Today's AI is largely machine learning techniques, deep learning algorithms and deep neural networks can't identify causality, its elements and structures, processes and mechanisms, rules and relationships, data and models, all what makes our world. This leads to all sorts of decision and prediction errors, data and algorithmic biases, the lack of quality data, and implementation failings, or the absence of real machine intelligence and learning. Correlation-based ML; Predictions only; Limited explainability; Spirals out of control in novel situations; Minimal human-machine interaction; Constrained by historical data; No guarantees on fairness; Needs a lot of data; True AI will emerge as Causal AI, State-of-the-Art AI Causal AI True AI: Real AI Platform. Decision-making AI: Causal AI doesn't just predict the future, it shapes it. Explainable AI: Put the "cause" in "because" with next-generation explainable AI.

Real AI is not data engineering or coding and software engineering skills, in big data tools or developer's skills in Python, R, Java, MATLAB, C or any other programming language desired, combined with machine learning skills. Keep a big view of Real AI as growing via three human intelligence faking levels to the Trans-AI: Artificial Narrow Intelligence (ANI)/ML/DLNNs; Artificial General Intelligence (AGI)/Human-Level AI; Artificial Super Intelligence (ASI); Trans-AI, Real and True AI, Meta-AI, Causal Machine Intelligence and Learning Man-Machine Hyperintelligence, the most disruptive integrative general-purpose technology.

Is artificial intelligence blurring the line between reality and fantasy? The science fantasy is becoming the science reality, "the improbable made possible" while "the impossible made probable".

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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 Is it important that an intelligent system act like a human? The real AI is about reality and causality, with its mentality encoded in digital reality. In fact, there is a single AI, which is real and true AI. Its a reality/causality-based machine intelligence, which is synergetic to human intelligence. So, there must be Causal AI, ML, DL, ANN and others, instead of Symbolic or Statistic AI, ML, DL or others.

Today, the term artificial intelligence (AI) is thrown around rather generously. As businesses around the world become more open to making waves and ditching legacy technologies in their quest to become data-driven, an ever-increasing number of tech deployments are claiming to use AI or machine learning (ML). But, frankly, it's often not true AI that is being used. The problem is, AI doesn't have a widely recognised definition, so it's hard to draw a line between what is AI and what isn't. In recent years, multiple businesses have invested in tools and technologies to help them understand their data, ultimately looking to maximise efficiency and provide the best possible experience for their customers.

Most people in AI don't care too much about the details, says Jeff Hawkins, a neuroscientist and tech entrepreneur. He wants to change that. Hawkins has straddled the two worlds of neuroscience and AI for nearly 40 years. In 1986, after a few years as a software engineer at Intel, he turned up at the University of California, Berkeley, to start a PhD in neuroscience, hoping to figure out how intelligence worked. But his ambition hit a wall when he was told there was nobody there to help him with such a big-picture project. Frustrated, he swapped Berkeley for Silicon Valley and in 1992 founded Palm Computing, which developed the PalmPilot--a precursor to today's smartphones.

Sometimes you initiate an action and in a domino-like manner it gets going and going, seemingly feeding off itself and rapidly agitating in an almost unstoppable manner. For example, you might be familiar with those popular YouTube videos of a beaker that when filled with a special liquid will spontaneously gush out foam, akin to a type of chain reaction. History indicates that during the initial creation of the atomic bomb, some of the scientists involved were concerned that if the atomic bomb was set off, it might begin a chain reaction due to igniting a fission explosion in the air, and would generate a globally wide conflagration. There is a venue today in which a chain reaction phenomenon is being bandied about by researchers and scientists. Some vehemently assert that we are potentially going to have an AI "intelligence explosion" that will someday occur, and there are various bets that this might happen somewhere between the year 2050 and the year 2100.

An AI intelligence explosion might lead to true AI or more. Sometimes you initiate an action and in a domino-like manner it gets going and going, seemingly feeding off itself and rapidly agitating in an almost unstoppable manner. For example, you might be familiar with those popular YouTube videos of a beaker that when filled with a special liquid will spontaneously gush out foam, akin to a type of chain reaction. History indicates that during the initial creation of the atomic bomb, some of the scientists involved were concerned that if the atomic bomb was set off, it might begin a chain reaction due to igniting a fission explosion in the air, and would generate a globally wide conflagration. There is a venue today in which a chain reaction phenomenon is being bandied about by researchers and scientists.

Or, if you prefer, you can state it as weak versus strong AI (it's okay to list them in either order). If you've been reading about AI in the popular press, the odds are that you've seen references to so-called strong AI and so-called weak AI, and yet the odds further are that both of those phrases have been used wrongly and offer misleading and confounding impressions. Time to set the record straight. First, let's consider what is being incorrectly stated. Some speak of weak AI as though it is AI that is wimpy and not up to the same capabilities as strong AI, including that weak AI is decidedly slower, or much less optimized, or otherwise inevitably and unarguably feebler in its AI capacities.