Ted Gross Original publication on , the World’s Top AI and Technology News and Media Company. We invite you to become an AI sponsor if you’re working on an AI product or service. helps technology and AI startups scale. We can help you bring your technology to mass markets. Artificial Intelligence: Hidden in Plain Sight behind Artificial Intelligence. Photo by Daniele Levis Pelusi, Unsplash Preface. “Chaos Complexity Emergence & Technological Singularity” was a modified version of a larger work that appeared in October (Applied Marketing Analytics Volume 7 #2, a Henry Stewart Publications journal) and presenting “Emanating confluence”, which is a theory about the evolution of Artificial Intelligence. This includes the transition from Chaos Theory through Complexity Theory, Complexity Theory, to Emergence, then to Complexity Theory, Complexity Theory, Complexity Theory, to Emergence, to the Technological Singularity Theory, the Technological Singularity. For a free PDF copy of “Emanating confluence”, you can message me on Medium or LinkedIn if interested. Image by Author (c) 2021 The Chaos Theory “Introduce some anarchy.” Chaos Theory ‘Introduce a little anarchy. You are an agent for chaos. You know what chaos is? You know the thing about chaos? It was this mistake that gave rise to the “chaos theory”3 and the “butterfly effects4”. Prosaically, the butterfly effect is described as an idea where a butterfly flying in Brazil could cause a tornado to form in Texas. Lorenz said that the butterfly effect could be described as a single flap by a seagull’s wings causing a change in the weather’s course. Although the controversy is still ongoing, the latest evidence appears to favor the seagulls. 6 Chaos theory’s core proposition states that even small events, such as those of a meteorologist, can have significant effects on large systems, leading to important consequences. This statement, known as the butterfly effect or chaos, refers to the sensitivity to initial conditions. Surprisingly, chaos can be expressed mathematically. In mathematical terms, randomness can suddenly become an ordered disorder. This means that in both existential and real-world situations there is an order to chaos. The 1960s slowly realized that simple mathematical equations could be used to model chaotic systems as violent and unpredictable as waterfalls. Small differences in input can quickly turn into huge differences in output, a phenomenon known as “sensitive dependence upon initial conditions”.7 This is why chaos theory advocates often use the phrase, “For lack of a nail” to illustrate the butterfly effect. A single nail could cause the destruction of a kingdom. How can we even predict how systems will behave when so many small events could have such huge consequences? For centuries, this nagging question was ignored. The supreme being called karma or luck was thought to be responsible for chaos. It was impossible to predict or control chaos despite its widespread presence. Random events are unpredictable, so it is impossible to predict what will happen. Or so everyone believed. The brain searches for ‘patterns,’ almost as a kind of genetic imperative. The terms “chaos” and “patterns” seem like polar opposites. How can chaos be distinguished from a pattern? If chaos reigns, how is there a pattern to be discerned? 2005, Lorenz simplified chaos theory to the following: “Chaos is when the past determines the future but the approximate future does not determine the future.8 The night sky was chaotic with hundreds of million stars, and ancient man searched for patterns within it. He found them in the constellations. The modern man is interested in underlying patterns of activity and behavior. This is evident in the recent COVID -19 epidemic. At the moment, the focus is on the cause of the nail. However, intense attention is paid to patterns such as how it spreads and what prophylactic measures were effective. This is in the hopes that pattern recognition can be applied to combat the spread of coronavirus. This helps in fighting the virus as it allows us to predict future outcomes by identifying such patterns. (It’ll be fascinating to see Amy Webb’s analysis of this issue in her forthcoming book, “The Genesis Machine”.10) Chaos appears in weather patterns, in airplane flight behaviour, in cars that cluster on expressways, and in oil that flows underground. The same laws apply regardless of the medium. This realization has had a profound impact on the decisions of business leaders about insurance and the views taken by astronomers about the solar system. It also introduced ‘linear,’ and nonlinear progressions to help them understand the behavior. These systems are able to be predicted by using linear progressions that go from step A through step B. They are able to see their patterns even before they start. Because they are clear about their beginnings and have specific steps, chaos is not an issue. Our brains are wired to handle linear data. This is because most people were taught this way from birth. Linear relationships are easily captured by drawing a straight line on graphs. It is easy to see linear relationships: The more you have, the better. They are easy to solve, making them ideal for textbooks. The modularity of linear systems is important because you can disassemble them and then put them back together. They add up. It is difficult to solve nonlinear systems and they cannot be combined. Nonlinear systems in fluid and mechanical systems are the ones that people tend to want to ignore when trying to understand them. Nonlinearity is the key feature. Linear systems are those that you understand individually, then put them together. Nonlinear systems have the opposite.
The author(s) of Chaos, Complexity and Emergence & Technological Singletonity
