The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning and Linguistic Representation looks at the application of (...) a variety of deep learning systems to several cognitively interesting NLP tasks. It also considers the extent to which this work illuminates our understanding of the way in which humans acquire and represent linguistic knowledge. Key Features: combines an introduction to deep learning in AI and NLP with current research on Deep Neural Networks in computational linguistics. is self-contained and suitable for teaching in computer science, AI, and cognitive science courses; it does not assume extensive technical training in these areas. provides a compact guide to work on state of the art systems that are producing a revolution across a range of difficult natural language tasks. (shrink)

The transformers that drive chatbots and other AI systems constitute large language models (LLMs). These are currently the focus of a lively discussion in both the scientific literature and the popular media. This discussion ranges from hyperbolic claims that attribute general intelligence and sentience to LLMs, to the skeptical view that these devices are no more than “stochastic parrots”. I present an overview of some of the weak arguments that have been presented against LLMs, and I consider several of the (...) more compelling criticisms of these devices. The former significantly underestimate the capacity of transformers to achieve subtle inductive inferences required for high levels of performance on complex, cognitively significant tasks. In some instances, these arguments misconstrue the nature of deep learning. The latter criticisms identify significant limitations in the way in which transformers learn and represent patterns in data. They also point out important differences between the procedures through which deep neural networks and humans acquire knowledge of natural language. It is necessary to look carefully at both sets of arguments in order to achieve a balanced assessment of the potential and the limitations of LLMs. (shrink)

Toldot adam 3 -- Bet ha-midot -- Sheʼelot u-teshuvot.

Was Maimonides a radical philosopher who subtly argued for a naturalist world and who saw the obligation to keep the Torah's commandments as a social and moral obligation - or was he a conservative Jewish believer who only tried to formulate philosophical arguments in favour of a revealed religion? This question has been central to the interpretation of Maimonides from the 12th century until modern times. In the four chapters of this book, Shalom Sadik argues for a radical philosophical (...) interpretation of Maimonides. (shrink)

Studies in Abraham Joshua Heschel's Oeuvre.

Ḥelek 2. Toroto shel Rabenu bi-lesh. ha-ḳ. ... maʼamarim shonim be-Idish ... sheʼelot u-teshuvot ba-ʻavodat ha-Sh. yit ... shivḥo shel tsadiḳ.

This book explores what motivates net-generation community college students to complete their degrees. By emphasizing relationships, personal growth, and support systems, the author’s research will empower educational institutions to increase retention and foster success.

The human self model suggests that the construct of self involves functions such as agency, body-centered spatial perspectivity, and long-term unity. Vogeley, Kurthen, Falkai, and Maieret (1999) suggest that agency is subserved by the prefrontal cortex and other association areas of the cortex, spatial perspectivity by the prefrontal cortex and the parietal lobes, and long-term unity by the prefrontal cortex and the temporal lobes and that all of these functions are impaired in schizophrenia. Exploring the connections between the prefrontal cortex (...) and the construct of self, the present article extends the application of the self model to autism. It suggests that in contrast to schizophrenia, agency and spatial perspectivity are probably preserved in autism, but that, similarly to schizophrenia, long-term unity is probably impaired. This hypothesis is compatible with a model of neuropsychological dysfunction in autism in a neural network including parts of the prefrontal cortex, the temporal lobes, and the cerebellum. (shrink)

The language of standard propositional modal logic has one operator (? or ?), that can be thought of as being determined by the quantifiers ? or ?, respectively: for example, a formula of the form ?F is true at a point s just in case all the immediate successors of s verify F.This paper uses a propositional modal language with one operator determined by a generalized quantifier to discuss a simple connection between standard invariance conditions on modal formulas and generalized (...) quantifiers: the combined generalized quantifier conditions of conservativity and extension correspond to the modal condition of invariance under generated submodels, and the modal condition of invariance under bisimulations corresponds to the generalized quantifier being a Boolean combination of ? and ? (shrink)