David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Artificial neural networks (ANNs) are new mathematical techniques which can be used for modelling real neural networks, but also for data categorisation and inference tasks in any empirical science. This means that they have a twofold interest for the philosopher. First, ANN theory could help us to understand the nature of mental phenomena such as perceiving, thinking, remembering, inferring, knowing, wanting and acting. Second, because ANNs are such powerful instruments for data classification and inference, their use also leads us into the problems of induction and probability. Ever since David Hume expressed his famous doubts about induction, the principles of scientific inference have been a central concern for philosophers
|Keywords||No keywords specified (fix it)|
No categories specified
(categorize this paper)
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Gualtiero Piccinini (2008). Some Neural Networks Compute, Others Don't. Neural Networks 21 (2-3):311-321.
Dan Hunter (1999). Out of Their Minds: Legal Theory in Neural Networks. [REVIEW] Artificial Intelligence and Law 7 (2-3):129-151.
Enrico Blanzieri (1997). Dynamical Learning Algorithms for Neural Networks and Neural Constructivism. Behavioral and Brain Sciences 20 (4):559-559.
Peter R. Krebs, Models of Cognition: Neurological Possibility Does Not Indicate Neurological Plausibility.
Stan Franklin & Max Garzon (1992). On Stability and Solvability (or, When Does a Neural Network Solve a Problem?). Minds and Machines 2 (1):71-83.
Paul Skokowski (2007). Networks with Attitudes. Artificial Intelligence and Society 22 (3):461-470.
D. Levine & W. Elsberry (eds.) (1997). Optimality in Biological and Artificial Networks? Lawrence Erlbaum.
Michael Lamport Commons (2008). Stacked Neural Networks Must Emulate Evolution's Hierarchical Complexity. World Futures 64 (5 - 7):444 – 451.
Lothar Philipps & Giovanni Sartor (1999). Introduction: From Legal Theories to Neural Networks and Fuzzy Reasoning. [REVIEW] Artificial Intelligence and Law 7 (2-3):115-128.
Aarre Laakso & Garrison W. Cottrell (2000). Content and Cluster Analysis: Assessing Representational Similarity in Neural Systems. Philosophical Psychology 13 (1):47-76.
Pete Mandik (2003). Varieties of Representation in Evolved and Embodied Neural Networks. Biology and Philosophy 18 (1):95-130.
Paul A. Koch & Gerry Leisman (2004). The Local is Running on the Express Track: Localist Models Better Facilitate Understanding of Nervous System Function. Behavioral and Brain Sciences 27 (5):700-700.
Kamal Dahbur & Thomas Muscarello (2003). Classification System for Serial Criminal Patterns. Artificial Intelligence and Law 11 (4):251-269.
Added to index2010-07-23
Total downloads20 ( #83,429 of 1,098,628 )
Recent downloads (6 months)1 ( #285,836 of 1,098,628 )
How can I increase my downloads?