
An Amoeba-Based Computer Found Solutions to 8-City Traveling Salesman Problem - n0pe_p0pe
https://motherboard.vice.com/en_us/article/gy7994/an-amoeba-based-computer-calculated-approximate-solutions-to-a-very-hard-math-problem
======
KineticLensman
Nice for the amoeba, but slime molds have first mover advantage. They solved
the travelling salesman problem in 2013, and they managed 20 cities [0].

[edit: my bad: the two reports referred to amoeba and slime molds but are
actually discussing the same species (Physarum polycephalum)]

[edit 2: it's been pointed out that the 2013 creature I referenced wasn't even
real, just a sim. However, _real_ slime molds can model Canadian transport
networks [1] and can use slime trails to navigate around obstacles [2]]

[Edit 3. The Vice article says 'Amoeba' but references a Royal Society [3]
story that says 'slime mold'. Wikipedia [4] says that slime molds comprise the
mycetozoan group of the amoebozoa. So slime molds are in fact amoeba, but not
all amoeba are slime molds.]

[0] [https://phys.org/news/2013-03-blob-
salesman.html](https://phys.org/news/2013-03-blob-salesman.html)

[1] [https://phys.org/news/2012-03-slime-mold-mimics-canadian-
hig...](https://phys.org/news/2012-03-slime-mold-mimics-canadian-
highway.html#nRlv)

[2] [https://phys.org/news/2012-10-slime-molds-spatial-
memory.htm...](https://phys.org/news/2012-10-slime-molds-spatial-
memory.html#nRlv)

[3]
[https://royalsocietypublishing.org/doi/10.1098/rsos.180396](https://royalsocietypublishing.org/doi/10.1098/rsos.180396)

[4]
[https://en.wikipedia.org/wiki/Slime_mold](https://en.wikipedia.org/wiki/Slime_mold)

~~~
yorwba
The work in 2013 appears to have been a _simulation_ of slime mold/amoeba
behavior, not an actual living organism.

~~~
jacquesm
One slime mold to another: It just might be that these artifacts of
quantization we see are proof we are living in a simulation. In fact, there is
this argument to be made that the chances that we live in a simulation are
bigger than that we do not. Here, let me show you...

~~~
whatshisface
> _It just might be that these artifacts of quantization we see_

The artifacts of quantization that we see are of the kind that come from
continuous differential equations, not the kind that come from a lattice. Even
the qbits in a quantum computer are found on a continuous Bloch sphere,
instead of a discrete zero or one state space.

------
brg1007
So if I understood well the concept, this type of amoeba is like an ASIC for
traveling salesman problem. Interesting concept, to find bio-organisms that
can be used very efficentlly to "solve" a certain type of problem.

~~~
frevd
No, if I understood right, this thing is a quantum computer solving NP-
complete problems in linear time. And the article claims to have a simulation
doing the same, but they are not sure whether this is actually only a special
case scenario.

~~~
Filligree
It found a solution. Not an _optimal_ solution.

~~~
sano1
Similar to the way my NN (brain) can solve the TS problem by just guessing the
best route. Sure that's <i>a</i> solution. edit:typo

~~~
whatshisface
On Hackernews you can use asterisks to produce italics, like in markdown.

------
emtel
This is a non story, at least insofar as it concerns complexity. The result
would be no more or less interesting if the amoebas sorted an array or did
some other polynomial time task. Here's why:

1\. Finding approximate or near-optimal solutions for hard optimization
problems is almost always easy. So in this case, an amoeba did something that
is also easy to do with a classical computer.

2\. 8 is a small number. There are 8! possible paths, which is about 40,000,
which means you could find the optimal solution to this problem in a
millisecond or two with a classical computer. To be remotely interesting,
you'd need to solve a TSP for a number like, say, 50 or 100.

3\. Even if you had an amoeba that somehow could find solutions for large
numbers of cities, how would you know? You can't solve the problem instance
yourself, so you don't know how close it is to the optimum. This is because
TSP is not in NP, which are the set of problems whose solutions can be
verified in polynomial time. Rather, its NP-hard, meaning its at least as hard
as any NP problem, but its solutions can't be verified in polynomial time. So
if amoebas had some magic TSP solving algorithm, we probably wouldn't be able
to tell that they did unless we had our own magic algorithm to test it
against.

4\. Also, even if the amoeba could solve this _optimally_ for large instances,
_most_ instances of hard problems are actually easy. Therefore, simply showing
that the amoebas can do this for some finite number of instances is not
sufficient. An efficient algorithm for TSP or some other hard problem has to
be able to solve _any possible instance_ efficiently, in order for this to
have any implications for questions like P=NP. So if you wanted to show that
amoebas had a magic TSP algorithm, you'd have to formally prove that their
algorithm works on all instances. Since amoebas themselves are not easily
formalizable, I don't see how you'd do this.

~~~
algorias
>> This is because TSP is not in NP

Um, the decision version of TSP (does there exist a Hamilton path of length at
most x?) is definitely in NP. It is also NP-hard (your definition of NP-
hardness is wrong).

I guess what you're trying to say is that the optimization version of TSP
(find the shortest Hamilton path) cannot be verified in polynomial time, as
this would require answering the decision version of TSP in the negative for
some length x, and this is hard to verify since TSP is _not_ in co-NP.

