

Explain It to Me Again, Computer - prostoalex
http://www.slate.com/articles/technology/future_tense/2013/02/will_computers_eventually_make_scientific_discoveries_we_can_t_comprehend.html

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Xcelerate
Very good article. It makes the point that science is heading in an
unprecedented direction where the complexity of what is happening is too
difficult for a single human mind to understand. In fact, I would say it's
almost _arrogant_ to assume that our 1100 grams of processing power is capable
of understanding the incredibly complex interactions that build and build upon
themselves.

This is the reason for computational science. Imagine trying to render a
photorealistic scene analytically. There's just no way you could do that.
Exactly integrate the rendering equation over complex geometries? It's
unfathomable. There's a reason the Schrodinger equation has only been solved
analytically for systems of just a few particles (not to be confused with
_exactly_ , which is possible with numerical methods).

Eventually science experiments will be conducted by setting up your initial
system, plugging in the relatively simple laws of physics, and letting it go
(well... if we solve the n-body problem).

I don't think biology will ever be "understood" in the same way that
derivatives or diffusion or gravity is understood. There's so many levels of
nested complexity that it is totally mind-boggling, and I'm surprised more
biologists aren't impressed by the sheer fact that any of it works at all.

~~~
Retric
I don't think what your describing is science. At it's core science is a
question of experimentation and running complex simulations can't truly
provide new information, just validation. That said, there is a great
temptation to call many things science because it lends credibility even if
you go though the motions and ignore why honest experementwtion has value in
the first place.

~~~
Xcelerate
What?!?

>Running complex simulations can't truly provide new information, just
validation.

This is entirely and absolutely false.

I think you may want to read up on simulation (FEM, molecular dynamics, etc.).
It's my research field. For instance, Quantum Monte Carlo has been used to
calculate energy levels to a ridiculous level of agreement with experimentally
measured values. There's plenty that can be learned about reality that can't
currently be studied through experimentation because it's either 1) too
expensive or 2) impossible to study with current technology.

Also, I wouldn't say science is "about" experimentation. It's about
prediction. Any theory that can more accurately predict a system's future
state than another theory is the better science. And simulations can do this
better in many cases than experiment.

~~~
troymc
A simulation is just a way of finding out what the _current_ model predicts
(maybe... assuming things like roundoff and truncation error didn't mess
things up).

What if the current model is _wrong_?

An experiment in the real/natural world is the only way to find out if a model
(or its simulation) is wrong. You have to ask nature.

~~~
Xcelerate
Quantum mechanics (or QFT) isn't likely to be found wrong anytime soon. And if
it is, it will be to such a degree that it has almost no day-to-day
consequences (much as how GR is only considered for particle physics and
satellite synchronization).

What I mean by this is that the whole nature of chemistry and biology is
already rooted on a very secure foundation. So as simulation capabilities
increase, you can be sure that these have a very high probability of modeling
the "higher-order" phenomena correctly. Even superconductors and superfluids
can be modeled correctly. The only things that may not be correct would be the
high energy limit of quantum gravity models. But simulation in this area is
almost (and may be?) nonexistent.

Now... having stated that we have an almost perfect description of the
underlying reality for any practical phenomena we would be interested in, it
is true that this model is currently mostly unamenable to calculation. The
amount of computation required to reach chemical accuracy is very high and we
may not have that capability for quite a while. So we make concessions and
approximate things. For example, wavefunctions are modeled as Slater
determinants which satisfy the wavefunction's antisymmetry but do not take
into effect all the correlative effects (like electron-electron interactions).

It is a scientific and mathematical challenge to characterize exactly how much
these models are off from the "true" solution. Most of the time, you have a
good idea if the approximation you are using will work for your domain. If I
want to fold proteins, I don't need anything more accurate than CHARMM
(describes pairwise interactions; it's a non-reactive potential). But if I
want to study Cooper Pairs (in superconductors) I'm going to need to make many
less approximations, and it will be a much more expensive model
computationally.

~~~
Retric
QM says nothing about the validity of your simulation. Worse we are incapable
of simulating something as complex as a single helium molicule using the full
QM model for 1 second using 1 days computing power on any current
supercomputer. Even protean folding is forced to greatly simplify what's going
on to the point it's often wrong. ( Proteans are not folded in a vacuum.)

Want to simulate a cell? Even with hardware 10^100 times faster you can't use
QM, it's all models of models. None of which are 100% accurate, but that's no
reason you can't Kidd yourself.

