What is the place of emotion in intelligent robots? In the past two decades, researchers have advocated for the inclusion of some emotion-related components in the general information processing architecture of autonomous agents, say, for better communication with humans, or to instill a sense of urgency to action. The framework advanced here goes beyond these approaches and proposes that emotion and motivation need to be integrated with all aspects of the architecture. Thus, cognitive-emotional integration is a key design principle. Emotion is not an "add on" that endows a robot with "feelings" (for instance, reporting or expressing its internal state). It allows the significance of percepts, plans, and actions to be an integral part of all its computations. It is hypothesized that a sophisticated artificial intelligence cannot be built from separate cognitive and emotional modules. A hypothetical test inspired by the Turing test, called the Dolores test, is proposed to test this assertion.

Mistaking cognitive computing for just another AI misses the important contributions this computing platform offers. In 2011, two episodes of Jeopardy stunned the world when the best Jeopardy players in the history squared off against IBM's Watson Cognitive Computing System and were soundly beaten. For many, this was the moment when artificial intelligence probably became a very real thing in their minds; one contestant even scrawled "I, for one, welcome our future computer overlords" on his answer in his final losing round. He likely spoke for many in the audience. Watson dominated a game where nuanced wordplay was intrinsic to the challenge of the contest, where contestants needed to provide the question that fit an answer shrouded in double meaning.

Artificial intelligence has been a far-flung goal of computing since the conception of the computer, but we may be getting closer than ever with new cognitive computing models. Cognitive computing comes from a mashup of cognitive science -- the study of the human brain and how it functions -- and computer science, and the results will have far-reaching impacts on our private lives, healthcare, business, and more. The goal of cognitive computing is to simulate human thought processes in a computerized model. Using self-learning algorithms that use data mining, pattern recognition and natural language processing, the computer can mimic the way the human brain works. While computers have been faster at calculations and processing than humans for decades, they haven't been able to accomplish tasks that humans take for granted as simple, like understanding natural language, or recognizing unique objects in an image.

Mistaking cognitive computing for just another AI misses the important contributions this computing platform offers. In 2011, two episodes of Jeopardy stunned the world when the best Jeopardy players in the history squared off against IBM's Watson Cognitive Computing System and were soundly beaten. For many, this was the moment when artificial intelligence probably became a very real thing in their minds; one contestant even scrawled "I, for one, welcome our future computer overlords" on his answer in his final losing round. He likely spoke for many in the audience. Watson dominated a game where nuanced wordplay was intrinsic to the challenge of the contest, where contestants needed to provide the question that fit an answer shrouded in double meaning.

Mistaking cognitive computing for just another AI misses the important contributions this computing platform offers. In 2011, two episodes of Jeopardy stunned the world when the best Jeopardy players in the history squared off against IBM's Watson Cognitive Computing System and were soundly beaten. For many, this was the moment when artificial intelligence probably became a very real thing in their minds; one contestant even scrawled "I, for one, welcome our future computer overlords" on his answer in his final losing round. He likely spoke for many in the audience. Watson dominated a game where nuanced wordplay was intrinsic to the challenge of the contest, where contestants needed to provide the question that fit an answer shrouded in double meaning.

Artificial intelligence, machine learning, and cognitive computing in audit and tax are the top trends that will impact the accounting and finance world over the next three years, according to research conducted by the Maryland Association of CPAs, the Business Learning Institute, and world-renowned futurist Daniel Burrus. The research began with Burrus' "Top 20 Technology-Driven Hard Trends Shaping 2018 and Beyond." Using Burrus' annual list as a starting point, MACPA Executive Director Tom Hood asked more than 1,000 CPAs and finance and accounting professionals which of those trends will have the greatest impact on the profession over the next three years. "These trends highlight enormous, game-changing opportunities in a broad array of applications and industries," Burrus has said. "As you read through them, look for opportunities for you to leverage them and become a positive disruptor."

Cognitive computing has taken the tech industry by storm and has become the new buzzword among entrepreneurs and tech enthusiasts. Based on the basic premise of stimulating the human thought process, the applications and advantages of cognitive computing are a step beyond the conventional AI systems. According to David Kenny, General Manager, IBM Watson – the most advanced cognitive computing framework, "AI can only be as smart as the people teaching it." The same is not true for the latest cognitive revolution. Cognitive computing process uses a blend of artificial intelligence, neural networks, machine learning, natural language processing, sentiment analysis and contextual awareness to solve day-to-day problems just like humans. IBM defines cognitive computing as an advanced system that learns at scale, reason with purpose and interacts with humans in a natural form.

Concept-cognitive learning (CCL) is a hot topic in recent years, and it has attracted much attention from the communities of formal concept analysis, granular computing and cognitive computing. However, the relationship among cognitive computing (CC), concept-cognitive computing (CCC), CCL and concept-cognitive learning model (CCLM) is not clearly described. To this end, we first explain the relationship of CC, CCC, CCL and CCLM. Then, we propose a generalized concept-cognitive learning (GCCL) from the point of view of machine learning. Finally, experiments on some data sets are conducted to verify the feasibility of concept formation and concept-cognitive process of GCCL.

Human-robot communication in situated environments involves a complex interplay between knowledge representations across a wide variety of modalities. Crucially, linguistic information must be associated with representations of objects, locations, people, and goals, which may be represented in very different ways. In previous work, we developed a Consultant Framework that facilitates modality-agnostic access to information distributed across a set of heterogeneously represented knowledge sources. In this work, we draw inspiration from cognitive science to augment these distributed knowledge sources with Short Term Memory Buffers to create an STM-augmented algorithm for referring expression generation. We then discuss the potential performance benefits of this approach and insights from cognitive science that may inform future refinements in the design of our approach.

Modern day Cognitive Computing date back to the late 19th century, with the work of mathematician George Boole and his book The Laws of Thought, and the propositions of Charles Babbage on creating what he termed an "analytical engine." The term Artificial Intelligence (AI) was coined by the late John McCarthy in 1955 (revised in 2007), when he defined AI as "the science and engineering of making intelligent machines." Artificial intelligence has been a far-flung goal of computing since the conception of the computer, but we may be getting closer than ever with new cognitive computing models. While computers have been faster at calculations and processing than humans for decades, they haven't been able to accomplish tasks that humans take for granted as simple, like understanding natural language, or recognizing unique objects in an image. The study of AI really began to excel during the 1980s when funding increased considerably over previous decades to develop new technologies into Machine Learning and AI. Then on May 11, 1997 the world's imagination was captivated when IBM's Deep Blue beat Garry Kasparov, the current world chess champion.