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Cognitive Architectures


Tenyx Announces $15 Million in Seed Funding

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Tenyx, an AI technology company, announced it has raised $15 million in seed funding to grow its research and development team and further product development. Investors include AME Cloud Ventures, Cota Capital, Morado Ventures, Pathbreaker Ventures, Point72 Ventures and StageOne Ventures, as well as notable angel investors John Lilly, Georges Harik and Jaan Tallinn. Tenyx is led by the founding team behind Apprente, which developed the world's first voice-based AI solutions to automate the order-taking process at drive-thru restaurants. Apprente was acquired by McDonald's Corporation and subsequently by IBM. Tenyx' seasoned leadership team includes Dr. Itamar Arel, a former professor of AI and CEO at Apprente, and Prof. Ron Christly, an established AI researcher and head of the Cognitive Science program at Sussex University.


Cognitive Science: Bermúdez, José Luis: 9781108440349: Books: Amazon.com

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Educated at St Pauls School, London and Cambridge University, José Luis Bermúdez is Professor of Philosophy at Texas A&M University, where he has also served as Dean of Liberal Arts and Associate Provost for Strategic Planning. Since his first book, The Paradox of Self-Consciousness (MIT Press 1998) he has been working on interdiscipinary aspects of self-representation and self-consciousness, most recently in Understanding "I": Language and Thought (OUP, 2017) and The Bodily Self: Selected Essays (MIT Press, 2018). He also works on rationality and reasoning, where he has published Decision Theory and Rationality (OUP, 2009). He is currently writing a book of framing and rationality, and also preparing the third edition of his textbook Cognitive Science: An Introduction to the Science of the Mind, both for Cambridge University Press. His work has appeared in seven languages and he is one of the 100 most cited philosophers on Google scholar.


Neurons Acquires Key Competitors Boosting Global Dominance

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Neurons, the world's leading applied neuroscience company has moved a step closer to becoming the leading platform for predicting human behaviour by acquiring two of its competitors, VisualEyes and Loceye. Latest Aithority Insights: E-con Systems Launches A Ready To Deploy AI Vision Kit With E-con's Sony Imx415 Based 4k… The move brings 35,000 new clients with a projected 1,000 more each month and enables Neurons to focus on using AI to accurately predict human emotions and sentiments. It boosts the company's competitive advantage and dramatically increases the growth and accuracy of its customer prediction platform, revolutionising the way brands do business. Companies often fail to predict, know, or understand consumer responses. Neurons' technology has enabled major corporations such as Facebook, TikTok, and Ikea to optimize every part of their customer journey from advertising and retail to innovation and beyond.


Cognitive science and artificial intelligence: simulating the human mind and its complexity

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A 4-compartment permeability-limited brain (4Brain) model consisting of brain blood, brain mass, cranial and spinal cerebrospinal fluid (CSF) compartments has been developed and incorporated into a whole body physiologically-based pharmacokinetic (PBPK) model within the Simcyp Simulator. The model assumptions, structure, governing equations and system parameters are described. The model in particular considers the anatomy and physiology of the brain and CSF, including CSF secretion, circulation and absorption, as well as the function of various efflux and uptake transporters existing on the blood-brain barrier (BBB) and blood-CSF barrier (BCSFB), together with the known parameter variability. The model performance was verified using in vitro data and clinical observations for paracetamol and phenytoin. The simulated paracetamol spinal CSF concentration is comparable with clinical lumbar CSF data for both intravenous and oral doses.


Predicting the intended action using internal simulation of perception

arXiv.org Artificial Intelligence

This article proposes an architecture, which allows the prediction of intention by internally simulating perceptual states represented by action pattern vectors. To this end, associative self-organising neural networks (A-SOM) is utilised to build a hierarchical cognitive architecture for recognition and simulation of the skeleton based human actions. The abilities of the proposed architecture in recognising and predicting actions is evaluated in experiments using three different datasets of 3D actions. Based on the experiments of this article, applying internally simulated perceptual states represented by action pattern vectors improves the performance of the recognition task in all experiments. Furthermore, internal simulation of perception addresses the problem of having limited access to the sensory input, and also the future prediction of the consecutive perceptual sequences. The performance of the system is compared and discussed with similar architecture using self-organizing neural networks (SOM).


