Can we say that animals are more intelligent than #artificialintelligence? Why? They look similar, being unconscious in response to external conditions, automatic and programmed or trained in their behavior, containing both innate (inborn) and learned elements. In all, there are three grades/scales of intelligence: Natural non-human intelligence, marked with basic cognition, animal instincts, trial and error, unconscious and automatic behaviour and learned behaviour [from operant conditioning] through training, reinforcement or punishment; Natural human intelligence, marked with full cognition, causal reasoning, language, numeracy, problem-solving, learning, theory of mind, consciousness, self-awareness or sapience; Artificial or #machineintelligence, marked with world models and computing algorithms and trial and error, unconscious and automatic behaviour and learned behaviour from [operant conditioning] through data training, reinforcement or punishment; In reality, there is no human-like animal cognition, but animal reflexes and instincts, inherited patterns of behavior, as FAPs, caused by the hard-wired #neuralnetwork mechanisms, added with basic cognitions. For example, long-distance navigation should not be considered as #spatialcognition, when many animals travel thousands of kilometers in seasonal migrations or returns to breeding grounds. They may be guided by the sun, the stars, the polarization of light, magnetic cues, olfactory cues, winds, or a combination of these environmental variables.

What is the understanding about some of the most important conceptions in the philosophy of #AI? Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. My philosophy, science and engineering of #machineintelligence and learning is plain and clear, The basis of MIL is all quantitative sciences, as mathematics and physics, statistics, probability theory and #datascience and #engineering, all within the framework of the data universe ontology, Establishing quantitative structure and relationships between different quantities is the cornerstone of mathematical and physical sciences. Their progress is achieved due to transforming the abstract qualities of entities into quantities, like as postulating that all material bodies marked by quantitative properties or physical dimensions are subject to some measurements and observations.

What are The Sustainable Way to General AI? There are two approaches to General AI, namely: The mainstream, human-like, human-level Anthropocentric AI (AAI) Model of human intelligence (99.9999%); The AAI systems work by taking in large amounts of labeled training data, analyzing the data for spurious correlations and statistic patterns, and using those patterns to make predictions about future states. The AAI programming is imitating three human cognitive abilities: Learning, reasoning, and self-correction. Learning processes focuses on gathering data and creating rules as algorithms for how to turn the data into actionable information to instruct computing devices how performing a specific task.

Why Computers Can't Think the Way We Do? Ever since Alan Turing, AI enthusiasts have equated artificial intelligence with human intelligence. This is a profound mistake. AI works on inductive reasoning, crunching data sets to predict outcomes. But humans don't correlate data sets: we make conjectures informed by context and experience. Human intelligence is a web of best guesses, given what we know about the world.

Why Machine Intelligence is NOT Artificial [Human] Intelligence? AI is NOT Machine Learning, ML is NOT AHI, and Artificial NNs are NOT Human NNs. AA/AI rule 2: All Artificial Intelligence is Artificial Human Intelligence (AHI), divided as Narrow AI, Artificial General Intelligence (AGI), or Artificial Superintelligence (ASI). AA/AI rule 3: Real Machine Intelligence (MI) is NOT Artificial Human Intelligence (AHI), the capability of a computer system to mimic human intelligence/cognitive functions/behavior such as perception, learning, reasoning and problem-solving or NL communication. AA/AI rule 4: Machine Learning is NOT AHI.

Systems science, systems research, or, systems, is an interdisciplinary field concerned with understanding systems, from simple to complex, in nature, society, cognition, engineering, technology and science itself. The field is spanning the formal, natural, social, and applied sciences. If Systems Science is an interdisciplinary field that studies the complexity of systems in nature, social or any other scientific field, then the Systems Ontology is a transdisciplinary R & D of the complexity of causal systems in all fields of science, engineering and practice. Then to systems ontologists and scientists, the world can be understood as the universal system of real-world systems or the global network of causal networks. The real-world approach to reality is the theory of the world of systems (systems ontology), where a system is a network of interacting or interrelated or interdependent entities that act according to causal/logical rules to form a unified whole, a complex emerging entity.

Is Real AI Superintelligence the Fundamental Solution of Human Problems? RAIS is like a scientific modelling makes a particular part or feature of the world to automatically understand, define, quantify, visualize, or simulate by referencing to its encoded/programmed world's data/information/knowledge base. The RAIS is to run the Master Algorithm of Reality and Mentality as Descriptive, Deductive, Intuitive, Inductive, Exploratory, Explainable, Predictive and Prescriptive (DDIIEEPP) Platform. In all, the RAI program implies radically innovative approaches and paradigmatic shifts in fundamental knowledge fields and advanced technology domains, as reality and mentality, causality, science, technology and statistics, AI and ML, data and intelligence, information and knowledge, AI software and hardware, cyberspace and intelligent robotics. The RAIS Platform could compute the real world as a whole and in parts [e.g., the causal nexus of various human domains, such as fire technology and human civilizations; globalization and political power; climate change and consumption; economic growth and ecological destruction; future economy, unemployment and global pandemic; wealth and corruption, perspectives on the world's future, etc.].

Is artificial intelligence manipulating the human behaviour? Given an AI that can read your mind completely, and one that desires to manipulate you into doing what it wants, would we be able to resist? I have bad news for you. We have been already manipulated by narrow AI models, ML algorithms, codes, programs and DL applications, such as recommendation engines. Google and Facebook know their users better than their families and friends do.

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 Are we using #artificialintelligence to determine a theory of everything? It is a real and true AI, which is about modeling and simulation everything in terms of the theory of everything. The so-called "Classic/symbolic/logical AI" is dead due to the large-scale AI projects, as GOFAI, CYC, Soar, Japan's 5th Generation CI, US SCI, WBE, failed and closed or failing. The whole construct of AI, be it weak AI or strong AI, full AI, or HL AI, is turned speculative due to its failed program of simulating human reasoning by formal logical means. Too many AI investments end up as "pretty shiny objects" that don't pay off.