This article attempts to examine the portrayal of the character of Arthur Fleck in Todd Phillips’s Joker (2019). In the initial part of the film, Arthur exhibits signs that reveal he is headed towards committing a violent crime. Arthur displays signs of psychopathy and a lack of empathy. This article links criminal behaviour analysis to the Bible of the Arya Samaj, an Indian text, to find out how ancient Indian literature’s empirical theories, which are intertwined with philosophical and religious content, (...) shed light on criminal behaviour. The primary purpose of this article is to analyse the behavioural pattern in the character of Fleck and establish links to Dayanand Saraswati’s Satyarth Prakash, which discusses different types of Avidyas, a key concept in Indian philosophy that can be translated as ignorance or unwisdom, which is seen as a fundamental cause of suffering and bondage. It is certainly a significant factor in a person’s personality and behaviour. The major aim of this character analysis is not only to establish the link between Indian ancient texts and criminological behavioural patterns but also to reconceptualize evil through the lenses of Vedic hermeneutics. Vedic hermeneutics is the study of interpreting and understanding the ancient Vedic texts of Hinduism, where an important aspect is the recognition of different levels of meaning within the texts. (shrink)

We demonstrate that the key components of cognitive architectures (declarative and procedural memory) and their key capabilities (learning, memory retrieval, probability judgment, and utility estimation) can be implemented as algebraic operations on vectors and tensors in a high‐dimensional space using a distributional semantics model. High‐dimensional vector spaces underlie the success of modern machine learning techniques based on deep learning. However, while neural networks have an impressive ability to process data to find patterns, they do not typically model high‐level cognition, and (...) it is often unclear how they work. Symbolic cognitive architectures can capture the complexities of high‐level cognition and provide human‐readable, explainable models, but scale poorly to naturalistic, non‐symbolic, or big data. Vector‐symbolic architectures, where symbols are represented as vectors, bridge the gap between the two approaches. We posit that cognitive architectures, if implemented in a vector‐space model, represent a useful, explanatory model of the internal representations of otherwise opaque neural architectures. Our proposed model, Holographic Declarative Memory (HDM), is a vector‐space model based on distributional semantics. HDM accounts for primacy and recency effects in free recall, the fan effect in recognition, probability judgments, and human performance on an iterated decision task. HDM provides a flexible, scalable alternative to symbolic cognitive architectures at a level of description that bridges symbolic, quantum, and neural models of cognition. (shrink)

A rich exhibition of Minnesota’s beloved libraries, with stunning photographs by the popular Doug Ohman and library stories by seven of Minnesota’s best-known writers of books for children and young adults.