Spraragen

AAAI Conferences

Understanding the interaction between emotion and cognitive processes is important for developing architectures for general intelligence, and vital for the fields of human social and behavioral modeling, game intelligence, and human-computer interaction. However, relatively little work in AI has been done on emotion in intelligent architectures, particularly on the effect of emotions on cognitive processes such as inference, planning and learning, despite research showing that emotion is a crucial and often beneficial factor in human decision-making. My work will provide a new emotional-cognitive architecture, focusing on a small set of theories, mechanisms and algorithms for the modeling of a wide array of emotional effects on human cognitive processes. The work and its results will be evaluated against current computational models of cognition and emotion, and validated by results from human cognitive science, neuroscience, and psychology.


Computational Metacognition

arXiv.org Artificial Intelligence

Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems that seeks to leverage ideas from human metacognition and from metareasoning approaches in artificial intelligence. The key characteristic is to declaratively represent and then monitor traces of cognitive activity in an intelligent system in order to manage the performance of cognition itself. Improvements in cognition then lead to improvements in behavior and thus performance. We illustrate these concepts with an agent implementation in a cognitive architecture called MIDCA and show the value of metacognition in problem-solving. The results illustrate how computational metacognition improves performance by changing cognition through meta-level goal operations and learning.


An Analysis and Comparison of ACT-R and Soar

arXiv.org Artificial Intelligence

This is a detailed analysis and comparison of the ACT-R and Soar cognitive architectures, including their overall structure, their representations of agent data and metadata, and their associated processing. It focuses on working memory, procedural memory, and long-term declarative memory. I emphasize the commonalities, which are many, but also highlight the differences. I identify the processes and distinct classes of information used by these architectures, including agent data, metadata, and meta-process data, and explore the roles that metadata play in decision making, memory retrievals, and learning.


Social Neuro AI: Social Interaction as the "dark matter" of AI

arXiv.org Artificial Intelligence

We are making the case that empirical results from social psychology and social neuroscience along with the framework of dynamics can be of inspiration to the development of more intelligent artificial agents. We specifically argue that the complex human cognitive architecture owes a large portion of its expressive power to its ability to engage in social and cultural learning. In the first section, we aim at demonstrating that social learning plays a key role in the development of intelligence. We do so by discussing social and cultural learning theories and investigating the abilities that various animals have at learning from others; we also explore findings from social neuroscience that examine human brains during social interaction and learning. Then, we discuss three proposed lines of research that fall under the umbrella of Social NeuroAI and can contribute to developing socially intelligent embodied agents in complex environments. First, neuroscientific theories of cognitive architecture, such as the global workspace theory and the attention schema theory, can enhance biological plausibility and help us understand how we could bridge individual and social theories of intelligence. Second, intelligence occurs in time as opposed to over time, and this is naturally incorporated by the powerful framework offered by dynamics. Third, social embodiment has been demonstrated to provide social interactions between virtual agents and humans with a more sophisticated array of communicative signals. To conclude, we provide a new perspective on the field of multiagent robot systems, exploring how it can advance by following the aforementioned three axes.


Making AI 'Smart': Bridging AI and Cognitive Science

arXiv.org Artificial Intelligence

The last two decades have seen tremendous advances in Artificial Intelligence. The exponential growth in terms of computation capabilities has given us hope of developing humans like robots. The question is: are we there yet? Maybe not. With the integration of cognitive science, the 'artificial' characteristic of Artificial Intelligence (AI) might soon be replaced with 'smart'. This will help develop more powerful AI systems and simultaneously gives us a better understanding of how the human brain works. We discuss the various possibilities and challenges of bridging these two fields and how they can benefit each other. We argue that the possibility of AI taking over human civilization is low as developing such an advanced system requires a better understanding of the human brain first.