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Review of Information Mechanics |
The term Information Mechanics was used by the MIT group lead by Charles Bennett, Edward Fredkin, Norman Margolus, and Tommaso Toffoli working since the late eighties in the relations between information and physics. Those scientists contributed enormously to present technology developments in areas ranging from Quantum Physics to Micro-Electronics and to advance the efforts for finding new architectures of processors improving the Von Neumann computing model (beyond parallelism, multiprocessors, GRIDs, pipelines, and key works on compilers able to translate instructions efficiently into the new architectures).
The Billiard ball model was proposed in 1982 in a seminal paper[2] of Edward Fredkin and Tommaso Toffoli of the Massachusetts institute of Technology. They show how it is possible to see a computer as a billiard table in which the billiard balls act as message carriers and their interactions act as logical decisions. In a more recent review of the state-of-the art, InterQuanta 2002 report, the model is seen in general as “carrying the data in particles” and “the processing in their changes after interactions (including collision and reactions)”.
Let us see the power of a billiard-ball computer in the diagram, it shows a billiard-ball logic device consisting of two in-channels that admit balls into a collision chamber and three out-channels. If one and only one ball enters the chamber, from either in-channel, it will leave by either the geometrically opposite out-channel (the upper right or the bottom one). However, when two balls enter the device simultaneously, one of thee two balls exits the device by the out-channel at the lower right. The presence or absence of a ball in this out-channels corresponds with the logical AND function: the output is a ball if, and only if, a ball enters one in-channel and the other one. Furthermore, Fredkin and Toffili highlighted the properties of reversibility and conservation of these devices in opposition with present dissipative electronics. Several works [2006] [2005] are presently following this path of computer modelling.
When the number of objects (such as billiard balls) in a system becomes large, we need new principles like the entropy or temperature relations. And when the multitude of particles are able to react and change (not only in position and momentum) then new behaviours arise. The Amorphous paradigm prepares the engineering principles to observe, control, organize, and exploit the coherent and cooperative behaviour of programmable multitudes. It is a new paradigm of architecture on http://www-swiss.ai.mit.edu/projects/amorphous/ . Together with the Nature-Inspired Computing paradigms [2005] it uses asynchronous and decentralized agents, as in the model of cellular automats.
Presently there are a few research lines related to these kind of models in what it is known as unconventional computing. Unconventional computing is, according to [2007], "an interdisciplinary research area with the main goal to enrich or go beyond the standard models, such as the von Neumann computer architecture and the Turing machine, which have dominated computer science for more than half a century". These methods model their computational operations based on non-standard paradigms, and are currently mostly in the research and development stage. Recent works related to the billiard ball model are the particle-based model [1981] and the reaction and diffusion of chemical species [2001].
The Gray Scott equations model such a reaction to produce a variety of patterns
reminiscent of those often seen in nature. Fig 3 shows the kinds of patterns
obtained by the Gray-Scott model of a chemical reaction. http://www-swiss.ai.mit.edu/projects/amorphous/GrayScott/
Related to this works is also the “Blob machine” which can use cellular automats
produced either with traditional chips or with new less-reliable technologies
using the Amorphous computing paradigm. blob.lri.fr/ portal.acm.org/citation.cfm?id=977091.977111
The term Physics-Based is also used for modeling graphics,
in that sense physics-based modeling involves constructing dynamic models of
animated objects and computing their motions via physical simulation. Physics-based
modeling implies that object motions are governed by the laws of physics, which
leads to physically realistic animation. Moreover, this approach frees the animator
from having to specify many low-level motion details, since motion is synthesized
automatically by the physical simulation. This is evident especially when animating
passive motion (i.e. motions of inanimate objects)--the animator need only supply
the initial state of the object and a physical simulator automatically computes
its motion by integrating the differential equations stemming from Newton's
laws.
The success of physics-based modeling was demonstrated in modeling the movements
of inanimate objects, such as deformable objects [Terzopoulos et al.1987, http://www.dgp.toronto.edu/~tu//thesis/node166.html#Terzopoulos87)
The more recent Particle-Dynamics model relates to the research related to the MIT Information Mechanics group and to others such as the work of Ron Weiss in Amorphous Computing or the Quantum Mechanical Hamiltonian Model of Paul Benioff. The current model has an extended the underlying mathematics to what can be named an "Information Geometry Model". The most recent publications in 2006 provided concrete examples where the particles were applied to newest information technologies, from Mobility to High-Performance, and we are currently expanding on new applications of this Model. To key papers are:
KEY REFERENCES
IMPLICATIONS
In Roger Penrose' book "The Emperor's New Mind", he attacks the claims of artificial intelligence using the physics of computing. He highlights that present computing lies more in the tangible world of classical mechanics than in the imponderable realm of quantum mechanics. Penrose takes a billiard table and Fredking and Toffoli model to show that modem computer is a deterministic system that for the most part simply executes algorithms. There are many other implications of Particle-Base modeling, let us see how solar produces millions of photons and neutrinos per second (figure below), could the thinking process be just doing the analogous with particles of information ?
Note: Image of Proton Proton
Chain reactions in a Star, public at the NDM'06 site, Int'l Conference on Neutrino
and Dark Matter, Thursday 07 Sept 2006, Session 14.
Modeling Information Systems using physics-based laws has practical uses in telecomunications for instance. A 2003 prototype used particles and reactions to create a SIP Software phone able to place calls to most common Voice IP terminal and handsets. It is possible to model, simulate, and program telecom switches not by using variables and procedures but rather object-particles that react, following modeling laws. The models have associated conservation of magnitudes such as “message or call momentum”, analogous to magnitudes like momentum in physics.
Fig. What advantages do we gain by using object particles to program switches or to model systems using physics based laws? The system Symmetry and Conservation laws lead to the creation of particles carrying any missing information, or related magnitude. Thus,
No matter what is the origin of an error, or how unexpected it was, a conservation-driven system sends an alert to equilibrate the conserved magnitude.
This page: Information Mechanics and Unconventional computing, http://www.interquanta.biz/im/
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