Entropy study suggests Pictish symbols likely were part of a written language

How can you tell the difference between random pictures and an ancient, symbol-based language ?

 A new study has shown that concepts in entropy can be used to measure the degree of repetitiveness in Pictish symbols from the Dark Ages, with the results suggesting that the inscriptions appear be much closer to a modern written language than to random symbols 

. The Picts, a group of Celtic tribes that lived in Scotland from around the 4th-9th centuries AD, left behind only a few hundred stones expertly carved with symbols. Although the symbols appear to convey information, it has so far been impossible to prove that this small sample of symbols represents a written language.

Buscan crear super computadoras sin límite de velocidad

Un equipo de investigadores liderados por científicos de la Universidad de Yale ejecutó exitosamente operaciones simples de mecánica cuántica, lo cual podría significar la creación de hardware con potencial ilimitado de procesamiento.

Stephen Wolfram: The Man Who Cracked The Code to Everything ...

..."The climax of the book is the principle of computational equivalence, which may as well be called "Wolfram's law." After hundreds of pages of laying groundwork, presenting case after case of visual examples where simple rules generate counterintuitively complex results, Wolfram concludes that this phenomenon is overwhelmingly commonplace - it's at the base of everything from morphology to traffic jams. Then he goes further, stating that once a system achieves a certain, easily attainable degree of complexity, it's reached the point of maximum complexity, as measured by the computation required to crank out the end result. Everything at that level of complexity - and that means almost everything you can think of, from human thought to rain hitting pavement - is exactly as complex as anything else."...

AAAI Fall Symposium - Nov. 5 - 7, 2009 - Arlington, VA

Complex Adaptive Systems and the Threshold Effect: Views from the Natural and Social Sciences

Most interesting phenomena in natural and social systems include constant transitions and oscillations among their various phases. Wars, companies, societies, markets, and humans rarely stay in a stable, predictable state for long. Randomness, power laws, and human behavior ensure that the future is both unknown and challenging. How do events unfold ? When do they take hold ? Why do some initial events cause an avalanche while others do not ? What characterizes these events ? What are the thresholds that differentiate a sea change from a non-event ?

Complex Adaptive Systems have proven to be a powerful tool for exploring these and other related phenomena. We characterize a general CAS model as having a large number of self-similar agents that:

1. utilize one or more levels of feedback;
2. exhibit emergent properties and self-organization; and
3. produce non-linear dynamic behavior.

Advances in modeling and computing technology have led not only to a deeper understanding of complex systems in many areas, but they have also raised the possibility that similar fundamental principles may be at work across these systems, even though the underlying principles may manifest themselves differently.

Patrick Cox: The Quantum Leap of Quantum Computing

The electronics and computing industries are getting primed for a massive transformation in the years ahead. Quantum technologies that were only theories in scientific journals just a few years ago are being prototyped in labs now. These new components will change the way we live forever. They will also create transformational profit opportunities.

How to Avoid Yourself

Every Sunday morning you go for a walk in the city, heading nowhere in particular, with just one rule to your rambling: You never retrace your steps or cross your own path. If you have already walked along a certain block or passed through an intersection, you refuse to set foot there again.

This recipe for tracing a loopless path through a grid of city streets leads into some surprisingly dark back alleys of mathematics—not to mention byways of physics, chemistry, computer science and biology. Avoiding yourself, it turns out, is a hard problem. The exact analysis of self-avoiding walks has stumped mathematicians for half a century; even counting the walks is a challenge.

My own initiation into the trials of self-avoidance came when I began experimenting with a simple model of the folding of protein molecules, a story I told in an earlier "Computing Science" column (see Hayes 1998). Protein folding is close to the historical roots of the self-avoiding walk, which was first conceived as a tool for understanding the geometry of long-chain polymer molecules. A polymer writhing and wriggling in solution forms a random tangle—random, that is, except that no two atoms can occupy the same position at the same time. This "excluded volume effect" in the polymer is modeled by the walk's insistence on avoiding itself.

Fuzzy Math

Have the votes for president been properly counted in Florida ?. On the surface, that's a question of simple math. But beneath the number crunching, Republicans and Democrats are waging a war of disguised biases. When data don't turn out the way your theory predicts, should you question the theory or the data ?. When a new vote tally contradicts an old one, should you distrust the first count or the second ?. When one kind of recount is more evenhanded but another is more comprehensive, which is better ?. These dilemmas form the hidden crux of the debate over whether to recount Florida's ballots by hand, as Democrats prefer, or to rely on a machine recount, as Republicans prefer. The two parties aren't being candid about these questions. And math won't answer them.

Guilt by Calculation

This kind of statistical gumshoeing has a long history. In 1936, for example, English biologist and statistician R. A. Fisher went gunning for Gregor Mendel, whose experimental results Fisher believed had been tweaked to be more favorable to Mendel's ideas. "Fictitious data can seldom survive a careful scrutiny," Fisher wrote, "and, since most men underestimate the frequency of large deviations arising by chance, such data may be expected generally to agree more closely with expectation than genuine data would." In other words, it was precisely the beautiful agreement of experiment with theory that exposed Mendel's thumb on the scale. Only once in 15,000 times, Fisher computed, could one expect such strong conformity. ( The controversy over Mendel's research practices continues to this day, with notable scientists lining up on both men's sides ).

Quantum communication: When 0 + 0 is not equal to 0

One of the lesser known cornerstones of modern physics is Claude Shannon’s Mathematical Theory of Communication which he published in 1948 while juggling and unicycling his way around Bell Labs. Shannon’s theory concerns how a message created at one point in space can be reproduced at another point in space. He calls the conduit for such a process a channel and the limits imposed by the universe on this process the channel capacity. The capacity of a communications channel is hugely important idea. It tells you, among other things, the rate at which you can send information from one location to another, without loss. If you’ve ever made a phone call, watched television or surfed the internet you’ll have benefited from the work associated with this idea. In recent years, our ideas about communication have been transformed by the possibility of using quantum particles to carry information. When that happens the strange rules of quantum mechanics govern what can and cannot be sent from one region of space to another. This kind of thinking has has spawned the entirely new fields of quantum communication and quantum computing.