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Cognitive Architectures


Book Discussion - Cognitive Design for Artificial Minds

#artificialintelligence

Cognitive Design for Artificial Minds (Routledge/Taylor & Francis, 2021) explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental, and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. The event is moderated by Antonio Chella (Prof. of Robotics at the University of Palermo) The event is free (but the registration is mandatory) and will be held on Gather Town (you will receive the link once registered). The book "Cognitive Design for Artificial Minds" (with related editorial reviews) can be found at: Antonio Lieto is a researcher in Artificial Intelligence at the Department of Computer Science of the University of Turin, Italy, and a research associate at the ICAR-CNR in Palermo, Italy. He is the current Vice-President of the Italian Association of Cognitive Science (2017–2022) and an ACM Distinguished Speaker on the topics of cognitively inspired AI and artificial models of cognition.


Rethink Insurance With Cognitive Robotic Process Automation

#artificialintelligence

Now, a complex AI-driven automation initiative like cognitive RPA is majorly anchored by bots. They need extensive strategy, support, maintenance and trouble-shooting arsenal around it – in a continuous manner. Process changes, app updates, security patches etc. have a huge impact on bots and involve human intervention. Bot maintenance and governance should be a conscious and well-thought out strategy from day one. Having a Robotic Operations Center (ROC) in place makes the job easier for enterprises betting big on cognitive RPA.


Care Angel Appoints New Vice President of Business Development

#artificialintelligence

Care Angel, the leader in AI and voice-enabled nurse assistant technology, announced that Mike Hahn has recently joined the company as the new vice president of business development. In this role he will be responsible for leading the company's strategy for aggressive revenue growth moving forward. The appointment follows the recent news that prominent healthcare executive Bud Flagstad, will now serve as the company's new CEO. Mr. Hahn, holds an MBA and is a 15 year healthcare veteran who brings to the senior business development role a commitment to both innovation and collaboration as well as deep experience with large payer and provider relationships. He previously led the Innovation team for UnitedHealth Group's OptumCare unit where he was responsible for the strategy and implementation of new business opportunities and technology-enabled resources to serve various health care stakeholders spanning consumers, health care providers and practices.


Interview with Ali Boyle – talking AI, cognitive sciences and philosophy of mind

AIHub

Ali Boyle is currently a Research Fellow in Kinds of Intelligence at University of Cambridge and University of Bonn. Her main research field is philosophy of mind and psychology, focusing particularly on nonhuman minds and the methods used to study them. She holds a PhD in Philosophy from University of Cambridge. In this interview, we talk about artificial intelligence, cognitive sciences and philosophy of mind. My research focuses on theoretical questions about nonhuman minds: what are nonhuman minds like, and how can we learn about them?


Towards a Predictive Processing Implementation of the Common Model of Cognition

arXiv.org Artificial Intelligence

Modern machine learning techniques based on artificial neural networks (ANNs) are implemented through algebraic manipulations of vectors, matrices, and tensors in high-dimensional spaces. While ANNs have an impressive ability to process data to find patterns, they do not typically model high-level cognition. Furthermore, ANNs are usually models of only a single task. Otherwise, when an ANN is trained to learn a series of tasks, catastrophic interference occurs, with each new task causing the ANN to forget all previously learned tasks [8, 21, 22]. On the other hand, symbolic cognitive architectures, such as the widely used ACT-R [1, 31], can capture the complexities of high-level cognition but scale poorly to the naturalistic, non-symbolic data of sensory perception, e.g., images, or to big data sets necessary for modelling learning over a lifetime, e.g., corpora with hundreds of millions of words.


Philosophy for AI Enthusiasts

#artificialintelligence

"The biology of mind bridges the sciences -- concerned with the natural world -- and the humanities -- concerned with the meaning of human experience." Welcome to Part 3 of this new series exploring artificial general intelligence (AGI). If you missed Part 1 or Part 2, check them out; part 1 covers what AGI is, and part 2 is a brief overview of cognitive science for AI folk. This week we will introduce important concepts in philosophy of mind that I think every computer scientist, AI/ML researcher, or AGI enthusiast should know. The concept of minds--their nature, their implementation, their applications, etc.--are of huge interests to AGI researchers, and even anyone remotely interested in AI; arguably, this is the entire job of an AI/AGI researcher: creating artificial minds (some are just more narrow then others).


