Author Topic: The Structure of Intelligence A New Mathematical Model of Mind  (Read 378 times)

0 Members and 1 Guest are viewing this topic.

Offline Flavio58

The Structure of Intelligence A New Mathematical Model of Mind
« Reply #1 on: May 17, 2018, 05:25:26 PM »
Advertisement
Over the last century, psychology has become much less of an art and much more of a science. Philosophical speculation is out; data collection is in. In many ways this has been a very positive trend. Cognitive science (Mandler, 1985) has given us scientific analyses of a variety of intelligent behaviors: short-term memory, language processing, vision processing, etc. And thanks to molecular psychology (Franklin, 1985), we now have a rudimentary understanding of the chemical processes underlying personality and mental illness. However, there is a growing feeling -- particularly among non-psychologists (see e.g. Sommerhoff, 1990) -- that, with the new emphasis on data collection, something important has been lost. Very little attention is paid to the question of how it all fits together. The early psychologists, and the classical philosophers of mind, were concerned with the general nature of mentality as much as with the mechanisms underlying specific phenomena. But the new, scientific psychology has made disappointingly little progress toward the resolution of these more general questions.
[/size]One way to deal with this complaint is to dismiss the questions themselves. After all, one might argue, a scientific psychology cannot be expected to deal with fuzzy philosophical questions that probably have little empirical significance. It is interesting that behaviorists and cognitive scientists tend to be in agreement regarding the question of the overall structure of the mind. Behaviorists believe that it is meaningless to speak about the structures and processes underlying behavior -- on any level, general or specific. And many cognitive scientists believe that the mind is a hodge-podge of special-case algorithms, pieced together without any overarching structure. Marvin Minsky has summarized this position nicely in his Society of Mind (1986).
[/size]It is not a priori absurd to ask for general, philosophical ideas that interlink with experimental details. Psychologists tend to become annoyed when their discipline is compared unfavorably with physics -- and indeed, the comparisonis unfair. Experimental physicists have many advantages over experimental psychologists. But the facts cannot be ignored. Physics talks about the properties of baseballs, semiconductors and solar systems, but also about the fundamental nature of matter and space, and about the origin of the cosmos. The physics of baseball is much more closely connected to experimental data than is the physics of the first three minutes after the Big Bang -- but there is a continuum of theory between these two extremes, bound together by a common philosophy and a common set of tools.
[/size]It seems that contemporary psychology simply lacks the necessary tools to confront comprehensive questions about the nature of mind and behavior. That is why, although many of the topics considered in the following pages are classic psychological topics, ideas from the psychological literature are used only occasionally. It seems to me that the key to understanding the mind lies not in contemporary psychology, but rather in a newly emerging field which I will call -- for lack of a better name -- "complex systems science." Here "complex" does not mean "complicated", but rather something like "full of diverse, intricate, interacting structures". The basic idea is that complex systems are systems which -- like immune systems, ecosystems, societies, bodies and minds -- have the capacity to organize themselves. At present, complex systems science is not nearly so well developed as psychology, let alone physics. It is not a tightly-knit body of theorems, hypotheses, definitions and methods, but rather a loose collection of ideas, observations and techniques. Therefore it is not possible to "apply" complex systems science to the mind in the same way that one would apply physics or psychology to something. But complex systems science is valuable nonetheless. It provides a rudimentary language for dealing with those phenomena which are unique to complex, self-organizing systems. And I suggest that it is precisely these aspects of mentality which contemporary psychology leaves out.
[/size]     More specifically, the ideas of the following chapters are connected with four
[/size]"complex systems" theories, intuitively and/or in detail. These are: the theory of pattern (Goertzel, 1991), algorithmic information theory (Chaitin, 1987), the theory of multiextremal optimization (Weisbuch, 1991; Dixon and Szego, 1978; Goertzel, 1989), and the theory of automata networks (Derrida, 1987; Weisbuch, 1991).
[/size]     The theory of pattern provides a general yet rigorous way of talking about concepts such as structure, intelligence, complexity and mind. But although it is mathematically precise, it is extremely abstract. By connecting the theory of pattern with algorithmic information theory one turns an abstract mathematical analysis of mind into a concrete, computational analysis of mind. This should make clear the limited sense in which the present theory of mind is computational, a point which will be elaborated below. Most of the ideas to be presented are not tied to any particular model of computation, but they are discussed in terms of Boolean automata for sake of concreteness and simplicity.
[/size]     Pattern and algorithmic complexity give us a rigorous framework for discussing various aspects of intelligence. The theory of multiextremaloptimization, which is closely tied to the abstract theory of evolution (Kauffman, 1969; Langton, 1988), gives us a way of understanding some of the actual processes by which intelligences recognize and manipulating patterns. Perception, control, thought and memory may all be understood as multiextremal optimization problems; and recent theoretical and computational results about multiextremal optimization may be interpreted in this context. And, finally, the theory of automata networks -- discussed in Appendix 2 -- gives a context for our general model of mind, which will be called the "master network". The master network is not merely a network of simple elements, nor a computer program, but rather a network of programs: an automata network. Not much is known about automata networks, but it is known that in many circumstances they can "lock in" to complex, self-organizing states in which each component program is continually modified by its neighbors in a coherent way, and yet does its individual task effectively. This observation greatly increases the plausibility of the master network.
[/size]
[/size]http://www.bernardotti.it/TheStructureofIntelligence/contents.html


Consulente in Informatica dal 1984

Software automazione, progettazione elettronica, computer vision, intelligenza artificiale, IoT, sicurezza informatica, tecnologie di sicurezza militare, SIGINT. 

Facebook:https://www.facebook.com/flaviobernardotti58
Twitter : https://www.twitter.com/Flavio58

Cell:  +39 366 3416556

f.bernardotti@deeplearningitalia.eu

#deeplearning #computervision #embeddedboard #iot #ai

 

Related Topics

  Subject / Started by Replies Last post
0 Replies
79 Views
Last post July 08, 2018, 12:14:36 AM
by Flavio58
0 Replies
80 Views
Last post July 13, 2018, 02:02:17 AM
by Flavio58
0 Replies
87 Views
Last post July 27, 2018, 12:06:49 PM
by Marco1971
0 Replies
68 Views
Last post September 10, 2018, 10:01:36 PM
by Flavio58
0 Replies
69 Views
Last post October 31, 2018, 08:06:37 AM
by Flavio58

Sitemap 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326