
Visions of Future Physics - Bootvis
https://www.quantamagazine.org/20150922-nima-arkani-hamed-collider-physics/#top
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rubidium
The naturalness questions seems outside the realm of importance for physics
and over-hyped by this article (and perhaps by Arkani-Hamed).

A much more sober approach was put forth by David Gross in the article "And
Gross, who considers naturalness a murky concept, simply wants a last-ditch
search for new physics. “We need more hints from nature,” he said. “She’s got
to tell us where to go.”

This is the reality of particle physics right now: we have a theory that works
for pretty much all the measurements we've made, but it's not particularly
elegant. There are big questions certainly (like dark matter) but we have no
particle physics data to really study there. Particle physicists like elegant
theories (b/c they learned and loved Maxwells equations) and therefore aren't
satisfied with correct physics.

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pcmaffey
Seems like the question of naturalness or "knowability" speaks more to the
nature and limits of the human mind than of the universe itself.

Definitely agree with Gross. It's why the loss of species, destruction of our
environment, etc--replaced with mechanisms that are a reflection of ourselves
--is all so disturbing. We are depriving ourselves of the potential to learn
from the unique diverse world around us.

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jsprogrammer
>Seems like the question of naturalness or "knowability" speaks more to the
nature and limits of the human mind than of the universe itself.

Certainly, the apparent fact that we can only know our own perceptions (images
of observations) severely limits what we can know about the larger universe
that may contain our perceptions. It would be foolish to believe that you know
the ultimate reality; we already know that it is impossible to know since we
can only ever access perceptions of the projections of "reality". Either all
that exists is our perceptions, or we can never know the ultimate reality.

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ixtli
I'm pretty sure Nima was featured in the fantastic documentary Particle Fever
( [http://particlefever.com/](http://particlefever.com/) )

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astrodust
He basically took over the movie, but not in a bad way. Very engaging.

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JabavuAdams
How likely is a plan to build a new accelerator, over 30 years, to succeed?

If we look at the technological changes of the preceding 30 years, anything we
started in 1985 and finished in 2015 would likely be eclipsed by modern
capabilities.

Should I work on Physics, or General AI, or IA? It seems that GAI or IA solve
the other two.

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mariusz79
And how will GAI solve physics? AI is not god.

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adwn
Indeed. This is a common fallacy among believers of bootstrapping,
superintelligent AI: In order to understand and master the real world (and
humans, for that matter), you first have to interact with the real world –
i.e., you have to run experiments. Thinking really, really hard about a
physical problem will not solve that problem. Ancient philosophers tried it,
without much success.[1]

[1] e.g.,
[https://en.wikipedia.org/wiki/Aristotelian_physics](https://en.wikipedia.org/wiki/Aristotelian_physics)

~~~
qrendel
Except ancient philosophers weren't even working with scientific methodology.
The issue is with how much empirical data you need to be able to form
hypotheses about the world and falsify them, of which humans are almost
certainly not doing a near-optimal job. Otherwise we might as well say
scientific revolutions are independent of intelligence and the edifice of pre-
existing knowledge, and depend only on observed data about the world, and
there's substantial evidence against this[1]. For a counterargument from one
of the believers of bootstrapping, superintelligent AI:
[http://lesswrong.com/lw/qk/that_alien_message/](http://lesswrong.com/lw/qk/that_alien_message/)

[1] [http://infoproc.blogspot.com/2008/07/annals-of-
psychometry-i...](http://infoproc.blogspot.com/2008/07/annals-of-psychometry-
iqs-of-eminent.html) ... In addition to evidence such as the long periods of
time before simple understandings like natural selection, the heliocentric
model, and constant acceleration of gravity were developed.

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mariusz79
no matter how many hypothesis you create you need to be able to test them. AI
won't be able to run experiments by itself, at least not in the beginning. and
even when it could, some of them will require years to complete, simply
because so much science depends on real physical phenomena that often are very
slow. Computer based simulations are often very slow, and AI won't be able to
speed it up, just because it's AI.

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dframe
Incorrect thinking in two ways :

>A.I could run experiments by itself if it is provided with enough information
to do so. Classical physics (already possible) and even quantum physics, once
fully understood can be programmed. There are already scores of algorithms and
computer programs that can run physics experiments and it doesn't take years.
You seem to be misinformed about even present technological capability

> Computer simulations aren't slow. Distributed computing and processing power
> allow for a considerable number of experiments to be simulated.

If you work in this area, I feel you have a totally different view of things.
You can either speak in terms of 'x' isn't possible and end it there or say
'x' is possible and I'm going to discover how.

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mariusz79
Let's just consider a basic example - AI tries to develop better algorithms to
simulate fluid dynamics.

1\. How do you expect AI to create these simulations? Will it program it
itself? I assume that the answer is yes, if so a. it needs to know how to
program. b. it needs to know how to efficiently use the hardware that it can
access. c. it may need to know all the quirks of the software, hardware and
operating system that it works with. d. it also will need to have a pretty
good math skills

Someone will need to teach AI how to do all of that or it will need to learn
by itself.. This will take time.And experience.

Such AI will need to run the simulation it created, analyze it, compare
results, come up with a hypothesis, and do it all over again to refine the
results. What if there is a wrong assumption about fluid dynamics that is
caused by some wrong assumptions on a lower level, i.e theory of matter. How
will that change the results and will AI need to go back a step lower to "fix"
our understanding of matter? what if there are some issues with math that
first need to be "fixed", or totally new math theories must first be created?

Of course AI will not need to have everything perfect, right from the
beginning, and better theories will suffice, but to say that long-term
experiments such as this, are not necessary simply because AI will solve
physics is just wrong.

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qnaal
> fluid dynamics is hard, let's smash particles!

