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Single photon decision-maker solves multi-armed bandit problem

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  Single photon decision-maker solves multi-armed bandit problem Sam Wormley <swormley1@gmail.com> - 2015-09-17 16:25 -0500
    Re: Single photon decision-maker solves multi-armed bandit problem jimp@specsol.spam.sux.com - 2015-09-17 21:46 +0000
      Re: Single photon decision-maker solves multi-armed bandit problem Sam Wormley <swormley1@gmail.com> - 2015-09-17 21:07 -0500
        Re: Single photon decision-maker solves multi-armed bandit problem jimp@specsol.spam.sux.com - 2015-09-18 03:14 +0000
          Re: The ass hat spams some more jimp@specsol.spam.sux.com - 2015-09-18 17:46 +0000
        Re: Single photon decision-maker solves multi-armed bandit problem "reber g=emc^2" <herbertglazier0@gmail.com> - 2015-09-18 11:43 -0700
          Re: Single photon decision-maker solves multi-armed bandit problem benj <nobody@gmail.com> - 2015-09-18 14:48 -0400
          Re: Single photon decision-maker solves multi-armed bandit problem Double-A <double-a3@hush.com> - 2015-09-18 16:29 -0700
            Re: Single photon decision-maker solves multi-armed bandit problem "reber g=emc^2" <herbertglazier0@gmail.com> - 2015-09-20 09:47 -0700
    Re: Single photon decision-maker solves multi-armed bandit problem "Y.Porat" <y.y.porat@gmail.com> - 2015-09-19 01:55 -0700
      Re: Single photon decision-maker solves multi-armed bandit problem "Oliver Jacob Bacon" <invalid@example.com> - 2015-09-19 07:58 -0700
    Re: Single photon decision-maker solves multi-armed bandit problem "Y.Porat" <y.y.porat@gmail.com> - 2015-09-19 01:57 -0700
      Re: Single photon decision-maker solves multi-armed bandit problem "Oscar Jazz Bottom" <invalid@example.com> - 2015-09-19 07:57 -0700
        Re: Single photon decision-maker solves multi-armed bandit problem "Y.Porat" <y.y.porat@gmail.com> - 2015-09-20 01:21 -0700

#521715 — Single photon decision-maker solves multi-armed bandit problem

Single photon decision-maker solves multi-armed bandit problem
> http://phys.org/news/2015-09-photon-decision-maker-multi-armed-bandit-problem.html
> http://cdn.phys.org/newman/csz/news/800/2015/55fac55493e54.jpg


> (Phys.org)—A combined team of researchers from France and Japan has
> created a decision-making device that is based on basic properties of
> quantum mechanics. In their paper published in Scientific Reports
> (and uploaded to the arXiv preprint server), the team describes the
> idea behind their device and how it works.

> There is a classic decision-making problem that is known as the
> exploration-exploitation dilemma—it is typically described by
> suggesting a scenario where a gambler faced with a floor full of slot
> machines must decide which offers the best payout on a regular basis.
> In real life, the solution involves feeding all of the machines coins
> until a discernible pattern emerges.


>
> More information: Single-photon decision maker, Scientific Reports 5,
> Article number: 13253 (2015) DOI: 10.1038/srep13253 . On Arxiv:
> http://arxiv.org/abs/1509.00638

> Abstract

> Decision making is critical in our daily lives and for society in
> general and is finding evermore practical applications in information
> and communication technologies. Herein, we demonstrate experimentally
> that single photons can be used to make decisions in uncertain,
> dynamically changing environments. Using a nitrogen-vacancy in a
> nanodiamond as a single-photon source, we demonstrate the
> decision-making capability by solving the multi-armed bandit problem.
> This capability is directly and immediately associated with
> single-photon detection in the proposed architecture, leading to
> adequate and adaptive autonomous decision making. This study makes it
> possible to create systems that benefit from the quantum nature of
> light to perform practical and vital intelligent functions.
>
>
> Read more at:
> http://phys.org/news/2015-09-photon-decision-maker-multi-armed-bandit-problem.html#jCp

-- 

sci.physics is an unmoderated newsgroup dedicated
to the discussion of physics, news from the physics
community, and physics-related social issues.

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#521730

Sam Wormley <swormley1@gmail.com> wrote:
> Single photon decision-maker solves multi-armed bandit problem
 
>> There is a classic decision-making problem that is known as the
>> exploration-exploitation dilemma?it is typically described by
>> suggesting a scenario where a gambler faced with a floor full of slot
>> machines must decide which offers the best payout on a regular basis.
>> In real life, the solution involves feeding all of the machines coins
>> until a discernible pattern emerges.