In spirit, your comment is absolutely spot on. But as we all know, technically
correct is the best kind of correct :)

~~~
emtel
But the article isn't talking about the decision TSP, is it, so your objection
doesn't apply?

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thekhatribharat
A couple of questions (some of them might be stupid :))

\- It says they used a neural network to control illumination in different
channels, so the total time complexity should consider neural network + amoeba
system for calculations.

\- When you put the amoeba in a environment with a vastly large number of
channels, does it still behave the same? I mean, does it scale? Maybe amoeba
becomes less averse to illumination with increase in number of channels and
you get progressively worse solutions to TSP.

~~~
salamanderman
The neural network is like the plate itself, fixed at least for any
configuration of N cities. Interpret that as you will. The plate itself has
N^2 channels.

------
devy
Multiple reposts from the last 2 days:

[https://news.ycombinator.com/item?id=18726441](https://news.ycombinator.com/item?id=18726441)

[https://news.ycombinator.com/item?id=18729284](https://news.ycombinator.com/item?id=18729284)

[https://news.ycombinator.com/item?id=18725828](https://news.ycombinator.com/item?id=18725828)

------
jadar
So does this mean that there is a polynomial solution to the Traveling
Salesman problem (and by extension, every other NP-complete problem)? Or does
this mean that the amoeba is just really good at approximating a solution to
it, so it's either not a complete solution, or just solves the exponential
problem really quickly?

~~~
lainga
I would caution against equating the computational capacity of a physical
entity like this with what a single Turing machine or equivalent can do. The
classic example is that I can sort a bunch of arbitrary-precision real numbers
in O(n) time using spaghetti: cut a piece to length for each number, then push
the ends flat against a table and sweep your hand down along them while you
repeatedly remove the first piece to touch your hand [0]. The pieces of
spaghetti are able to do this because they're acting like a parallel computer;
the thousands or millions of chemical reactions taking place inside the amoeba
are probably doing the same thing.

[0]
[https://en.wikipedia.org/wiki/Spaghetti_sort](https://en.wikipedia.org/wiki/Spaghetti_sort)

~~~
aasasd
Rather idiosyncratically, this algorithm is prone to breaking the items being
sorted.

~~~
TheOtherHobbes
Often a problem for spaghetti code techniques.

~~~
quickthrower2
Are we still talking pasta, or badly written OO code!

------
DmitryOlshansky
When it comes to biocomputing nothing beats humans challenged with
“impossible” problems and decent amount of time.

~~~
angel_j
Indeed, I bet 4chan could solve this faster than amoeba.

~~~
i_am_nomad
Lower collective IQ though.

~~~
Izkata
Really doesn't seem like it when stuff like The Haruhi Problem pops up:
[https://mobile.twitter.com/robinhouston/status/1054637891085...](https://mobile.twitter.com/robinhouston/status/1054637891085918209?s=21)

------
justicezyx
Good analogy for human to be computing organisms developed unnoticer self
awareness to a higher level beings. Maybe they are now debating if the things
they created really are self-conscious ...

~~~
dleslie
We are computing the answer to the ultimate question.

~~~
module0000
I thought we were computing the _question_ , since the answer is known to be
42.

~~~
jadar
The real question is can the _question_ be solved, or is it undecidable? If
undecidable, how to reduce the Halting Problem to it? ;)

~~~
justicezyx
Now I know why human have a thing called emotions, that must be the randomness
injected by the system!

------
jarmitage
If this interests you, Springer just published an encyclopaedia on
‘Unconventional Computing’, which includes a chapter from Hal Abelson and
Gerry Sussman

[https://link.springer.com/referencework/10.1007%2F978-1-4939...](https://link.springer.com/referencework/10.1007%2F978-1-4939-6883-1)

~~~
mindcrime
Interesting, I never knew there was a conference series on "Unconventional
Computing"[1] until just now.

Sadly they publish their proceedings through Springer, instead of PMLR. That
said, at least some of the older proceedings can be found using the usual
pirate sites if one is so inclined.

[1]: [https://ucnc2018.lacl.fr](https://ucnc2018.lacl.fr)

[2]:
[http://proceedings.mlr.press/index.html](http://proceedings.mlr.press/index.html)

------
black_13
The amoeba will be given H1b status.

------
exabrial
Spoiler, this is not
[https://en.wikipedia.org/wiki/Amoeba_(operating_system)](https://en.wikipedia.org/wiki/Amoeba_\(operating_system\))

~~~
nikofeyn
why would it be?

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keefhowie
What am I missing here? The amoeba reacted predictably to lights turning on
and off. In what way does that constitute problem solving?

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zachguo
Search "evolutionary dynamics" and "complex adaptive system".

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hpcjoe
So ... this is a Turing complete bio-organism? I wonder if we can get them to
solve the towers of Hanoi problem ...

------
xyproto
Isn't this what quantum computing wanted to achieve?

What's next? Amoeba-resistant encryption algorithms?

~~~
QML
Not really: NP-hard problems are a set of a decision problems "does a route
shorter than x exist?" while in quantum computing, we know an answer exists
but the question is obtaining it. E.g. the factors of a number always exist.

------
the_other_guy
Leonard Adleman did lots of research about that 25 years ago. It's now known
as "DNA Computing"

