Real artificial Intelligence (AI) is all about reality and causality, and how it is reflected in digital mentality and cyberspace or virtuality.
There are two classes of Machine Intelligence, Non-Real AI and Real AI.
Non-Real AI refers to the simulation of human intelligence in machines that are programmed to think and act like humans.
The Real AI is all about reality, mentality and causality, and how it is reflected as digital mentality, or machine intelligence, in cyberspace or virtuality, or mixed reality.
Its key domains as interacting universes are:
Actuality (the Physical World, the Universe, the total environment; philosophy, ontology, science, mathematics and technology)
It covers Popper's three worlds splitting reality into three worlds, 1, 2, 3:
The Real AI is running causal algos or algorithms as sets of causal rules for solving any complex real world problems or accomplishing tasks.
As such, the Real AI emerges as a global AI platform embracing all sorts and descriptions of Non-Real AI:
Narrow and Weak AI, ML, DL (Deep Neural Networks). It all emulates, mimics, simulates, counterfeits, or fakes synapse-connected brain neurons, some cognitive functions/skills/capacities or some intelligent behavior as running on graphics processing units (GPUs) or processors specialized for AI functions.
Computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing structures/patterns/correlations in the data. And induatrial.AI technology can analyze real world [oilfield and mining data] data, making assumptions, and providing insight, predictions, controls or recommendations and optimization [of energy, waste, raw materials, chemicals, or manpower], but for very narrow specific tasks.
Most computers are exceeding humans in many special tasks, such as self-driving drones, strategic games, or mathematics in general. But they are as good as the training data fed to them, GIGO, garbage in, garbage out, bias in and bias out.
To date, all the capabilities attributed to machine learning and AI have been in the category of narrow AI. 1) an algorithm designed to do one thing (say, identify objects) cannot be used for anything else (play a video game, for example), and 2) anything one algorithm “learns” cannot be effectively transferred to another algorithm designed to fulfill a different specific purpose. For example, AlphaGO, the algorithm that outperformed the human world champion at the game of Go, cannot play other games, despite those games being much simpler.
In image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human, such as digits or faces. Deep learning is currently the most sophisticated Narrow AI architecture in use today with its popular algorithms as convolutional, recurrent, long short-term memory, generative adversarial, or belief networks.