~~~
Xcelerate
Am I missing something or did you not read the post you replied to?

~~~
Retric
From a practical standpoint Chemistry is not based on QM. You basically said
QM > Chemistry > more complex things, my counter argument is even fairly
simple. Chemistry is way to complex to model from pure QM. So, even at your
first step your model is making simplifications. And 10^100 times the
processing power does not get you to the point where you can model even the
simplest cell for a meaningful time-frame.

Even at the timescales of atomic explosions and the best hardware / software
around those models are vary simplified.

PS: That's not to say you can't make useful simulations, but trusting them to
be accurate without testing is not science.

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derekp7
I don't really think it matters if we can fully understand a theory, as long
as that theory makes predictions that can be verified. For example, if a
computer-generated theory gives the ability to build a radio that is a million
times more efficient at spectrum utilization, and products can be built from
it, that theory is still useful even if it is uncomprehensible.

~~~
JulianK
I very much agree that you don't have to understand the details of how
something works for it to be useful. Whether that's a theory, a car or a
computer, the world is full of things that are too complex for me to
understand _fully_.

But if you don't truly understand something, I think you've given up control
of that thing to someone else. Someone else has to do the thinking for you.

Once that happens, you'll have a hard time doing something new in that area,
or even applying whatever it is you're supposed to know to new areas.

Understanding isn't the only thing that matters, but it still matters.

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jpxxx
I think this is giving humanity way too much credit. We're only going to care
about profitable ideas that algorithms come up with.

Imagine an electronic intelligence with a profound understanding of TV shows.
Who would possibly want to hear what it has to say?

~~~
saulrh

      Who would possibly want to hear what it has to say?
    

Television executives.

Everything has an application, and many fewer of those applications are
useless than you'd think. Plus there are higher-order effects to consider,
mostly related to the tools people develop to attack interesting problems. The
classic example is the space race: how useful is it to be able to fire four-
hundred-foot-tall fuel tanks into space?

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benbou09
Interesting article, but I think that the humans will probaly evolve in ways
that will extend their capacity of understanding. It may start by people
living longer and then someday they will have memory implants and maybe even
processing power implants.

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BariumBlue
We know that the mind is Turing complete. What the author is suggesting is
that it's possible for there to be some science or mathematics that is beyond
the capability of our mind to comprehend. (and that it's possible that there's
science or mathematics that may be derived by computers that we may not
fundamentally be able to understand)

However, any attempt to compute or derive new science/mathematics outside the
theoretical capabilities of our (Turing complete) minds on merely Turing
complete computers is doomed to failure (at least, until we get some computers
better than Turing complete ones).

~~~
comex
Turing complete is really only meaningful for a computer with infinite time
and storage; humans are limited in both. Just as a smarter human can
understand something a less smart human can't, computers will eventually
understand many things we can't wrap our heads around.

~~~
BariumBlue
True, but assuming in this future humans have developed brain enhancements,
the issue of storage space and computing power becomes less relevant. Though I
suppose the whole idea of brain enhancements would render the author's point
null anyways...

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nova
It doesn't help that most research papers are behind paywalls, or in forgotten
books long out of print but still in copyright (and owned by the publisher,
not the author), buried and not scanned theses, or technically published in
patents but then also unusable by anyone interested.

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lifeisstillgood
Science is not an agglomeration of random observations and facts - it should
not get more _complicated_ as more is discovered - if the theories and tools
like maths we have do not explain the theories then we are doing something
wrong with the fundamental tools (maths stats)

I accept there is more and more unknown or unexplained - but that is not to
say the theories we have are not explained with the same set of "unreasonably
effective" tools

~~~
TeMPOraL
> it should not get more complicated as more is discovered

But it does for the simple reason, that you need to read more and more papers
just to know whether or not someone didn't already do your research, or if
there isn't already a growing body of evidence against the theory you're
trying to develop.

[http://lesswrong.com/lw/kj/no_one_knows_what_science_doesnt_...](http://lesswrong.com/lw/kj/no_one_knows_what_science_doesnt_know/)

Scientific progress is about to hit a self-complexity boundary due to the
amount of knowledge we amassed - we have a huge overhead on adding any single
new piece to a puzzle. Our brains are not good enough for handling it anymore,
we need computers to help.

EDIT

And also because of that, I believe there's a huge amount of undiscovered
facts lurking in all the discoveries we already had described, that no one
noticed before because it requires correlating different papers from different
disciplines (think about the more messy fields, like biology/medicine).
Finding a way for computers to effectively do that work for us will likely
result in some amazing progress.

~~~
lifeisstillgood
Is that really true?

If so one should force the release of copyright papers from the publishers at
gunpoint tomorrow. And make google dedicate half it's employees to indexing it

I suspect though that there are so _few_ scientists working on most areas that
the research is old or outdated across the majority of fields.

And it rather indicates a lack of fundamental tools - for example studying
insect species - it's well know that there are millions of species to every
insectologist(?) - yet Linnaeus classification is three centuries old - one
could imagine a production line of genome mapping - put insec into machine out
comes its genome - oh look that is not the species we were thinking of

The tools (mechanical) and the tools (mental) need to integrate large branches
if science are what is needed - not a fear over the vast number of facts we
have acquired