Philosophy for AI Enthusiasts

#artificialintelligence

"The biology of mind bridges the sciences -- concerned with the natural world -- and the humanities -- concerned with the meaning of human experience." Welcome to Part 3 of this new series exploring artificial general intelligence (AGI). If you missed Part 1 or Part 2, check them out; part 1 covers what AGI is, and part 2 is a brief overview of cognitive science for AI folk. This week we will introduce important concepts in philosophy of mind that I think every computer scientist, AI/ML researcher, or AGI enthusiast should know. The concept of minds--their nature, their implementation, their applications, etc.--are of huge interests to AGI researchers, and even anyone remotely interested in AI; arguably, this is the entire job of an AI/AGI researcher: creating artificial minds (some are just more narrow then others).


Whole brain Probabilistic Generative Model toward Realizing Cognitive Architecture for Developmental Robots

arXiv.org Artificial Intelligence

Through the developmental process, they acquire basic physical skills (such as reaching and grasping), perceptional skills (such as object recognition and phoneme recognition), and social skills (such as linguistic communication and intention estimation) (Taniguchi et al., 2018). This open-ended online learning process involving many types of modalities, tasks, and interactions is often referred to as lifelong learning (Oudeyer et al., 2007; Parisi et al., 2019). The central question in next-generation artificial intelligence (AI) and developmental robotics is how to build an integrative cognitive system that is capable of lifelong learning and humanlike behavior in environments such as homes, offices, and outdoor. In this paper, inspired by the human whole brain architecture (WBA) approach, we introduce the idea of building an integrative cognitive system using a whole brain probabilistic generative model (WB-PGM) (see 2.1). The integrative cognitive system can alternatively be referred to as artificial general intelligence (AGI) (Yamakawa, 2021). Against this backdrop, we explore the process of establishing a cognitive architecture for developmental robots. Cognitive architecture is a hypothesis about the mechanisms of human intelligence underlying our behaviors (Rosenbloom, 2011). The study of cognitive architecture involves developing a presumably standard model of the humanlike mind (Laird et al., 2017).


Towards Human-Like Automated Test Generation: Perspectives from Cognition and Problem Solving

arXiv.org Artificial Intelligence

Automated testing tools typically create test cases that are different from what human testers create. This often makes the tools less effective, the created tests harder to understand, and thus results in tools providing less support to human testers. Here, we propose a framework based on cognitive science and, in particular, an analysis of approaches to problem-solving, for identifying cognitive processes of testers. The framework helps map test design steps and criteria used in human test activities and thus to better understand how effective human testers perform their tasks. Ultimately, our goal is to be able to mimic how humans create test cases and thus to design more human-like automated test generation systems. We posit that such systems can better augment and support testers in a way that is meaningful to them.


Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma

arXiv.org Artificial Intelligence

Simulations, along with other similar applications like virtual worlds and video games, require computational models of intelligence that generate realistic and credible behavior for the participating synthetic characters. Cognitive architectures, which are models of the fixed structure underlying intelligent behavior in both natural and artificial systems, provide a conceptually valid common basis, as evidenced by the current efforts towards a standard model of the mind, to generate human-like intelligent behavior for these synthetic characters. Sigma is a cognitive architecture and system that strives to combine what has been learned from four decades of independent work on symbolic cognitive architectures, probabilistic graphical models, and more recently neural models, under its graphical architecture hypothesis. Sigma leverages an extended form of factor graphs towards a uniform grand unification of not only traditional cognitive capabilities but also key non-cognitive aspects, creating unique opportunities for the construction of new kinds of cognitive models that possess a Theory-of-Mind and that are perceptual, autonomous, interactive, affective, and adaptive. In this paper, we will introduce Sigma along with its diverse capabilities and then use three distinct proof-of-concept Sigma models to highlight combinations of these capabilities: (1) Distributional reinforcement learning models in; (2) A pair of adaptive and interactive agent models that demonstrate rule-based, probabilistic, and social reasoning; and (3) A knowledge-free exploration model in which an agent leverages only architectural appraisal variables, namely attention and curiosity, to locate an item while building up a map in a Unity environment.