Facebook offers great functionalities, it is easy, fun, extensible... However, it seems that like many things which are positive for many people (e.g. automobiles), they get overused with unintended consequences (e.g. traffic jams).
OK, so I have 400+ friends on facebook. The problem is that I could spare like 5 minutes every other day to check the news feed. With so many people in my feed, I get what was posted 2 hours ago at most. Sure, there are ways of blocking applications, creating filters (e.g. with Better FB), but this does not work for me. There are just too many posts I am not interested about, but I am interested in few things that most people post about. It is difficult to categorize. Where is artificial intelligence when it is needed? I believe that algorithms similar to anti-spam filters would be immensely useful on social networks. For example, I am interested about the English postings of my Iranian friends, but I cannot make much of their Farsi posts... For users, it would be as easy as to add an "Unlike" button.
In Twitter I have a similar problem, but it is easier to unfollow people. Still, e.g. my friend @mauropm tweets about a hundred times a day. I can be interested in a couple of those, but I am unable to follow him because of the rest of his activity.
As feeds are becoming more and more widespread, it is becoming more and more necessary to develop AI algorithms to sort through the "relevant" stuff...
(read a big business opportunity)
2010-12-23
Why facebook stopped working for me
2010-12-13
Deadline Extended, Final CfP: Special Issue on Complex Networks, Artificial Life
Call for Papers
Special Issue on Complex Networks
Artificial Life Journal
Motivation
As a result of the quality of the Complex Networks track at the ALife XII conference last August in Odense, Denmark and the interest of the attendants; we announce a call for papers for a special issue on this theme for the Artificial Life Journal.
Many complex systems are amenable to be described as networks. These include genetic regulatory, structural or functional cortical networks, ecological systems, metabolism of biological species, author collaborations, interaction of autonomous systems in the Internet, etc. A recent trend suggests to study common global topological features of such networks, e.g. network diameter, clustering coefficients, assortativity, modularity, community structure, etc.
Various network growth models have also been proposed and studied to emulate the features of the real-world networks, e.g. the preferential attachment model, which explains scale-free power law degree distributions observed in many real-world networks.
Another direction is to investigate network motifs and subgraphs in order to understand and analyse the local structure and function of networks. The presence of a certain motif in a network may mean that that motif plays an important role in the overall functionality of the network. Thus, functionality of specific motifs, including their information processing and control functions, is a challenging topic relevant in Artificial Life studies, such as genetic regulatory networks, cell signaling networks, and protein interaction networks.
In addition, propagation and processing of information within networks may be analysed as (Shannon) information dynamics. Such analysis requires to consider not only networks' topology, but also the time-series dynamics at individual nodes. Specific topics of interest include phase transitions of network properties between ordered and chaotic regimes, where information transfer is often maximised, and other nonlinear phenomena related to criticality in networks.
Goal
The intention of the special issue is to bring together research from both Artificial Life and Complex Networks communities, in order to facilitate cross-fertilization, increase exposure of both communities to relevant research and foster new collaborations.
Submission
Contributions to the Session should be prepared and submitted according to the Artificial Life journal guidelines, available at http://www.mitpressjournals.org/page/sub/artl. Authors should also include a cover letter describing briefly the relevance of their article to the specific topic of this call. Every submission will be subject to full peer review.
Articles should NOT be submitted to the journal editor, but should be uploaded through the special issue website (http://turing.iimas.unam.mx/~review ).
Papers will be judged by members of the Review Committee on their relevance to the call for papers, originality, clarity of the presentation, and overall quality.
Important Dates
Extended paper submission: January 7th, 2011
Paper notification: February 28th, 2011
Camera-ready papers due: March 31st, 2011
Review Committee
- Chris Adami
- Lee Altenberg
- Alain Barrat
- Randall Beer
- Hugues Bersini
- Johan Bollen
- Markus Brede
- Mikhail Burtsev
- Alan Dorin
- Nic Geard
- Carlos Gershenson
- Mario Giacobini
- Juan Luis Jiménez Laredo
- Joseph Lizier
- Michael Mayer
- Juan Julián Merelo Guervós
- Oliver Obst
- Charles Ofria
- Mikhail Prokopenko
- Tom Ray
- Hiroki Sayama
- Hideaki Suzuki
- Vito Trianni
- Elio Tuci
- Rosalind Wang
- Borys Wrobel
- Larry Yaeger
Guest Editors
Dr. Mikhail Prokopenko
CSIRO, Australia
Dr. Carlos Gershenson
IIMAS, UNAM, Mexico
2010-12-10
Paper Published: The sigma profile: A formal tool to study organization and its evolution at multiple scales, Complexity
Gershenson, C. (2010). The sigma profile: A formal tool to study organization and its evolution at multiple scales. Complexity, first published online: 10 NOV 2010. DOI: 10.1002/cplx.20350
Abstract
The σ profile is presented as a tool to analyze the organization of systems at different scales, and how this organization changes in time. Describing structures at different scales as goal-oriented agents, one can define σ ∈ [0,1] (satisfaction) as the degree to which the goals of each agent at each scale have been met. σ reflects the organization degree at that scale. The σ profile of a system shows the satisfaction at different scales, with the possibility to study their dependencies and evolution. It can also be used to extend game theoretic models. The description of a general tendency on the evolution of complexity and cooperation naturally follows from the σ profile. Experiments on a virtual ecosystem are used as illustration.
Full text
http://dx.doi.org/10.1002/cplx.20350
(send me an email if you do not have access and want a copy)
Science catching up science fiction
The science: mice are born with genetic material of two males (fresh news).
http://blogs.nature.com/news/thegreatbeyond/2010/12/look_dads_no_mum.html
The science fiction: dreams of a teenager (~12 years ago).
http://turing.iimas.unam.mx/~cgg/jlagunez/npm1.htm [in Spanish...]
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