Project Tabula Rasa is an umbrella term for a few software, system, and (eventual) hardware projects modeled after the generalized intelligence exhibited by the human mind. These intensely detailed and interconnected projects are the product of academic research started in 2005 collaborating knowledge from the interdisciplinary study of Computer Science, Mathematics, Physics, Neurobiology, Psychology, Philosophy, Literature, Cognitive Science, and Education.
Some sub-projects include: - Dynamic Load Balancing for Dynamic Distributed Networks
- Invariant Representations of Temporal Percept Sequences
- Scalable Distributed Cortical System Design
- Modular, On-line, Competitive, Unsupervised Machine Learning Algorithms
This is a massive theoretical undertaking which will result in several publications. The academic papers that have contributed to my knowledge have not been cataloged (yet), but a list of books that I use as reference/inspiration materials include (but is not limited to): - Understanding Human Behavior (Vols. 1-24)
- MIT Encyclopedia of the Cognitive Sciences
- Cambridge Handbook of Thinking & Reasoning
- Handbook of Brain Theory and Neural Networks – Arbib
- Connectionism and the Mind – Bechtel/Abrahamsen
- Minds and Computers – Carter
- Decision Fusion – Dasarathy
- Pattern Classification – Duda/Hart/Stork
- Artificial Dreams – Ekbia
- From Molecule to Metaphor – Feldman
- Markov Models for Pattern Recognition – Fink
- Emotion Science – Fox
- Gut Feelings – Gigerenzer
- The Hidden Pattern – Goertzel
- Artificial General Intelligence – Goertzel
- Robot Brains – Haikonen
- Beyond AI – Hall
- On Intelligence – Hawkins
- The Art of Multiprocessor Programming – Herlihy/Shavit
- The Chemistry of Conscious States – Hobson
- Gödel, Escher, Bach – Hofstadter
- Human Memory – Howes
- Modern Multivariate Statistical Techniques – Izenman
- Dynamical Systems in Neuroscience – Izhikevich
- Innovations in ART Neural Networks – Jain
- In Search of Memory – Kandel
- Programming Massively Parallel Processors – Kirk/Whu
- The Emotional Brain – Ledoux
- How We Decide – Lehrer
- Proust Was a Neuroscientist – Lehrer
- Complexity – Lewin
- Big Brain – Lynch/Granger
- Cognition – Matlin
- Learning & Instruction – Mayer
- Mind and Mechanism – McDermott
- Cracking Creativity – Michalko
- The Emotion Machine – Minsky
- The Society of Mind – Minsky
- Machine Learning – Mitchell
- How People Learn – National Research Council
- The Human Brain – Nolte
- The Emperor's New Mind – Penrose
- Motivation – Petri/Govern
- An Introduction to Information Theory – Pierce
- Artificial Beings – Pitrat
- Remembrance of Things Past – Proust
- Semantic Cognition – Rogers/McClelland
- Parallel Distributed Processing Vol 1 & 2 – Rumelhart/McClelland/the PDP Research Group
- Artificial Intelligence – Russel/Norvig
- CUDA by Example – Sanders/Kandrot
- The Quantum Brain – Satinover
- Nonlinear Regression – Seber/Wild
- Programming Collective Intelligence – Segaran
- Kernel Methods – Shawe-Taylor/Cristianini
- Fundamentals of Physiology – Sherwood
- Contemporary Debates in Cognitive Science – Stainton
- Nonlinear Dynamics and Chaos – Strogatz
- Distributed Systems Principles and Paradigms – Tanenbaum/Van Steen
- Pattern Recognition – Theodoridis/Koutrounbas
- The Computer and The Brain – von Neumann
- Parallel Programming – Wilkinson/Allen
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