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If we want to understand music we have to go beyond understanding sound. We have to understand the transformations that sound can have if you play a … - Joscha Bach
" "If we want to understand music we have to go beyond understanding sound. We have to understand the transformations that sound can have if you play a different pitch. We have to arrange the sound in a sequencer that gives you rhythms, and so on, and then we want to identify some kind of musical grammar that we can use to... control the sequencer. So we have stacked structures that simulate the world. ...If you want to model a world of music you need to have the lowest level of the precepts, then the higher levels of mental simulations, which give the sequences... and the grammars of music... [B]eyond this you have the conceptual landscape that you can use to describe the different styles of music. ...[I]f you go up in the hierarchy, you get to more and more abstract models, more and more conceptual models, and more and more analytic models. ...[T]hese are causal models...
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About Joscha Bach
, also known as “the wizard of consciousness”(born 1973 in Weimar, Germany) is a cognitive scientist focusing on cognitive architectures, models of mental representation, emotion, motivation and sociality. Achievements include research in novel data compression algorithm using concurrent entropy models; development of microPsi cognitive architecture for modeling emotion, motivation, mental representation. In 2000, Bach graduated with a diploma in Computer Science from Berlin, followed by a Doctor of Philosophy at Osnabrück University, Germany, in 2006. Before joining , he worked as a visiting researcher at the and the Harvard Program for Evolutionary Dynamics. Fact finding reports by the and found that Bach’s research was supported with more than $150,000 by the Foundation.
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Additional quotes by Joscha Bach
AI has recently made huge progress in encoding data at perceptual interfaces. is about using a stacked hierarchy of feature detectors. ...[W]e use pattern detectors and we build them into networks that are arranged in hundreds of layers and then we adjust the links between these layers, usually using some kind of . ...[Y]ou can use this to classify [e.g.,] images and parts of speech. ...[W]e get to features that are more and more complex. They start with these very... simple patterns, and then get more and more complex until we get to object categories. ...[N]ow the systems are able, in image recognition tasks, to approach performance that's very similar to human performance. ...[I]t seems to be somewhat similar to what the brain seems to be doing in visual processing.
Early AI systems tended to constrain themselves to micro-domains that could be sufficiently described using simple ontologies and binary predicate logics, or restricted themselves to hand-coded ontologies altogether. ...AI systems will probably have to be perceptual symbol systems, as opposed to amodal symbol systems...
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