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Desk of contents
v Preface and acknowledgements
Chiara Gianollo, Agnes Jäger and Doris Penka
1 Language switch on the syntax-semantics interface. views and demanding situations 1
Elly van Gelderen
2 Semantic and formal gains: characteristic economic system in language
change 33
Simon Kasper
3 Linking syntax and semantics of adnominal ownership within the historical past of German 57
Remus Gergel
4 such a lot traditionally one zero one Paola Crisma
5 The “indefinite article” from cardinal to operator to expletive 125
Nikolaos Lavidas
6 The Greek Septuagint and language swap on the syntax-semantics interface: from null to “pleonastic” item pronouns 153
Magnus Breder Birkenes and Florian Sommer
7 The contract of collective nouns within the historical past of historic Greek and German 183
Antje Casaretto and Carolin Schneider
8 Vedic neighborhood debris on the syntax-semantics interface 223
Cleo Condoravdi and Ashwini Deo
9 point shifts in Indo-Aryan and trajectories of semantic switch 261
Anne Breitbarth
10 the advance of conditional should still in English 293
Katerina Chatzopoulou
11 The Greek Jespersen’s cycle: Renewal, balance and structural microelevation 323
Subject index 355

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We have collections of dumb specialists in small domains; the true majesty of general intelligence still awaits our attack. We have got to get back to the deepest questions of AI and general intelligence and quit wasting time on little projects that don’t contribute to the main goal. 24 Chapter 1 Wolfram made a similar comment [Stork, 1997a]: Nobody’s trying more fundamental stuff. Everyone assumes it’s just too difficult. Well, I don’t think there’s really any evidence of that. It’s just that nobody has tried to do it.

If someone insists that these works should be called AI simply because they solve problems that were previously solvable only by the human mind, then by the same token numerical calculating programs should be called AI, as well. Beside the issues of label and credit, the real problem of this approach is that it fails to explain why ordinary computer systems are not intelligent. Many people enter AI to look for a fundamentally different way to build computer systems. To them, traditional computer systems are stupid, not because they cannot do anything (in fact, they can do many amazing things), but because they solve problems in a rigid manner.

The running of NARS consists of individual inference steps. In each step, a concept is selected probabilistically (according to its priority), then within the concept a task and a belief are selected (also probabilistically), and the applicable inference rules take the task and the belief as premises to derive new tasks and beliefs, which are added into the memory. The system runs continuously, and interacts with its environment all the time, without stopping at the beginning and ending of each task.

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