Gallery

SNA – Machine Learning – Dimensionality reduction

Visualization of a social network analysis (SNA) defined by a high-dimensional parameter space (21 dimensions). Dimensionality reduction (DR) is used here to allow the reduction of the number of variables that defines a system, into a lower-dimensional space (2D).

In this case, I’m using self-organizing maps (SOM), which represent a type of unsupervised artificial neural network, to reduce the high dimensionality of the data whilst also retain the high-dimensional non-linear associations.

Another feature extraction used (side by side image), is a linear feature extraction method -> K-means cluster analysis, (although linear methods don’t retain the associations distributed non-linearly in the high dimensional space).

#owl #machinelearning

As the dataset was relatively small, I used only CPU ( 2 x Intel Core i7-4930k @ 3.40GHz) computing for the training phase. If you are dealing with a big data set, a GPU might be a better way to go to train your dataset.

CPU – 2 x Intel Core i7-4930k @ 3.40GHz

GPU – GPU

GPGPU – Agent bodies facade

Investigations into agent bodies algorithms, and behavioral algorithms, where gradients and levels of differentiation are not predefined but rather a result of emergence.
#GPGPU #Stigmergy #Java #agentbodies #selforganization #JCuda #sematectonicFields nonstandardstudio

more description soon…

There were 2xNVidia GTX780Ti used in this study, having in total a combined 5760 CUDA cores (2880 each), running on a machine 2 x Intel Core i7-4930k @ 3.40GHz and Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz.

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

[3] Bonabeau E., M. Dorigo, G. Théraulaz. Swarm Intelligence: From Natural to Artificial System s. Santa Fe Institute in the Sciences of the Complexity, Oxford University Press, New York, Oxford, 1999.
[4] Chialvo, Dante R., Millonas, Mark M. “How Swarms Build Cognitive Maps” . In Luc Steels (Ed.), The Biology and Technology of Intelligent Autonomous Agents , (144) pp. 439-450, NATO ASI Series, 1995.
[5] Vitorino Ramos, Filipe Almeida, Artificial Ant Colonies in Digital Image Habitats – A Mass Behaviour Effect Study on Pattern Recognition, Proceedings of ANTS’2000 – 2nd International Workshop on Ant Algorithms (From Ant Colonies to Artificial Ants), Marco Dorigo, Martin Middendorf & Thomas Stüzle (Eds.), pp. 113 – 116, Brussels, Belgium, 7 – 9 Sep. 2000.

US equivalent
GPU: 2xNVidia GTX780Ti
CPU: 2 x Intel Core i7-4930k @ 3.40GH
RAM: Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

Structural Stigmergy Model

Structural Stigmergy Model nonstandardstudio #stigmergy #structuralStigmergy #shellStructures #catenary

Articulation of structural morphologies – through principles of stigmergic collaboration and structural coupling.

By self-regulating critical variables of force distribution within a structural system, the ecology of agents is developing an automatic mechanism of self-regulation – homeostasis/autopoiesis system – where the system becomes self-producing, allowing for a correlated and differentiated multi-system. Where like in natural systems, compositions are so highly integrated that they cannot be easily decomposed into independent subsystems. It becomes a system with self-reference and self-regulation which further evolves by using structural coupling. Therefore recognizing that the outside influences cannot shape the system’s internal structure, but act only as a trigger that causes the structure to alter its current attractors.

The ecology of agents allows us to correlate multiple systems, and encourage the contributive coexistence of different articulation layers.

There were 2xNVidia GTX780Ti used in this study, having in total a combined 5760CUDA cores (2880 each), running on a machine 2 x Intel Core i7-4930k @ 3.40GHz and Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz.

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

US equivalent
GPU: 2xNVidia GTX780Ti
CPU: 2 x Intel Core i7-4930k @ 3.40GH
RAM: Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

GPGPU – Structurally encoded agent bodies

Couple iterations of Pavilion studies that we did last year. Full project pictures will be presented soon.

#agentbodies #swarm #swarmintelligence #informedbodies #chargedfields #java #jCuda nonstandardstudio

more description soon…

There were 2xNVidia GTX780Ti used in this study, having in total a combined 5760CUDA cores (2880 each), running on a machine 2 x Intel Core i7-4930k @ 3.40GHz and Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz.

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

US equivalent
GPU: 2xNVidia GTX780Ti
CPU: 2 x Intel Core i7-4930k @ 3.40GH
RAM: Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

GPGPU – Sematectonic Fields – Smart Agent Bodies

The study explores the idea of agent bodies where the body of the agent is defined by a mesh geometry capable of reacting to different stimuli. The agent body has its own behavioral rules, that determine the way it will interact and connect with other neighbors’ bodies.

There are two levels of interactions, therefore, the interaction between agents, and interactions between agent bodies. The bodies are intelligent enough to be able to adapt to local conditions, interactions. Their perception and memory are not only self-gathered but also inherited from the agent swam. Series of smart agent bodies start to snap, interact and connect to each other giving rise to emergent gradient patterns, and intense levels of differentiation.

Because of the high amount of agents, and interactions involved, and to satisfy the required computational power, necessary for these sorts of processes, swarm algorithms adapted for GPGPU were deployed.

There were 2xNVidia GTX780Ti used in this study, having in total a combined 5760CUDA cores (2880 each), running on a machine 2 x Intel Core i7-4930k @ 3.40GHz and Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz.

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper

US equivalent
GPU: 2xNVidia GTX780Ti
CPU: 2 x Intel Core i7-4930k @ 3.40GH
RAM: Corsair DOMINATOR Platinum Series 64GB DDR4 DRAM 2800MHz

References:

[1]Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson

[2]The Myth of the Framework: In Defense of Science and Rationality by Karl Popper