Subscribe free to our newsletters via your
. 24/7 Space News .

Subscribe free to our newsletters via your

Success by deception
by Staff Writers
Zurich, Switzerland (SPX) Feb 14, 2017

File image.

When computers independently identify bodies of water and their outlines in satellite images, or beat the world's best professional players at the board game Go, then adaptive algorithms are working in the background. Programmers supply these algorithms with known examples in a training phase: images of bodies of water and land, or sequences of Go moves that have led to success or failure in tournaments.

Similarly to how our brain nerve cells produce new networks during learning processes, the special algorithms adapt in the learning phase based on the examples presented to them. This continues until they are able to differentiate bodies of water from land in unknown photos, or successful sequences of moves from unsuccessful ones. Until now, these artificial neural networks have been used in machine learning with a known decision-making criterion: we know what a body of water is and which sequences of moves were successful in Go tournaments.

Separating wheat from chaff
Now, a group of scientists working under Sebastian Huber, Professor of Condensed Matter Theory and Quantum Optics at ETH Zurich, have expanded the applications for these neural networks by developing a method that not only allows categorisation of any data, but also recognises whether complex datasets contain categories at all.

Questions of this kind arise in science: for example, the method could be useful for analysis of measurements from particle accelerators or astronomical observations. Physicists could thus filter out the most promising measurements from their often unmanageable quantities of measurement data. Pharmacologists could extract molecules with a certain probability of having a specific pharmaceutical effect or side-effect from large molecular databases. And data scientists could sort huge masses of disordered data ripples and obtain usable information (data mining).

Search for a boundary
The ETH researchers applied their method to an intensively researched phenomenon of theoretical physics: a many-body system of interacting magnetic dipoles that never reaches a state of equilibrium - even in the long term. Such systems have been described recently, but it is not yet known in detail which quantum physical properties prevent a many-body system from entering a state of equilibrium. In particular, it is unclear where exactly the boundary lies between systems that reach equilibrium and those that do not.

In order to locate this boundary, the scientists developed the "act as if" principle: taking data from quantum systems, they established an arbitrary boundary based on one parameter and used it to divide the data into two groups. They then trained an artificial neural network by pretending to it that one group reached a state of equilibrium while the other did not. Thus, the researchers acted as if they knew where the boundary was.

Scientists confused the system
They trained the network countless times overall, with a different boundary each time, and tested the network's ability to sort data after each session. The result was that, in many cases, the network struggled to classify the data as the scientists had. But in some cases, the division into the two groups was very accurate.

The researchers were able to show that this sorting performance depends on the location of the boundary. Evert van Nieuwenburg, a doctoral student in Huber's group, explains this as follows: "By choosing to train with a boundary far away from the actual boundary (which I don't know), I am able to mislead the network. Ultimately we're training the network incorrectly - and incorrectly trained networks are very bad at classifying data." However, if by chance a boundary is chosen close to the actual boundary, a highly efficient algorithm is produced. By determining the algorithm's performance, the researchers were able to track down the boundary between quantum systems that reach equilibrium and those that do not: the boundary is located where the network's sorting performance is highest.

The researchers also demonstrated the capabilities of their new method using two further questions from theoretical physics: topological phase transitions in one-dimensional solids and the Ising model, which describes magnetism inside solids.

Categorisation without prior knowledge
The new method can also be illustrated in simplified form with a thought experiment, where we want to classify red, reddish, bluish and blue balls into two groups. We assume that we have no idea of how such a classification might reasonably look.

If a neural network is trained by telling it that the dividing line lies somewhere in the red region, then this will confuse the network. "You try to teach the network that blue and reddish balls are the same and ask it to differentiate between red and red balls, which it simply isn't able to do," says Huber.

On the other hand, if you place the boundary in the violet colour spectrum, the network learns an actual difference and sorts the balls into red and blue groups. However, one does not need to know in advance that the dividing line should be in the violet region. By comparing the sorting performance at a variety of chosen boundaries, this boundary can be found with no prior knowledge.

van Nieuwenburg EPL, Liu YH, Huber SD: Learning phase transitions by confusion. Nature Physics, 13 February 2017, doi: 10.1038/nphys4037

Comment on this article using your Disqus, Facebook, Google or Twitter login.

