Formalisms inspired by Quantum theory have been used in Cognitive Science for decades. Indeed, Quantum-Like (QL) approaches provide descriptive features that are inherently suitable for perception, cognition, and decision processing. A preliminary study on the feasibility of a QL robot perception model has been carried out for a robot with limited sensing capabilities. In this paper, we generalize such a model for multi-sensory inputs, creating a multidimensional world representation directly based on sensor readings. Given a 3-dimensional case study, we highlight how this model provides a compact and elegant representation, embodying features that are extremely useful for modeling uncertainty and decision. Moreover, the model enables to naturally define query operators to inspect any world state, which answers quantifies the robot's degree of belief on that state.

We consider an inverse reinforcement learning problem involving us versus an enemy radar equipped with a Bayesian tracker. By observing the emissions of the enemy radar,how can we identify if the radar is cognitive (constrained utility maximizer)? Given the observed sequence of actions taken by the enemy's radar, we consider three problems: (i) Are the enemy radar's actions (waveform choice, beam scheduling) consistent with constrained utility maximization? If so how can we estimate the cognitive radar's utility function that is consistent with its actions. We formulate and solve the problem in terms of the spectra (eigenvalues) of the state and observation noise covariance matrices, and the algebraic Riccati equation. (ii) How to construct a statistical test for detecting a cognitive radar (constrained utility maximization) when we observe the radar's actions in noise or the radar observes our probe signal in noise? We propose a statistical detector with a tight Type-II error bound. (iii) How can we optimally probe (interrogate) the enemy's radar by choosing our state to minimize the Type-II error of detecting if the radar is deploying an economic rational strategy, subject to a constraint on the Type-I detection error? We present a stochastic optimization algorithm to optimize our probe signal. The main analysis framework used in this paper is that of revealed preferences from microeconomics.

Cognitive computing typically refers to simulate human intelligence to enable computers to understand data and derive insights, all through the use of AI and machine learning. Applications of cognitive computing are enormous giving computers the human-like brain to compute data at fast. As a collection of algorithmic capabilities, the technology strengthens employee performance, automate complex workloads and create cognitive agents to mimic both human thoughts and engagement. Cognitive computing and its applications are holding much promise for Human Resources (HR), transforming HR functions and paving ways for new possibilities. As businesses always face challenges in acquiring adequate talent, in an IBM survey of 6,000 executives, 66 percent of CEOs believe cognitive computing can drive significant value in HR, while 50 percent of HR professionals acknowledge cognitive computing to drive transformation in HR.

Cognitive computing has expansive horizons, which covers various characteristics of cognition. In addition, cognitive science is an interdisciplinary, scientific study of the human reasoning, emotions, language, perception, attention, and memory. However, artificial intelligence (AI) is to explore the design of computers and software that would fit for intelligent behavior. The reconciliation of cognitive science and AI offers a profound comprehension of human cognition (human intelligence) and communication. What's more, the imaginative and technical abilities are applying the knowledge in AI solutions and applications in engineering psychology.

In an opinion piece in The New York Times written just as the novel coronavirus pandemic was taking off in New York City, author David Brooks used the phrase "plague eyes" to describe the radically new perspective that had begun to inject itself into the consciousness of everyone facing the unprecedented health threat. One of the things worth turning those eyes toward is the challenge of accessing trustworthy information at a time when new problems call for fast and far-reaching decisions from government politicians and bureaucrats, enterprise executives, healthcare leaders, small business owners, and individual families. At the time Brooks' column was written, there was confusing and contradictory information coming out of the healthcare agencies, the Trump administration, the Chinese government, the mainstream media, the European media, and the media of the right, to name a few sources of real and alternative fact. What is clear This is what we can see clearly after some months of reading, watching, and listening to the pronouncements on the crisis from around the globe: Content challenges continue to dog AI. For example, should AI be able to create its own channel of authoritative information so that both fake-news people and alternative-facts people can access "unvarnished" truths?

Cognitive collaboration will enable employers to improve workplace productivity, foster teamwork, and improve communication. Cognitive collaboration is the use of technologies such as artificial intelligence and machine learning in the workplace to enhance operations involving human interaction and communication, such as meetings or group discussions. The term is coined by Cisco and aims at providing services that help teams to collaboratively make smarter and faster decisions. The concept can be leveraged to build a strong workforce that can eventually help in the growth of the organization. Cognitive collaboration will help transform work operations by streamlining them, helping improve the interaction between employees.

The report titled "Cognitive Computing Market" report will be very useful to get a stronger and effective business outlook. It provides an in-depth analysis of different attributes of industries such as trends, SWOT analysis, policies, and clients operating in several regions. The qualitative and quantitative analysis techniques have been used by analysts to provide accurate and applicable data to the readers, business owners and industry experts. Cognitive Computing is completely changing the way organization use their big data in each verticals, especially in industries like Healthcare, BFSI and Customer services. This is big revolution in global information technology market and holds very strong potential of growth.

Today, the world is all about industry 4.0 and the technologies brought in by it. From Artificial Intelligence (AI) to Big Data Analytics, all technologies are transforming one or the other industries in some ways. AI-powered Cognitive Computing is one such technology that provides high scale automation with ubiquitous connectivity. More so, it is redefining how IoT technology operates. The need for Cognitive computing in the IoT emerges from the significance of information in present-day business.

A large body of compelling evidence has been accumulated demonstrating that embodiment - the agent's physical setup, including its shape, materials, sensors and actuators - is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe.

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build artificial agents able to exhibit intelligent behaviors in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs’ knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges.