Levels and loops: the future of artificial intelligence and neuroscience

被引:30
作者
Bell, AJ [1 ]
机构
[1] Interval Res Corp, Palo Alto, CA 94304 USA
关键词
artificial intelligence; neuroscience; cyclic systems; dualism; science fiction;
D O I
10.1098/rstb.1999.0540
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In discussing artificial intelligence and neuroscience, I will focus on two themes. The first is the universality of cycles (or loops): sets of variables that affect each other ill such a way that any feed-forward account of causality and control, while informative, is misleading. The second theme is based around the observation that a computer is an intrinsically dualistic entity with its physical set-up designed so as not to interfere with its logical set-up, which executes the computation. The brain is different. When analysed empirically at several different levels (cellular, molecular), it appears that there is no satisfactory way to separate a physical brain model (or algorithm, or representation)? from a physical implementational substrate. When program and implementation are inseparable and thus interfere with each other, a dualistic point-of-view is impossible. Forced by empiricism into a monistic perspective, the brain-mind appears as neither embodied by or embedded in physical reality but rather as identical to physical reality. This perspective has implications for the future of science and society I will approach these from a negative point-of-view, by critiquing some of our millennial culture's popular projected futures.
引用
收藏
页码:2013 / 2020
页数:8
相关论文
共 25 条
[1]  
[Anonymous], 1997, ARTIFICIAL LIFE OVER
[2]  
[Anonymous], 1990, SELFISH GENE
[3]  
Arkin RC, 1998, BEHAV BASED ROBOTICS
[4]   The ''independent components'' of natural scenes are edge filters [J].
Bell, AJ ;
Sejnowski, TJ .
VISION RESEARCH, 1997, 37 (23) :3327-3338
[5]  
Bohm D., 2002, Wholeness and the Implicate Order
[6]  
BRUCE V, 1990, VISUAL PERCEPTION PH
[7]  
de Duve, 1991, BLUEPRINT CELL
[8]  
Gibson J. J., 1977, ECOLOGICAL APPROACH
[9]  
Gibson William, 1986, Neuromancer
[10]  
Haykin S., 1999, NEURAL NETWORK COMPR