What Bodies Think About: Bioelectric Computation Outside the Nervous System - talk by Prof. Michael Levin at NeurIPS 2018. #ML #Regenerative #ALife
tag > ML
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Music Object Detector with TensorFlow: #ML #Generative #Music
https://github.com/apacha/MusicObjectDetector-TF
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Glove-Talk II: A Neural Network Interface Which Maps Gestures to Parallel Formant Speech Synthesizer Controls - by Sidney Fels and Geoffrey Hinton (1998):
http://www.cs.toronto.edu/~hinton/absps/glovetalkii.htm #HCI #ML
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Deep Interactive Evolution: #ML #Generative
http://sebastianrisi.com/wp-content/uploads/bontrager_evomusart18.pdf
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Neuroevolution in Games: State of the Art and Open Challenges - by Sebastian Risi and Julian Togelius: https://arxiv.org/pdf/1410.7326.pdf #ML #Games #ALife
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Embodied AI Characters for Emergent Narrative - by Jeffrey Ventrella:
How AI and augmented reality will issue forth a new genre of interactive character design:
http://ourmedia.org/embodied-ai-characters-for-emergent-narrative/ #ML #Generative #ALife
Emergent Narratives vs. Branching Stories. Simulation. The Artificial Life Approach: Starting with a Primordial Soup.
“A story should have a beginning, a middle and an end, but not necessarily in that order.” – Jean-Luc Godard
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Emergent Complexity via Multi-agent Competition:
https://sites.google.com/view/multi-agent-competition -
Walking with Neural Networks and Genetic Evolution. #Generative #ML #ALife
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A selection of virtual creatures - by Keyan Ghazi-Zahedi: #ML #Generative #Robot #ALife
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Morphological computation: synergy of body and brain - by Keyan Ghazi-Zahedi:
https://www.researchgate.net/publication/319403416_Morphological_Computation_Synergy_of_Body_and_Brain #ML #Robot #ALife
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Disney Meets Darwin - "An Evolution-based Interface for Exploration and Design of Expressive Animated Behavior" (1994) by Jeffrey Ventrella: http://www.ventrella.com/
https://pdfs.semanticscholar.org/9ded/6c66bec25e02ac3163fd3e9ba914c506fc16.pdf
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Gene Pool: http://www.swimbots.com/ #ML #Generative #ALife
It's a computer simulation where hundreds of virtual organisms evolve swimming skills. These organisms are called "swimbots". You can set mate preference criteria and thus influence what the swimbots consider as attractive qualities in potential mates. The most attractive swimbots get chosen most often to have little babies, and so their genetic building blocks propogate to future generations. Eventually, swimbots get better at pursuing each other, competing for food, and becoming babes to other swimbots. Local gene pools emerge which compete for sex and food (for energy to have more sex). Eventually a dominant sub-population takes over.
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Jordan Pollack - Full Interview. #Generative #ML #ALife
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Generative Representations for Design Automation:
http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html #Generative #ML #ALife
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Computer Evolution of Buildable Objects:
http://www.demo.cs.brandeis.edu/pr/buildable/crane/index.html #Generative #ML #ALife
We believe that not just the software, but also the physical body of a robot could be the result of an evolutionary process. A step in this direction is the evolution of buildable lego structures, designed by the computer through the combination of genetic algorithms and physical simulation.
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Automatic design and manufacture of robotic lifeforms - by Hod Lipson & Jordan B. Pollack:
http://www.demo.cs.brandeis.edu/golem/download/naturegolem.pdf
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The Golem Project - Automatic Design and Manufacture of Robotic Lifeforms: http://www.demo.cs.brandeis.edu/golem/ #ML #Generative #ALife
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Framsticks - a three-dimensional life simulation project: http://www.framsticks.com/
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Biped walking with CMA-ES: http://www.iri.upc.edu/files/scidoc/1917-On-the-application-of-CMA-ES-to-biped-walk.pdf #ML #Robot #ALife