In real life, the house sets the payouts based on how much attention
it will get, shit head.

-- 
Jim Pennino

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#521756

   Review for the jimp | Multi-armed bandit
> https://en.wikipedia.org/wiki/Multi-armed_bandit

> In probability theory, the multi-armed bandit problem (sometimes
> called the K-[1] or N-armed bandit problem[2]) is a problem in which
> a gambler at a row of slot machines (sometimes known as "one-armed
> bandits") has to decide which machines to play, how many times to
> play each machine and in which order to play them.[3] When played,
> each machine provides a random reward from a distribution specific to
> that machine. The objective of the gambler is to maximize the sum of
> rewards earned through a sequence of lever pulls.[4][5]
>
> Robbins in 1952, realizing the importance of the problem, constructed
> convergent population selection strategies in "some aspects of the
> sequential design of experiments".[6]
>
> A theorem, the Gittins index published first by John C. Gittins gives
> an optimal policy in the Markov setting for maximizing the expected
> discounted reward.[7]
>
> In practice, multi-armed bandits have been used to model the problem
> of managing research projects in a large organization, like a science
> foundation or a pharmaceutical company. Given a fixed budget, the
> problem is to allocate resources among the competing projects, whose
> properties are only partially known at the time of allocation, but
> which may become better understood as time passes.[4][5]
>
> In early versions of the multi-armed bandit problem, the gambler has
> no initial knowledge about the machines. The crucial tradeoff the
> gambler faces at each trial is between "exploitation" of the machine
> that has the highest expected payoff and "exploration" to get more
> information about the expected payoffs of the other machines. The
> trade-off between exploration and exploitation is also faced in
> reinforcement learning.


-- 

sci.physics is an unmoderated newsgroup dedicated
to the discussion of physics, news from the physics
community, and physics-related social issues.

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#521770

Sam Wormley <swormley1@gmail.com> wrote:
> 
> 
>   Review for the jimp

Review for the worthless, piece of shit cut and paste, shit head

You are still a worthless, piece of shit cut and paste, spamming
shit head.


-- 
Jim Pennino

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#521824 — Re: The ass hat spams some more

Sam Wormley <swormley1@gmail.com> wrote:
> On 9/17/15 10:14 PM, jimp@specsol.spam.sux.com wrote:
>>
>> Review for the worthless, piece of shit cut and paste, shit head
>>
>> You are still a worthless, piece of shit cut and paste, spamming
>> shit head.
>>
>>
> 
> 
> For the jimp:

For the ass hat shit head spammer: 

Fuck off and die.

Apparently the spamming shit head is too ignorant to make any real
comment on my response about the real world.


-- 
Jim Pennino

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#521839

On Thursday, September 17, 2015 at 7:07:16 PM UTC-7, Sam Wormley wrote:
> Review for the jimp | Multi-armed bandit
> > https://en.wikipedia.org/wiki/Multi-armed_bandit
> 
> > In probability theory, the multi-armed bandit problem (sometimes
> > called the K-[1] or N-armed bandit problem[2]) is a problem in which
> > a gambler at a row of slot machines (sometimes known as "one-armed
> > bandits") has to decide which machines to play, how many times to
> > play each machine and in which order to play them.[3] When played,
> > each machine provides a random reward from a distribution specific to
> > that machine. The objective of the gambler is to maximize the sum of
> > rewards earned through a sequence of lever pulls.[4][5]
> >
> > Robbins in 1952, realizing the importance of the problem, constructed
> > convergent population selection strategies in "some aspects of the
> > sequential design of experiments".[6]
> >
> > A theorem, the Gittins index published first by John C. Gittins gives
> > an optimal policy in the Markov setting for maximizing the expected
> > discounted reward.[7]
> >
> > In practice, multi-armed bandits have been used to model the problem
> > of managing research projects in a large organization, like a science
> > foundation or a pharmaceutical company. Given a fixed budget, the
> > problem is to allocate resources among the competing projects, whose
> > properties are only partially known at the time of allocation, but
> > which may become better understood as time passes.[4][5]
> >
> > In early versions of the multi-armed bandit problem, the gambler has
> > no initial knowledge about the machines. The crucial tradeoff the
> > gambler faces at each trial is between "exploitation" of the machine
> > that has the highest expected payoff and "exploration" to get more
> > information about the expected payoffs of the other machines. The
> > trade-off between exploration and exploitation is also faced in
> > reinforcement learning.
> 
> 
> -- 
> 
> sci.physics is an unmoderated newsgroup dedicated
> to the discussion of physics, news from the physics
> community, and physics-related social issues.

