What is the Importance of Artificial Intelligence in Everyday Life?

Artificial Intelligence, or AI, continues to surprise us with its rocket development, and we encounter increasingly smarter conversational Chabot’s, automatic machines, and artificial intelligence that mimic human strengths and add better analysis and factual capabilities. These machines make huge advances in technology, but they are also a future threat as they will lose up to 50% or more of all current jobs.
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"Artificial intelligence (Artificial Intelligence, AI) is a branch of science dealing with the creation of machines showing signs of intelligent behavior. The definition of "intelligent behavior" is still under discussion, most often human intelligence is used as the standard of intelligence. John McCarthy first came up with this concept in 1955.......

·        Automation of homogeneous repetitive work
·        Smart chat rooms
·        Flying drones for transportation
·        Automated production systems
·        Faster analytical tools
·        Prediction tools for event prediction
·        Robotic assistants
·        More accurate translations of languages
·        Autonomous cars and vehicles
·        Music composed with AI
·        Machine learning to understand meaning and context
·        Artificial intelligence and business!!
·        Composing music for advertising, movies, television
·        AI for administration
·        Voice assistants (Alexa, Siri, Cortana)
·        Smart Buildings and Internet of Things
·        Remote video communication
Artificial Intelligence (AI) is a machine that is capable of considering, learning and making decisions at the same level as a human being. Artificial intelligence is a direct translation of the English artificial intelligence, and often the English abbreviation "AI" is used. However, the dream of mechanizing human thinking has roots far back in history. The first attempt to find laws of thought is seen in the Greek philosopher Aristotle (384-322 BC) who founded the formal logic. In formal logic, one examines arguments by considering their logical form. One does not look at the meaning of the individual concepts, but only on the overall logical context that is formed by words such as "and", "or", "all" etc. Aristotle's logic was relatively limited, but in the 1800s and 1900s, the formal logic has been significantly developed and improved, including George Boole (1815-1864) developed the binary logic that forms the heart of computers.
A number of philosophers have expanded the idea behind the formal logic by imagining a particular "language of thought" in which all thoughts can be expressed as symbols, and thinking takes place as a pure mechanical procedure where the symbols are handled according to certain formal rules or "thought laws". The idea is seen in an early form by WG Leibniz (1646-1716), and is expressed in various forms in the following centuries. For the KI research, especially Jerry Fodor's The Language of Thought (1975) has had a great influence.
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In the years around 1900, the idea of ​​reducing thinking into a mechanical calculation was accomplished with some success in mathematics. Led by German mathematician David Hilbert, he managed to gather all mathematics into a single system. An essential part of human thinking, namely mathematics, could then be considered as mechanical handling with symbols according to formal rules. However, one has today become aware of a number of serious problems in this so-called formalistic concept of mathematics.
The first mechanical calculator that could handle all four calculations (plus, minus, times and divide) was the so-called pascalina, developed by Blasé Pascal in 1645. In the following centuries, various mechanical calculators developed dramatically. However, the use was limited because they were not programmable and could therefore only solve exactly the tasks they were created to solve. In 1837, however, Charles Babbage made the idea of ​​a programmable machine, i.e. a machine that with the same physical hardware could solve different tasks depending on which program it was equipped with. However, Babbage’s never managed to build its so-called Analytical Engine, partly because its design was very complicated. First with the advent of the electronic computer in early 1940’
With the combination of programmable computers and the notion that thinking is a mechanical calculation, the earth was ready for the first attempts to create thinking machines.

Attempts to create artificial intelligence can roughly be divided into three different research programs or paradigms, each of which has a perception of what thinking is and how intelligence can be created artificially: 1) the classic symbol paradigm, 2) connectivity’s and 3) embodies KI. Of these three, the classic symbol paradigm was dominant from the 1950s to the mid-80s, after which the other two paradigms were influenced. Today, a fourth paradigm that focuses on self-organization is slowly beginning to emerge...
According to the embodiment philosophy, human intelligence is closely linked to our physical existence. Therefore, one cannot understand (or copy) our ability to abstract thinking unless we first understand our fundamental ability to interact with the surrounding environment.
The hypothesis is largely developed within the so-called cognitive semantics, not least by the psychologist George Layoff and the philosopher Mark Johnson, who with a number of concrete studies seek to show that a large part of the human language is based on metaphors derived from basic experiences. For example, common terms such as "time runs", "prices rise" and "he seems unbalanced" are all metaphorical if you look. This shows, according to Layoff and Johnson et al, that very basic experiences create order for our world view and give us the ability to abstract thinking.
However, the Embodiment philosophy is also supported by other studies of how people in practice think. In a 1994 article humans do not always solve computational tasks through abstract thinking, but in certain situations choose to perform certain actions instead. Kirsch and Maglio examined users of the computer game Tetris, but you can, for example. Imagine how to turn a puzzle piece to see if it fits, rather than closing your eyes and calculating. In other words, one solves the calculation task by acting and sensing.
A system can be called self-structuring if, by itself, an overall order or structure arises in that the individual parts of the system follow certain rules. It is often seen in the biological world, for example. In bog theaters, whose structure does not arise through central control and planning, but by the fact that the individual ants follow some specific simple rules, that’s it!
Self-structuring has long been a theme in research into artificial intelligence. In the KI research, artificial neural networks can be seen as an early example of the use of self-structuring, but it is especially in connection with the increased focus on biologically realistic systems in the new KI that one has more purposefully started to use self-structuring. The principle is has been used by the Belgian Luc Steels, who in the 90s got autonomous agents to develop a common language using simple self-structuring mechanisms. The principle is also used by the Adaptronics Group. Develops robots composed of smaller units. The robots 'overall behavior and form arise as a result of the units' cooperation according to simple rules.
One cannot yet speak of a comprehensive paradigm or research program on self-structuring, but the onset of use testifies to a fundamental shift in the perception of how artificial intelligence is created. The researchers no longer see themselves as watchmakers who have to design and control the systems in the smallest detail. Instead, it is about creating the right conditions so that the overall complexity can grow by itself.