Herself’s Artificial Intelligence

Humans, meet your replacements.

Archive for November, 2007

Neural network trained to recognize 3d scenes falls for same optical illusions as do people

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Lightness illusions are fundamental to human perception, and yet why we see them is still the focus of much research. Here we address the question by modelling not human physiology or perception directly as is typically the case but our natural visual world and the need for robust behaviour. Artificial neural networks were trained to predict the reflectance of surfaces in a synthetic ecology consisting of 3-D “dead-leaves” scenes under non-uniform illumination. The networks learned to solve this task accurately and robustly given only ambiguous sense data. In addition—and as a direct consequence of their experience—the networks also made systematic “errors” in their behaviour commensurate with human illusions, which includes brightness contrast and assimilation—although assimilation (specifically White’s illusion) only emerged when the virtual ecology included 3-D, as opposed to 2-D scenes. Subtle variations in these illusions, also found in human perception, were observed, such as the asymmetry of brightness contrast. These data suggest that “illusions” arise in humans because (i) natural stimuli are ambiguous, and (ii) this ambiguity is resolved empirically by encoding the statistical relationship between images and scenes in past visual experience. Since resolving stimulus ambiguity is a challenge faced by all visual systems, a corollary of these findings is that human illusions must be experienced by all visual animals regardless of their particular neural machinery. The data also provide a more formal definition of illusion: the condition in which the true source of a stimulus differs from what is its most likely (and thus perceived) source. As such, illusions are not fundamentally different from non-illusory percepts, all being direct manifestations of the statistical relationship between images and scenes. [ read more Cognitive Daily: Artificial networks see illusions too]

More information:
Auntie Em’s House of Cookies
What are lightness illusions and why do we see them?
White’s illusion

See also:
Computer Model mimics brain and processes visual information

Written by Linda MacPhee-Cobb

November 30th, 2007 at 5:00 am

Presidental candidate uses illegal bot net to spam voters

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I’m not sure which is worse? The fact you can’t escape election propaganda even in your email in box anymore? Or that the government has found yet another way to intrude into our lives using the internet, artificial intelligence and datamining?

Allegations have been made that Ron Paul’s campaign is creating internet buzz with spam sent through illegal botnets. I’m sure the rest of the candidates won’t be far behind. What does it say about a candidate that he is willing to use criminal methods to help his chance of getting elected? Last time that happened Richard Nixon won the White House. Right now candidates do polls and tell various political factions what they want to hear. How long before those speeches are geared specifically to you and arrive in your in box?

More information:
Analysis: is Ron Paul internet buzz real or spam?
Criminal botnet stumps for Ron Paul, Researchers Allege
First Presidential Candidate spam

Written by Linda MacPhee-Cobb

November 28th, 2007 at 5:00 am

AT&T big brother or savior?

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In order to understand AT&Ts complicity in the recent surveillance with out warrants scandal you might look to ‘The Hacker Crackdown, Law and Disorder on the Electronic Frontier’.

AT&T was an early victim of crackers and one of the first companies building defenses. AT&T being a very old school company was a little unclear on the concept and has certainly gone above and beyond the call of duty in fighting crackers. One of the methods developed to deal with phone crackers was a large scale program to track crackers and their associates and to look for people calling the same circle of friends a someone previously banned from the AT&T network.

A brilliant idea, Hancock is implemented in C. Not only does is provide for building the graph of connections but it allows you to see how the graph changes over time. It sounds promising yet I could find no published case studies showing its effectiveness. Instead of worrying about handling the large volume of data you can concentrate on what you wish to pull out of the data.

Then of course there are the legal and social aspects of all this data mining. Was AT&T legally collecting data for the government? What is the government doing with the data and with people it finds closely connected to terrorists? Does the software work? Are we ruining innocent people’s lives or are we saving innocents from terrorists? There is much yet that needs to come to light about this program. The government is using it as a ‘guilt by association’ program.

More information:
FBI data mining reached beyond initial targets
AT&T Invents Surveillance Programming Language ( Slashdot )
AT&T Invents Surveillance Programming Language ( Wired )
Communities of Interest, (pdf)
Hancock: A Language for Processing Very Large Scale Data
Method of inferring behavioral characteristics based on a large volume of data
Source Code and binaries for Hancock
Manual for Hancock( pdf )
The Hacker Crackdown, Law and Disorder on the Electronic Frontier

See also:
Software recognizes short and long term anxiety in people
Big brother arrives via Comcast 24 years late

Written by Linda MacPhee-Cobb

November 26th, 2007 at 5:00 am