Sam Slots give back a 97.6 return.Time makes house big winner. TreBert

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#521840

On 09/18/2015 02:43 PM, reber g=emc^2 wrote:
> On Thursday, September 17, 2015 at 7:07:16 PM UTC-7, Sam Wormley wrote:
>> Review for the jimp | Multi-armed bandit
>>> https://en.wikipedia.org/wiki/Multi-armed_bandit
>>
>>> In probability theory, the multi-armed bandit problem (sometimes
>>> called the K-[1] or N-armed bandit problem[2]) is a problem in which
>>> a gambler at a row of slot machines (sometimes known as "one-armed
>>> bandits") has to decide which machines to play, how many times to
>>> play each machine and in which order to play them.[3] When played,
>>> each machine provides a random reward from a distribution specific to
>>> that machine. The objective of the gambler is to maximize the sum of
>>> rewards earned through a sequence of lever pulls.[4][5]
>>>
>>> Robbins in 1952, realizing the importance of the problem, constructed
>>> convergent population selection strategies in "some aspects of the
>>> sequential design of experiments".[6]
>>>
>>> A theorem, the Gittins index published first by John C. Gittins gives
>>> an optimal policy in the Markov setting for maximizing the expected
>>> discounted reward.[7]
>>>
>>> In practice, multi-armed bandits have been used to model the problem
>>> of managing research projects in a large organization, like a science
>>> foundation or a pharmaceutical company. Given a fixed budget, the
>>> problem is to allocate resources among the competing projects, whose
>>> properties are only partially known at the time of allocation, but
>>> which may become better understood as time passes.[4][5]
>>>
>>> In early versions of the multi-armed bandit problem, the gambler has
>>> no initial knowledge about the machines. The crucial tradeoff the
>>> gambler faces at each trial is between "exploitation" of the machine
>>> that has the highest expected payoff and "exploration" to get more
>>> information about the expected payoffs of the other machines. The
>>> trade-off between exploration and exploitation is also faced in
>>> reinforcement learning.
>>
>>
>> --
>>
>> sci.physics is an unmoderated newsgroup dedicated
>> to the discussion of physics, news from the physics
>> community, and physics-related social issues.
>
> Sam Slots give back a 97.6 return.Time makes house big winner. TreBert
>
Sam, Herb knows all about the mathematics of gambling. He knows that you 
can only win when Treb gives you football game outcome. with all other 
the house is the winner. Listen to Treebert! He thinks. YOu don't.

-- 
         ___           ___           ___            ___
        /\  \         /\  \         /\__\          /\  \
       /::\  \       /::\  \       /::|  |         \:\  \
      /:/\:\  \     /:/\:\  \     /:|:|  |     ___ /::\__\
     /::\~\:\__\   /::\~\:\  \   /:/|:|  |__  /\  /:/\/__/
    /:/\:\ \:|__| /:/\:\ \:\__\ /:/ |:| /\__\ \:\/:/  /
    \:\~\:\/:/  / \:\~\:\ \/__/ \/__|:|/:/  /  \::/  /
     \:\ \::/  /   \:\ \:\__\       |:/:/  /    \/__/
      \:\/:/  /     \:\ \/__/       |::/  /
       \_:/__/       \:\__\         /:/  /
                      \/__/         \/__/

