Pinaki Laskar on LinkedIn: #AI #MachineLearning #DeepLearning


AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Meta-AI is about modeling and simulating reality, causality, mentality by digital technologies. It is key source of data is science as the sum of universal knowledge, all the world's information as coordinated and systematized. It is typically divided into three major branches that consist of the following, - the natural sciences (e.g., biology, chemistry, and physics), which study nature in the broadest sense; - the social sciences (e.g., economics, psychology, and sociology), which study individuals and societies; - the formal sciences (e.g., logic, mathematics, and theoretical computer science), which deal with symbols governed by rules; As to empiricism, stating that knowledge comes only or primarily from sensory experience, both the philosophical sciences and the formal sciences as well as mathematics are out of any science as they do not rely on empirical evidence. It is plain and clear, data or information or knowledge have real value if only coordinated and systematized and organized. Again, drawing on pattern recognition and computational learning theory, Meta-ML is dedicated to the study of problem-solving by computer programs in general, enabling computers to reason about the world and learn from data, to effectively interact with any realities, physical, mental, social, digital, or virtual. AI/ML/DL modelling should consist of the following necessary features, Meta-physical Assumptions: prior knowledge, the basis of our knowing, understanding, or thinking about the whole world or a domain problem (primary causes, principles and elements).

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