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.

This paper is an analysis of the different methods proposed to achieve AGI, including Human Brain Emulation, AIXI and Integrated Cognitive Architecture. First, the definition of AGI as used in this paper has been defined, and its requirements have been stated. For each proposed method mentioned, the method in question was summarized and its key processes were detailed, showcasing how it functioned. Then, each method listed was analyzed, taking various factors into consideration, such as technological requirements, computational ability, and adequacy to the requirements. It was concluded that while there are various methods to achieve AGI that could work, such as Human Brain Emulation and Integrated Cognitive Architectures, the most promising method to achieve AGI is Integrated Cognitive Architectures. This is because Human Brain Emulation was found to require scanning technologies that will most likely not be available until the 2030s, making it unlikely to be created before then. Moreover, Integrated Cognitive Architectures has reduced computational requirements and a suitable functionality for General Intelligence, making it the most likely way to achieve AGI.

OTT platforms are now the primary entertainment source for a lot of people. You want to watch a movie, catch up on your favorite TV show, or just kick back and support your sports team -- you can access any content more conveniently via streaming services. There is a ton of effort that goes into keeping all of those users engaged with the OTT platform. Apart from delivering the best content, the platforms have to think about ways to make the viewing experience the most convenient. So, let's talk about just that -- you can enhance user engagement by providing a better viewing experience with cognitive computing. Ever find yourself looking at the credits when the movie or a TV series episode is over?

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.

The Cognitive Ledger Project is an effort to develop a modular system for turning users' personal data into structured information and machine learning models based on a blockchain-based infrastructure. In this work-in-progress paper, we propose a cognitive architecture for cognitive digital twins. The suggested design embraces a cognitive blockchain (Cognitive ledger) at its core. The architecture includes several modules that turn users' activities in the digital environment into reusable knowledge objects and artificial intelligence that one day can work together to form the cognitive digital twin of users.

In recent years, the scope and reach of artificial intelligence and associated domains have increased. As artificial intelligence's popularity develops, there has been significant criticism about the technical jargon that surrounds it. Deep learning, deep learning, voice recognition, text analytics, cognitive computing, and neural networks are just a few of the terms that come to mind. Although these phrases are frequently used interchangeably, there is a significant difference in their techniques and objectives. Cognitive computing is one such technology, which is sometimes confused with AI technology but is actually quite distinct.

Modern Artificial Intelligence (AI) systems excel at diverse tasks, from image classification to strategy games, even outperforming humans in many of these domains. After making astounding progress in language learning in the recent decade, AI systems, however, seem to approach the ceiling that does not reflect important aspects of human communicative capacities. Unlike human learners, communicative AI systems often fail to systematically generalize to new data, suffer from sample inefficiency, fail to capture common-sense semantic knowledge, and do not translate to real-world communicative situations. Cognitive Science offers several insights on how AI could move forward from this point. This paper aims to: (1) suggest that the dominant cognitively-inspired AI directions, based on nativist and symbolic paradigms, lack necessary substantiation and concreteness to guide progress in modern AI, and (2) articulate an alternative, "grounded", perspective on AI advancement, inspired by Embodied, Embedded, Extended, and Enactive Cognition (4E) research. I review results on 4E research lines in Cognitive Science to distinguish the main aspects of naturalistic learning conditions that play causal roles for human language development. I then use this analysis to propose a list of concrete, implementable components for building "grounded" linguistic intelligence. These components include embodying machines in a perception-action cycle, equipping agents with active exploration mechanisms so they can build their own curriculum, allowing agents to gradually develop motor abilities to promote piecemeal language development, and endowing the agents with adaptive feedback from their physical and social environment. I hope that these ideas can direct AI research towards building machines that develop human-like language abilities through their experiences with the world.

Content in English has dominated the entertainment industry for decades. But as technology advances globalization, content creators will have to start thinking about how they can service their content in other languages. In doing so, they'll be able to not only become more inclusive of different audiences but help them scale their reach across regions without jeopardizing the entertainment value of their content. It's for that very reason why many have shied away from translating their audio-based content. Whether for TV, movies, or even podcasts, translating audio and video content in the past meant forfeiting the unique voices of the program who typically only speak English.

With AI systems becoming more powerful and pervasive, there is increasing debate about keeping their actions aligned with the broader goals and needs of humanity. This multi-disciplinary and multi-stakeholder debate must resolve many issues, here we examine three of them. The first issue is to clarify what demands stakeholders might usefully make on the designers of AI systems, useful because the technology exists to implement them. We make this technical topic more accessible by using the framing of cognitive architectures. The second issue is to move beyond an analytical framing that treats useful intelligence as being reward maximization only. To support this move, we define several AI cognitive architectures that combine reward maximization with other technical elements designed to improve alignment. The third issue is how stakeholders should calibrate their interactions with modern machine learning researchers. We consider how current fashions in machine learning create a narrative pull that participants in technical and policy discussions should be aware of, so that they can compensate for it. We identify several technically tractable but currently unfashionable options for improving AI alignment.

Cognitive computing is the amalgamation of cognitive science and is based on the basic premise of simulating the basic thought process. It is a combination of disruptive technologies like AI and machine learning with sentiment analysis and contextual awareness to solve daily problems, just like humans. It is used in different industries like healthcare, insurance and more. The goal of cognitive computing is to simulate human thought processes in a computerized model. Implementing self-learning algorithms that use data mining, pattern recognition and natural language processing, the machines can mimic the way human brains function.