Thanks for being here;
We need your help. The SpaceDaily news network continues to grow but revenues have never been harder to maintain.

With the rise of Ad Blockers, and Facebook - our traditional revenue sources via quality network advertising continues to decline. And unlike so many other news sites, we don't have a paywall - with those annoying usernames and passwords.

Our news coverage takes time and effort to publish 365 days a year.

If you find our news sites informative and useful then please consider becoming a regular supporter or for now make a one off contribution.

SpaceDaily Contributor
$5 Billed Once

credit card or paypal
SpaceDaily Monthly Supporter
$5 Billed Monthly

paypal only


Related Links
ETH Zurich
All about the robots on Earth and beyond!

Share this article via these popular social media networks DiggDigg RedditReddit GoogleGoogle

Previous Report
Algorithms: the managers of our digital lives
Paris (AFP) Feb 11, 2017
Algorithms are a crucial cog in the mechanics of our digital world, but also a nosy minder of our personal lives and a subtle, even insidious influence on our behaviour. They have also come to symbolise the risks of a computerised world conditioned by commercial factors. - A gift from a Persian scientist - Long before they were associated with Google searches, Facebook pages and Amaz ... read more

Endurance athletes: Swig mouthwash for improved performance

Looking to the future: Russia, US mull post-ISS cooperation in space

Progress Underway for First Commercial Airlock on Space Station

A new recruit for ESA's astronaut corps

Airbus Safran Launchers: 77th consecutive successful launch for Ariane 5

India puts record 104 satellites into orbit

SpaceX Falcon 9 rocket vertical at Florida's Kennedy Space Center

India to launch record 104 satellites next week

ISRO saves its Mars mission spacecraft from eclipse

Mars Reconnaissance Orbiter plays crucial role in search for landing sites

Angling up for Mars science

Swirling spirals at the north pole of Mars

Chinese cargo spacecraft set for liftoff in April

China looks to Mars, Jupiter exploration

China's first cargo spacecraft to leave factory

China launches commercial rocket mission Kuaizhou-1A

NASA seeks partnerships with US companies to advance commercial space technologies

A New Space Paradigm

Why it's time for Australia to launch its own space agency

Government announces boost for UK commercial space sector

NASA and MIT collaborate to develop space-based quantum-dot spectrometer

NASA's TDRS-M space communications satellite begins final testing

Lasers could give space research its broadband moment

Terahertz chips a new way of seeing through matter

Possibility of Silicon-Based Life Grows

NASA finds planets of red dwarf stars may face oxygen loss in habitable zones

Dwarf star 200 light years away contains life's building blocks

Santa Fe Institute researchers look for life's lower limits

NASA receives science report on Europa lander concept

New Horizons Refines Course for Next Flyby

It's Never 'Groundhog Day' at Jupiter

Public to Choose Jupiter Picture Sites for NASA Juno

Memory Foam Mattress Review
Newsletters :: SpaceDaily :: SpaceWar :: TerraDaily :: Energy Daily
XML Feeds :: Space News :: Earth News :: War News :: Solar Energy News

The content herein, unless otherwise known to be public domain, are Copyright 1995-2017 - Space Media Network. All websites are published in Australia and are solely subject to Australian law and governed by Fair Use principals for news reporting and research purposes. AFP, UPI and IANS news wire stories are copyright Agence France-Presse, United Press International and Indo-Asia News Service. ESA news reports are copyright European Space Agency. All NASA sourced material is public domain. Additional copyrights may apply in whole or part to other bona fide parties. All articles labeled "by Staff Writers" include reports supplied to Space Media Network by industry news wires, PR agencies, corporate press officers and the like. Such articles are individually curated and edited by Space Media Network staff on the basis of the report's information value to our industry and professional readership. Advertising does not imply endorsement, agreement or approval of any opinions, statements or information provided by Space Media Network on any Web page published or hosted by Space Media Network. Privacy Statement