In online learning, the ability to provide quick and accurate feedback to learners is crucial. In skill-based learning, learners need to understand the underlying concepts and mechanisms of a skill to be able to apply it effectively. While videos are a common tool in online learning, they cannot comprehend or assess the skills being taught. Additionally, while Generative AI methods are effective in searching and retrieving answers from a text corpus, it remains unclear whether these methods exhibit any true understanding. This limits their ability to provide explanations of skills or help with problem-solving. This paper proposes a novel approach that merges Cognitive AI and Generative AI to address these challenges. We employ a structured knowledge representation, the TMK (Task-Method-Knowledge) model, to encode skills taught in an online Knowledge-based AI course. Leveraging techniques such as Large Language Models, Chain-of-Thought, and Iterative Refinement, we outline a framework for generating reasoned explanations in response to learners' questions about skills.

Recent studies of the applications of conversational AI tools, such as chatbots powered by large language models (LLMs), to complex real-world knowledge work have shown limitations related to reasoning and multi-step problem solving. Specifically, while existing chatbots simulate shallow reasoning and understanding they are prone to errors as problem complexity increases. The failure of these systems to address complex knowledge work is due to the fact that they do not perform any actual cognition. In this position paper, we present a higher-level framework ("Cognitive AI") for implementing programmatically defined neuro-symbolic cognition above and outside of large language models. Specifically, we propose a dual-layer functional architecture for Cognitive AI that serves as a roadmap for AI systems that can perform complex multi-step knowledge work. We propose that Cognitive AI is a necessary precursor for the evolution of higher forms of AI, such as AGI, and specifically claim that AGI cannot be achieved by probabilistic approaches on their own. We conclude with a discussion of the implications for large language models, adoption cycles in AI, and commercial Cognitive AI development.

The original vision of AI was re-articulated in 2002 via the term 'Artificial General Intelligence' or AGI. This vision is to build 'Thinking Machines' - computer systems that can learn, reason, and solve problems similar to the way humans do. This is in stark contrast to the 'Narrow AI' approach practiced by almost everyone in the field over the many decades. While several large-scale efforts have nominally been working on AGI (most notably DeepMind), the field of pure focused AGI development has not been well funded or promoted. This is surprising given the fantastic value that true AGI can bestow on humanity. In addition to the dearth of effort in this field, there are also several theoretical and methodical missteps that are hampering progress. We highlight why purely statistical approaches are unlikely to lead to AGI, and identify several crucial cognitive abilities required to achieve human-like adaptability and autonomous learning. We conclude with a survey of socio-technical factors that have undoubtedly slowed progress towards AGI.

We all have an intuitive idea of what it means but what really makes up the foundation elements of context, its dimensional existence and how can this be generalized to advantage in AI systems. We know that in humans context provides a measure between things but it also provides attention, flow, relativity, perception, response and much more. To truly be able to optimize the benefits and application of context in AI we must first do a deep dive into what context truly is. Context is more than just meaning and'meaning' itself is relatively hard to define although the use of meaning is not. In the easiest form of context, words have'meaning' associated with them.

Nothing can beat human thinking in any way. Most programming experts are on the verge to create such a computer system that can think and reason without any human intervention. Basically, they are working on cognitive artificial intelligence that can process human thought into a computerized model. A cognitive computer is a system that learns at scale, reasons with purpose, and interacts like humans on a natural basis. Instead of being programmed, these systems work through learning and reasoning from their interactions with human beings.

Activity Regulated Cytoskeleton (ARC) associated proteins are proteins in the brains of humans that are encoded through the transcription process by ARC genes and spread through the mRNA process. These proteins primarily impact synapse receptors of the kind found throughout the human brain and that are the foundation of human cognition and intelligence. ARC proteins are used in a variety of human and animal brain base functions such as memory, basic neurological signaling and complex thought. It is also believed that these genes are not just the foundation of early human development but can carry and pass'information' beyond human cognition using viral delivery methodologies. The ARC proteins form folded structures that can bind with a cell to deliver a code genome into the cell and then use the cells own machinery to replicate the RNA.

AJ Abdallat is CEO of Beyond Limits, a leader in artificial intelligence and cognitive computing. Artificial intelligence (AI), machine learning (ML) and similar digitalization solutions are modifying the way the world's most influential companies and industries -- as well as entire cities -- function every day. When working in harmony with humans, AI and other automation systems have the potential to make huge impacts on economic growth across the globe, going so far as to support solving humanity's most critical roadblocks, from streamlining energy production to improving grid systems and achieving more sustainable operations for nearly every major industry on Earth. As the CEO of an AI company making advanced digitalization software products and solutions, the paradigm of enabling people and AI to work together on achieving more sustainable operations is always top of mind; its importance cannot be curbed. As we move into the future, I'm confident there will be plenty of jobs for both humans and AI so long as they are able to function in conjunction with one another.

This is the last excerpt I will release from the Artificial Superintelligence Handbook IV (available globally on Amazon). ASIH IV is the last in the Superintelligence Handbook series documenting the design and development roadmap for a superintelligence. This final step in the design is the move to Deep Cognitive AI which is not covered in this series. The full chapter is contained in the ASIH4. Imagine if you could look at a dimmer switch and have it instantly turn on to the exact correct level without touching it. In the previous chapter I discussed Neuromorphic systems & how they are being tested & optimized for Cognitive Artificial Intelligence.

To foster the next level of accessibility and inclusion, it's time to start investing our efforts into developing more sophisticated cognitive AI machines. Developing more sophisticated forms of cognitive AI is the key to expanding global accessibility and broadening the scope of inclusion. In fact, we already see unprecedented language coverage. Flint Capital notes that recent research shows the number of machine translation language pairs has soared from 16,000 to about 100,000 in a single year. On top of this, Flint Capital also notes that the global cognitive computing market is projected to surge to $72.26 billion by 2027. We already see huge gains with the rapid development of new AI tech that pushes the existing limits of voice synthesis and speech recognition.

AJ Abdallat is CEO of Beyond Limits, the leader in artificial intelligence and cognitive computing. Artificial intelligence (AI) is making an impression on businesses and professionals. The impact has even been compared to the invention of the internet. We've only seen the tip of the iceberg of this technology's capabilities on macro and micro levels for individuals and venture groups to entire societies. AI interests have grown over the past decade, and we'll continue that trend as more digital transformation efforts are implemented.