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.

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.

Artificial intelligence (AI) and cognitive computing can work together closely by connecting technology with the physical world. The concepts of AI and cognitive computing are deployed widely in various sectors. AI and cognitive computing can self-learn and adapt to new surroundings. However, there is a slight difference between these two technologies. AI creates devices that can act smarter than humans, whereas cognitive computing creates devices that adapt to the surroundings and communicate with humans naturally.

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.

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.

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.

Human-level cognition (most uniquely characterized by our abilities to use language) should be seen as a superset of functional and behavioral capabilities shared by lower life-forms including animals and insects, and this perspective ought to principally guide our strategies for developing integrated cognitive architectures. Just as the study of biological model organisms has led to tremendous advances in our scientific knowledge of genetics and cellular function, the study of embodied cognition in simple agent-environment simulations can yield similar advances in Cognitive Science, Artificial Intelligence, and Robotics. By working first on the foundations of intelligent interaction with one's environment, and by focusing on core functions such as predictive and inductive learning, probabilistic goal-directed behavior compilation, and empathetic reasoning, we can better establish the grounding that the physical symbol system hypothesis assumes (Newell and Simon 1976), yet often without explicit demonstration of a mechanism to derive symbolic relations and semantics from raw sensory data. Logic and language are seen to emerge from our willingness to make discrete simplifying assumptions in a continuous and probabilistic world of experience, and developing a Standard Model of the Mind can help build much-needed bridges between historically non-aligned research communities.

While several unified theories of cognition have been proposed, no framework has been established with the same degree of universal agreement as in biology and physics. A universal model of cognition is needed to direct research, push cognitive sciences, and test more or less realistic interventions on shifting environments. Here, we propose the necessary components for modelling a socially oriented, generative, and adaptive agent. We argue such a model requires modules for information input, management, storage, and use in order to grow an agent capable of human-like adaptive, socio-cultural behavioural strategies. We further argue that such an agent may be tested in different contexts through Agent-Based Modelling.

We explore replacing the declarative memory system of the ACT-R cognitive architecture with a distributional semantics model. ACT-R is a widely used cognitive architecture, but scales poorly to big data applications and lacks a robust model for learning association strengths between stimuli. Distributional semantics models can process millions of data points to infer semantic similarities from language data or to infer product recommendations from patterns of user preferences. We demonstrate that a distributional semantics model can account for the primacy and recency effects in free recall, the fan effect in recognition, and human performance on iterated decisions with initially unknown payoffs. The model we propose provides a flexible, scalable alternative to ACT-R's declarative memory at a level of description that bridges symbolic, quantum, and neural models of cognition. Our intent is to advance toward a cognitive architecture capable of modeling human performance at all scales of learning.

Because navigation produces readily observable actions, it provides an important window into how perception and reasoning support intelligent behavior.