Collective behaviour of machines examined across animal species

By: James V. Kohl | Published on: January 25, 2014

Collective behaviour across animal species 
Excerpt: “The results of this work are expected to bridge the gap between manual and automated data analysis, which will ultimately contribute to the systematic definition of collective behaviour across diverse animal groups.”
Reported as: UNDERSTANDING COLLECTIVE ANIMAL BEHAVIOR MAY BE IN THE EYE OF THE COMPUTER
Excerpt: “The researchers believe that this breakthrough is the beginning of an entirely new way of understanding and comparing the behaviors of social animals.”

My comment: This suggests (to me) that anyone who wants to examine human behavior in the context of machine learning techniques, which are used to evaluated the differences in biologically based behaviors in other animals, can now progress with systematic definitions of collective behavior based on the assumption that all animals are machines. But, didn’t B.F. Skinner already do that to facilitate animal training?

I ask that question because it seems silly to

1) use classical conditioning to train animals; then…
2) report the training in terms of operant conditioning as if the animals…
3) never needed to eat and then…
4) eliminate both the chemical ecology of food odors and social odors so that…
5) our behavior could be portrayed in the context of machine learning without…
6) any concerns for organismal complexity…
or genetically predisposed
1) ecological
2) social
3) neurogenic, and
4) socio-cognitive niche construction.

If you take the time to scan the articles and think they are meaningful representations of how the science of human ethology may progress, I hope you will tell others why you think that.

Jay R. Feierman just posted them to the ISHE’s human ethology yahoo group with his emphasis, as if his emphasis made their content meaningful.

“We posit a new geometric perspective to define, detect, and classify inherent patterns of collective behaviour across a variety of animal species. We show that machine learning techniques, and specifically the isometric mapping algorithm, allow the identification and interpretation of different types of collective behaviour in five social animal species. These results offer a first glimpse at the transformative potential of machine learning for ethology, similar to its impact on robotics, where it enabled robots to recognize objects and navigate the environment.”

Feierman, as some people may know, is the moderator of the group who wrote: I am absolutely certain that if you showed this statement to any professor of biology or genetics in any accredited university anywhere in the world that 100% of them would say that “Random mutations are the substrate upon which directional natural selection acts” is a correct and true statement.


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