparently true.

My very best regards

Enrico

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Steven Andresen wrote on Feb. 22, 2018 @ 06:52 GMT
Dear Enrico

If you are looking for another essay to read and rate in the final days of the contest, will you consider mine please? I read all essays from those who comment on my page, and if I cant rate an essay highly, then I don’t rate them at all. Infact I haven’t issued a rating lower that ten. So you have nothing to lose by having me read your essay, and everything to...

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Cristinel Stoica wrote on Feb. 25, 2018 @ 14:58 GMT
Dear Enrico,

I think your essay is very good, and I like it. Do you believe it may be possible that the laws of physics, as we perceive them, may emerge somehow from a process of deep learning? For example maybe the world is a (quantum?) neural network, or a cellular automaton, which learned to behave in a certain way we observe now. A potential difficulty may be that normally we feed...

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Author Enrico Prati replied on Feb. 25, 2018 @ 22:59 GMT
Dear Cristinel,

thank you for your time to read my essay. I appreciate and share your comments, so in the following let me just focus on the question:

"Do you believe it may be possible that the laws of physics, as we perceive them, may emerge somehow from a process of deep learning? For example maybe the world is a (quantum?) neural network, or a cellular automaton, which learned to behave in a certain way we observe now."

I believe that laws of physics emerge from pure information. In this sense, the ideas of D'Ariano should be maybe further developed. Deep learning alone is not sufficient for emergence of rules in my opinion: you may train the network to either classify patterns of whatever information (supervised); or find hidden structures among data (unsupervised) or to combine exploration/exploitation to maximize reward once defined a target. All such powerful learning methods imply an external target to be set by someone, and some substrate to implement the process. Maybe neural networks are just very good approximators but they do not respect an economic principle for summarizing information in a compact way, so they are not the true ultima language of physics. Compact mathematical expression for describing physical laws are not only well performing from an algorithmic point of view, but it looks they solicit our understanding, they reflect some general property according to a principle of simplicity.

Thank you again for your comments

Enrico

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Don Limuti wrote on Feb. 25, 2018 @ 20:56 GMT
Enrico,

I am not quite on top of this essay contest as I would like to be...meaning I just found your essay. I took notice of deep learning when it was used in the field of biology to predict the ebb and flow of fish populations. Traditional algorithmic methods failed terribly.

Now, if I understand you correctly...you are proposing deep learning to determine the best algorithmic methods. I am all grins :) and believe your proposal will result in all schools having deep learning departments to determine "correct" algorithms.

I am glad I did not miss your essay!

Don Limuti

Do visit my blog, it has a simple theory of space-time.

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Peter Jackson wrote on Feb. 25, 2018 @ 23:38 GMT
Enrico,

Fascinating essay, nicely explained & very informative. I touched on deep learning, layering and loops in last years entry but hadn't realised it crossed over into my main study area; the ecliptic plane issue/stellar aberration,(ECRF/Barycentric transform, quantum optics, observer rationale and issues with relativity.

Past essays explaining a rationale have been finalists (since 2011) including 1st & 2nd placed community scores. The transform is explained in the model along with stellar aberration and a number of other anomalies. Editors don't seem interested in ontological constructions involving actual lens mechanism etc. without algorisation, but your approach seems maybe to vindicate it. If you have time to look and advise I'd be greatly appreciative.

The last 3yrs have been testing the model in finding a classical mechanism for QM. This years, shockingly, does so! Though of course weirdness is now mainstream so few dare even look! Declan Traill's short essay confirms the Bell violation is met with matching code and plot. Hope you get a chance to look.

Well written and thank you.

Peter

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