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#521917

On Friday, September 18, 2015 at 11:43:05 AM UTC-7, reber g=emc^2 wrote:
> On Thursday, September 17, 2015 at 7:07:16 PM UTC-7, Sam Wormley wrote:
> > Review for the jimp | Multi-armed bandit
> > > https://en.wikipedia.org/wiki/Multi-armed_bandit
> > 
> > > In probability theory, the multi-armed bandit problem (sometimes
> > > called the K-[1] or N-armed bandit problem[2]) is a problem in which
> > > a gambler at a row of slot machines (sometimes known as "one-armed
> > > bandits") has to decide which machines to play, how many times to
> > > play each machine and in which order to play them.[3] When played,
> > > each machine provides a random reward from a distribution specific to
> > > that machine. The objective of the gambler is to maximize the sum of
> > > rewards earned through a sequence of lever pulls.[4][5]
> > >
> > > Robbins in 1952, realizing the importance of the problem, constructed
> > > convergent population selection strategies in "some aspects of the
> > > sequential design of experiments".[6]
> > >
> > > A theorem, the Gittins index published first by John C. Gittins gives
> > > an optimal policy in the Markov setting for maximizing the expected
> > > discounted reward.[7]
> > >
> > > In practice, multi-armed bandits have been used to model the problem
> > > of managing research projects in a large organization, like a science
> > > foundation or a pharmaceutical company. Given a fixed budget, the
> > > problem is to allocate resources among the competing projects, whose
> > > properties are only partially known at the time of allocation, but
> > > which may become better understood as time passes.[4][5]
> > >
> > > In early versions of the multi-armed bandit problem, the gambler has
> > > no initial knowledge about the machines. The crucial tradeoff the
> > > gambler faces at each trial is between "exploitation" of the machine
> > > that has the highest expected payoff and "exploration" to get more
> > > information about the expected payoffs of the other machines. The
> > > trade-off between exploration and exploitation is also faced in
> > > reinforcement learning.
> > 
> > 
> > -- 
> > 
> > sci.physics is an unmoderated newsgroup dedicated
> > to the discussion of physics, news from the physics
> > community, and physics-related social issues.
> 
> Sam Slots give back a 97.6 return.Time makes house big winner. TreBert


Oregon's lottery run video poker machines give a poorer payout, more like only
90%.  The money goes really fast.  Widows lose their homes and children starve!

Double-A

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#522227

On Friday, September 18, 2015 at 4:29:12 PM UTC-7, Double-A wrote:
> On Friday, September 18, 2015 at 11:43:05 AM UTC-7, reber g=emc^2 wrote:
> > On Thursday, September 17, 2015 at 7:07:16 PM UTC-7, Sam Wormley wrote:
> > > Review for the jimp | Multi-armed bandit
> > > > https://en.wikipedia.org/wiki/Multi-armed_bandit
> > > 
> > > > In probability theory, the multi-armed bandit problem (sometimes
> > > > called the K-[1] or N-armed bandit problem[2]) is a problem in which
> > > > a gambler at a row of slot machines (sometimes known as "one-armed
> > > > bandits") has to decide which machines to play, how many times to
> > > > play each machine and in which order to play them.[3] When played,
> > > > each machine provides a random reward from a distribution specific to
> > > > that machine. The objective of the gambler is to maximize the sum of
> > > > rewards earned through a sequence of lever pulls.[4][5]
> > > >
> > > > Robbins in 1952, realizing the importance of the problem, constructed
> > > > convergent population selection strategies in "some aspects of the
> > > > sequential design of experiments".[6]
> > > >
> > > > A theorem, the Gittins index published first by John C. Gittins gives
> > > > an optimal policy in the Markov setting for maximizing the expected
> > > > discounted reward.[7]
> > > >
> > > > In practice, multi-armed bandits have been used to model the problem
> > > > of managing research projects in a large organization, like a science
> > > > foundation or a pharmaceutical company. Given a fixed budget, the
> > > > problem is to allocate resources among the competing projects, whose
> > > > properties are only partially known at the time of allocation, but
> > > > which may become better understood as time passes.[4][5]
> > > >
> > > > In early versions of the multi-armed bandit problem, the gambler has
> > > > no initial knowledge about the machines. The crucial tradeoff the
> > > > gambler faces at each trial is between "exploitation" of the machine
> > > > that has the highest expected payoff and "exploration" to get more
> > > > information about the expected payoffs of the other machines. The
> > > > trade-off between exploration and exploitation is also faced in
> > > > reinforcement learning.
> > > 
> > > 
> > > -- 
> > > 
> > > sci.physics is an unmoderated newsgroup dedicated
> > > to the discussion of physics, news from the physics
> > > community, and physics-related social issues.
> > 
> > Sam Slots give back a 97.6 return.Time makes house big winner. TreBert
> 
> 
> Oregon's lottery run video poker machines give a poorer payout, more like only
> 90%.  The money goes really fast.  Widows lose their homes and children starve!
> 
> Double-A

I love to play poker but not with a machine.People that play chess can't win against a computer.I stop playing lotto I had a hope Treb might give me 5 # 6 would be asking to much.I will by 50 tickets for super ball if he gives me a nod. TreBert

