Quote of the Day

It was only by escaping into the desert that Moses and the Jews were able to solidify their identity and reemerge as a social and political force.Jesus spent his forty days in the wilderness, and Mohammed, too, fled Mecca at a time of great peril for a period of retreat. He and just a handful of his most devoted supporters used this period to deepen their bonds, to understand who they were and what they stood for, to let time work its good. Then this little band of believers reemerged to conquer Mecca and the Arabian Peninsula and later, after Mohammed's death, to defeat the Byzantines and the Persian empire, spreading Islam over vast territories. Around the world every mythology has a hero who retreats, even to Hades itself in the case of Odysseus, to find himself.

- Robert Greene, The 33 Strategies of War

Quote of the Day

People travel to wonder at the height of mountains, at the huge waves of the sea, at the long courses of rivers, at the vast compass of the ocean, at the circular motion of the stars; and they pass by themselves without wondering.

- St Augustine

Quote of the Day

When you meet someone better than yourself, turn your thoughts to becoming his equal. When you meet someone not as good as you are, look within and examine yourself.

- Confucius

Single Artificial Neuron Taught to Recognize Hundreds of Patterns

Thanks to the work of Jeff Hawkins and Subutai Ahmad at Numenta, a Silicon Valley startup focused on understanding and exploiting the principles behind biological information processing. The breakthrough these guys have made is to come up with a new theory that finally explains the role of the vast number of synapses in real neurons and to create a model based on this theory that reproduces many of the intelligent behaviors of real neurons.

Real neurons consist of a cell body, known as the soma, that contains the cell nucleus and from which extend a number of nearby, or proximal, dendrites as well as the axon, a fine cable-like projection that can extend many centimeters to connect to other neurons. At the end of the axon are another set of branches, known as distal dendrites because of their distance from the soma..

Proximal and distal dendrites all make thousands connections, called synapses, to the axons of other nerve cells. These connections famously influence the rate at which the nerve cell produces electrical signals known as spikes..

The consensus is that neurons “learn” by recognizing certain patterns of connections among its synapses and  fire when they see this pattern..

But while it’s easy to understand how proximal synapses can influence the cell body and the rate of firing, it’s hard to understand how distal synapses can do the same thing, because they are so far away..

Hawkins and Ahmad now say they know what’s going on. Their new idea is that distal and proximal synapses play entirely different roles in the process of learning. Proximal synapses play the conventional role of triggering the cell to fire when certain patterns of connections crop up..

This is the conventional process of learning. “We show that a neuron can recognize hundreds of patterns even in the presence of large amounts of noise and variability as long as overall neural activity is sparse,” say Hawkins and Ahmad..

But distal synapses do something else. They also recognize when certain patterns are present, but do not trigger firing. Instead, they influence the electric state of the cell in a way that makes firing more likely if another specific pattern occurs. So distal synapses prepare the cell for the arrival of other patterns. Or, as Hawkins and Ahmad put it, these synapses help the cell predict what the next pattern sensed by the proximal synapses will be..

That’s hugely important. It means that in addition learning when a specific pattern is present, the cell also learns the sequence in which patterns appear. “We show how a network of neurons with this property will learn and recall sequences of patterns,” they say..

What’s more, they show that all this works well, even in the presence of large amounts of noise, as is always the case in biological systems..

That’s a significant new way of thinking about neurons and one that reproduces some of the key features of information processing in the human brain. For example, Hawkins and Ahmad show that this system doesn’t remember every detail of every pattern in a sequence but instead stores the difference between one pattern and the next..

So what’s important is not the total amount of information in a pattern but the difference between this pattern and the next.

- More Here

GPS Always Overestimates Distances

If you make a measurement and it is subject to a random unbiased error then you generally are safe in assuming that the random component will make the quantity larger as often as it makes it smaller. This is how it seems to be with GPS  there are errors in positioning that are inherent in the system but certainly don't show any particular bias. Given this observation you would expect the distance between two points located with unbiased random error would also be unbiased, i.e. it would be on average bigger as often as it was smaller than the true value.

However, you would be wrong.

Researchers at the University of Salzburg (UoS), Salzburg Forschungsgesellchaft (SFG), and the Delft University of Technology have done some fairly simple calculations that prove that this is not the case. Irrespective of the distribution of the errors, the expected measured length squared between two points is bigger than the true length squared unless the errors at both points are identical.
That is, if you have two points p1 and p2 and errors in measuring x and y at each, the squared distance measured between them will come out as bigger than the true distance unless the errors are such that they move both points by the same amount - which is highly unlikely in practice.
 
How can this be?

Consider the two points and the straight line between them. This straight line is the shortest distance between the two points. Now consider random displacements of the two points. The only displacements that reduce the distance are those that move the two points closer together, for example displacements along the line towards each other. The majority of random displacements end up increasing the distance.
 
This is the reason that unbiased errors end up biasing the distance measurement.
So given that the GPS path is just a sum of distances computed between pairs of points, the total estimated distance is going to be bigger than the true distance because of random errors. A little more work and the researchers derive a formula for how much of an Over Estimate of Distance OED is produced:
 
OED= (d² + var - C)^1/2 - d

where var is the variance in the GPS position and C is the autocovariance (correlation) between the errors. Notice that the more correlated the errors, the smaller the over estimate.

- More Here

Quote of the Day

Be more concerned with your character than your reputation, because your character is what you really are, while your reputation is merely what others think you are.

- John Wooden

Analyzing Academic Papers Using Cutting-Edge AI to Find Meaning in Billions of Words

The Allen Institute for Artificial Intelligence is working toward this very goal, and has developed a new tool called Semantic Scholar that can search through millions of computer science papers. The tool, launched today, features ways of refining searches based on information extracted from the text of papers.
 
It is, for instance, possible to narrow a search according to the journal in which a paper was published, or the conference at which it was presented, or by the data set used. Semantic scholar will also show key phrases in a paper.
 
Many academic search engines already exist, among them Google Scholar, Microsoft Academic Search, PubMed, and JSTOR. But these typically only search through papers using keywords and other information that is clearly categorized, such as the publication date.
 
Oren Etzioni, executive director of the Allen Institute, says a lot of pertinent information found in research papers is presented in different ways. The software behind Semantic Scholar was trained to extract different concepts using a variety of machine-learning techniques. “With millions of papers appearing every year, you just can’t keep up with them,” Etzioni says. “So you need some level of understanding.”


- More Here

Quote of the Day

To accuse others for one's own misfortunes is a sign of want of education. To accuse oneself shows that one's education has begun. To accuse neither oneself nor others shows that one's education is complete.

- Epictetus

An Instant Classic: Rochet & Tirole, Platform Competition in Two-Sided Markets

The press release announcing that Jean Tirole had been awarded the 2014 Nobel Prize in Economic Sciences noted that he had “made important theoretical research contributions in a number of areas.” One of his most important contributions was the discovery and pioneering analysis of multi-sided platforms in his 2003 paper with Jean-Charles Rochet, Platform Competition in Two-Sided Markets. According to Google Scholar, this paper has been cited over 1800 times, fourth among Jean’s many papers.

The Rochet &Tirole paper has spawned an enormous literature in a very short time—over 200 papers by the end of 2012, and the economics of multi-sided platforms is now a standard component of graduate courses in industrial organization. The RT paper is the first post-2000 academic paper to be deemed a classic by Competition Policy International, an honor it richly deserves….

- Full paper here

Quote of the Day

The first step in changing someone’s mind is to know where that mind is.

- William Ury, Getting to Yes with Yourself: And Other Worthy Opponents