Journal Article10.1093/9780191925375.003.0001
Introduction
Stephen Laurence,Eric Margolis +1 more
- 22 Aug 2024
- pp 1-22
TL;DR: This chapter introduces the book, contextualizing the rationalism-empiricism debate by tracing its history from Noam Chomsky's work to contemporary cognitive science, highlighting the need to address philosophical and theoretical questions amidst empirical data.
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Abstract: Abstract This chapter provides an introduction to the book as a whole. It contextualizes the contemporary rationalism-empiricism debate about the origins of concepts, tracing it back to Noam Chomsky’s seminal work in the 1960s and 1970s, which drew an explicit link between twentieth-century linguistics and the historical philosophical debate about innate ideas, particularly in the seventeenth and eighteenth centuries. The chapter illustrates how the wealth of empirical data that is now available through research in cognitive science has transformed the debate. At the same time, it makes clear that this doesn’t mean that the philosophical debate has now simply been reduced to a straightforward empirical question. Bringing this wealth of empirical data to bear on the debate requires addressing a broad range of philosophical and theoretical questions, some old and some new.
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References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
•Book
The Ecological Approach to Visual Perception
James J. Gibson
- 01 Jan 1979
TL;DR: The relationship between Stimulation and Stimulus Information for visual perception is discussed in detail in this article, where the authors also present experimental evidence for direct perception of motion in the world and movement of the self.
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