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#521970

On Friday, September 18, 2015 at 12:25:39 AM UTC+3, Sam Wormley wrote:
> Single photon decision-maker solves multi-armed bandit problem
> > http://phys.org/news/2015-09-photon-decision-maker-multi-armed-bandit-problem.html
> > http://cdn.phys.org/newman/csz/news/800/2015/55fac55493e54.jpg
> 
> 
> > (Phys.org)--A combined team of researchers from France and Japan has
> > created a decision-making device that is based on basic properties of
> > quantum mechanics. In their paper published in Scientific Reports
> > (and uploaded to the arXiv preprint server), the team describes the
> > idea behind their device and how it works.
> 
> > There is a classic decision-making problem that is known as the
> > exploration-exploitation dilemma--it is typically described by
> > suggesting a scenario where a gambler faced with a floor full of slot
> > machines must decide which offers the best payout on a regular basis.
> > In real life, the solution involves feeding all of the machines coins
> > until a discernible pattern emerges.
> 
> 
> >
> > More information: Single-photon decision maker, Scientific Reports 5,
> > Article number: 13253 (2015) DOI: 10.1038/srep13253 . On Arxiv:
> > http://arxiv.org/abs/1509.00638
> 
> > Abstract
> 
> > Decision making is critical in our daily lives and for society in
> > general and is finding evermore practical applications in information
> > and communication technologies. Herein, we demonstrate experimentally
> > that single photons can be used to make decisions in uncertain,
> > dynamically changing environments. Using a nitrogen-vacancy in a
> > nanodiamond as a single-photon source, we demonstrate the
> > decision-making capability by solving the multi-armed bandit problem.
> > This capability is directly and immediately associated with
> > single-photon detection in the proposed architecture, leading to
> > adequate and adaptive autonomous decision making. This study makes it
> > possible to create systems that benefit from the quantum nature of
> > light to perform practical and vital intelligent functions.
> >
> >
> > Read more at:
> > http://phys.org/news/2015-09-photon-decision-maker-multi-armed-bandit-problem.html#jCp
> 
 ================================
imbecile parrots !!
you   can never even detect a single photon
what you can do is detecting 
****A HUGE BUNDLE OF SINGLE PHOTONS*
 !!
BECAUSE THE MASS(THE ONLY MASS ) OF THE REAL SINGLE PHOTON IS 
ABOUT  EX -90 KILOGRAM !!!

GOT IT ONCE AND FOR ALL -BLOCKHEADS ???

Y.Porat
=============================================

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#522007

Post shit.

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#521971

On Friday, September 18, 2015 at 12:25:39 AM UTC+3, Sam Wormley wrote:
> Single photon decision-maker solves multi-armed bandit problem
> > http://phys.org/news/2015-09-photon-decision-maker-multi-armed-bandit-problem.html
> > http://cdn.phys.org/newman/csz/news/800/2015/55fac55493e54.jpg
> 
> 
> =======================
On Friday, September 18, 2015 at 12:45:21 AM UTC+3, Sam Wormley wrote:
> How can electrons "jump" between places without covering the intervening 
> distance?
> > http://www.askamathematician.com/2010/08/q-how-can-electrons-jump-between-places-without-covering-the-intervening-distance/
> 
> 
> > Physicist: Frequently in quantum mechanics you'll find that particles
> > are restricted to only a certain set of states or locations, and yet
> > somehow they can move from one to the next. It's like moving between
> > islands without crossing any water.
> 
> > "Classically" (19th century, pre-relativity, pre-quantum) this is
> > impossible. If you see a particle in one place and then see it again,
> > but in a new place, then of course it must have traversed the
> > distance from one to the other.
> 
> > But here's the essential difference between quantum mechanics
> > (correct) and classical physics (wrong): particles aren't solid
> > objects that have a genuine position, instead they're waves that are
> > "smeared out".
> 
> 
> 
> -- 
 ====================================
electrons  do not jump further  or closer to the nuc 
the are located /connected to very specific locations on the nuc 
to different directions 
as 'CHAIN OF ORBITALS '!!!!
(a new revolutionary innovation for itself )

the  revolutionary historic model of Y Porat
see Google :

'the Y Porat Model an abstract '

an abstract of the book:

'A MODEL OF THE ATOM AND NUC '
TIA
Y.Porat
========================================== 

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#522006

Shit post.

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#522172

On Saturday, September 19, 2015 at 5:57:34 PM UTC+3, Oscar Jazz Bottom wrote:
> Shit post.

===========================
anonymous computerized deck P D  hired gangster 
go f your mother !!
next !!
to human beings 
Y.P
===============

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