Computer science as empirical inquiry

Abstract

C o m p u t e r science is the study of the p h e n o m e n a s u r r o u n d i n g c o m p u t e r s . The founders of this society understood this very well when they called themselves the A s s o c i a t i o n for C o m p u t i n g M a c h i n e r y . T h e machine---not j u s t the h a r d w a r e , but the p r o g r a m m e d , living m a c h i n e --i s the o r g a n i s m we study. This is the tenth T u r i n g Lecture. The nine persons who preceded us on this p l a t f o r m have presented nine different views of c o m p u t e r science. F o r our organism, the machine, can be studied at m a n y levels and from m a n y sides. W e are d e e p l y h o n o r e d to a p p e a r lhere today and to present yet a n o t h e r view, the one that has permeated the scientific w o r k for which we have been

Untitled

“…• modeling language is merely an add-on to our natural language; it is an additional link of the semiotic structure of language that connects us to reality and there are valid reasons to believe that specialized expertise in modeling language is not necessary for modeling and solving problems. It is helpful to support our argumentation with the paradigm reported as the Physical Symbol System Hypothesis (Newell andSimon 1976, Simon 1980). In H. Simon's words, phrases and other elements of language remind the construction in the edifice -words and expressions (semantics) are the blocks and bricks, and the relationships and rules (grammar) act as cement connecting them.…”

Rethinking Eliminative Connectionism

“…Symbols are mental encodings of equivalence classes (Abler, 1989;Newell & Simon, 1975;Pylyshyn, 1984Pylyshyn, , 1986Vera & Simon, 1994), which is to say that all members encoded via a particular symbol are treated equally by some higher level operation (Pylyshyn, 1984(Pylyshyn, , 1986. Symbols can encode either individuals (e.g., Donald Duck) or categories (e.g., duck or cartoon character), and can encode either atomic elements (e.g.…”

Symbol Grounding Through Cumulative Learning

“…This is one of the most important questions underlying the study of cognition, language, and artificial intelligence. In the field of artificial intelligence, the intellectual history of this problem traces back to the earliest speculations on the nature of intelligence 3 . Alan Turing, in the conclusion to his classic article which introduced the Turing test, suggested that there might be at least two routes to building intelligent machines: attempting very abstract activities like playing chess, or outfitting a computer with sensory devices and then attempting to teach it natural language [2].…”

“…Alan Turing, in the conclusion to his classic article which introduced the Turing test, suggested that there might be at least two routes to building intelligent machines: attempting very abstract activities like playing chess, or outfitting a computer with sensory devices and then attempting to teach it natural language [2]. In subsequent years the purely symbolic approach gained dominance, partly due to the comparative ease of building purely symbolic systems, and partly due to the influence of the 3 It should be pointed out that this question has a much longer history in philosophy.…”

“…Physical Symbol System Hypothesis of Newell and Simon [3], which says that a set of symbols, combined with appropriate rules for their manipulation (essentially, a formal system), is sufficient for general intelligent action. The implicit assumption underlying this view is that intelligent behavior from a machine does not require that the machine "understand" things in the same way as we do.…”