AI taken to another level as IBM Watson analysis data in the sky. IBM is taking to the skies with its latest Watson deployment, with Korean Air using the AI tech to help crews on the ground with aircraft maintenance. Using machine learning, Korean Air wants to increase the safety of their planes by taking advantage of the data collection, analytical ability and AI capabilities of IBM's Watson. IBM's Watson will do this by collating data from technical guidelines, inventory and in-flight incident history to analyse a current or potential future problem. In doing so, it will help to identify the cause of these problems and quickly offer an effective solution.

The development of a cognitive architecture based on neurons is currently viable. An initial architecture is proposed, and is based around a slow serial system, and a fast parallel system, with additional subsystems for behaviours such as sensing, action and language. Current technology allows us to emulate millions of neurons in real time supporting the development and use of relatively sophisticated systems based on the architecture. While knowledge of biological neural processing and learning rules, and cognitive behaviour is extensive, it is far from complete. This architecture provides a slowly varying neural structure that forms the framework for cognition and learning. It will provide support for exploring biological neural behaviour in functioning animals, and support for the development of artificial systems based on neurons.

Cognitive architectures are generally considered to be theories of the innate capabilities of the (human) cognitive system.Any knowledge that is not innate is encoded in the architectures memory systems, either by the modeler or learned by the architecture itself. However, in humanintelligent behavior few things are innate. An alternative is to acknowledge that learning occurs at different levels of abstraction. A standard model of the mind should therefore span multiple levels of abstraction, encouraging research efforts to establish learning mechanism that connect them.

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.

The widely demonstrated ability of humans to deal with multiple representations of information has a number of important implications for a proposed standard model of the mind (SMM). In this paper we outline four and argue that a SMM must incorporate (a) multiple representational formats and (b) meta-cognitive processes that operate on them. We then describe current approaches to extend cognitive architectures with visual-spatial representations, in part to illustrate the limitations of current architectures in relation to the implications we raise but also to identify the basis upon which a consensus about the nature of these additional representations can be agreed. We believe that addressing these implications and outlining a specification for multiple representations should be a key goal for those seeking to develop a standard model of the mind.

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.

This paper presents a new Retinotopic Reasoning (R2) cognitive architecture that is inspired by studies of visual mental imagery in people. R2 is a hybrid symbolic-connectionist architecture, with certain components of the system represented in propositional, symbolic form, but with a primary working memory store that contains visual ``mental'' images that can be created and manipulated by the system. R2 is not intended to serve as a full-fledged, stand-alone cognitive architecture, but rather is a specialized system focusing on how visual mental imagery can be represented, learned, and used in support of intelligent behavior. Examples illustrate how R2 can be used to model human visuospatial cognition on several different standardized cognitive tests, including the Raven's Progressive Matrices test, the Block Design test, the Embedded Figures test, and the Paper Folding test.

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.

A standard model of how brains produce natural cognition would provide a framework for organizing cognitive neuroscience research. A recent effort (Laird et al., in press) to build on consensus views of cognitive operations and produce a standard model of natural cognition started with common aspects of well-established cognitive architectures ACT-R, Sigma, and SOAR. The model captures scientific consensus on “how” the brain works, but it does not offer a coherent story for “why” the component modules (i.e., working memory, long-term memory, visual and motor areas) exist and interact in the ways described. This manuscript starts with background information on a well-cited theory of action selection, and extends that theory to a fuller explanation of decision-making, action and perception that includes a framework for the elements of cognition.

If you've been seeing the word "cognitive" a lot lately, you're not alone. And if you're confused about exactly what it means from an IT and business perspective, you're not alone in that either. To help provide some clarity around the cognitive concept and what it might mean for your organization, I've put together this primer. Cognitive computing uses technology and algorithms to automatically extract concepts and relationships from data, understand their meaning, and learn independently from data patterns and prior experience--extending what people or machines could do on their own, says Paul Roma, chief analytics officer at consulting firm Deloitte Consulting. Deloitte refers to cognitive computing as "more encompassing than the traditional, narrow view of AI [artificial intelligence]," Roma says.