What is Artificial intelligence?and how it's work!

Artificial Intelligence (AI) is a commonly employed appellation to refer to the field of science aimed at providing machines with the capacity of performing functions such as logic,reasoning, planning, learning, and perception. Despite the reference to “machines” in this definition, the latter could be applied to “any type of living intelligence”. Likewise, the meaning of intelligence, as it is found in primates and other exceptional animals for example, it can be extended to include an interleaved set of capacities, including creativity,emotional knowledge, and self-awareness.The birth of the computer took place when the first calculator machines were developed, from the mechanical calculator of Babbage, to the electromechanical calculator of Torres-Quevedo. The dawn of automata theory can be traced back to World War II with what was known as the “codebreakers”. The amount of operations required to decode the German trigrams of the Enigma machine,without knowing the rotor’s position, proved to be too challenging to be solved manually. The inclusion of automata theory in computing conceived the first logical machines to account for operations such as generating, codifying, storing and using information. Indeed, these four tasks are the basic operations of information processing performed by humans.The pioneering work by Ramón y Cajal marked the birth of neuroscience, although many neurological structures and stimulus responses were already known and studied before him. For the first time in history the concept of “neuron”was proposed. McClulloch and Pitts further developed a connection between automata theory and neuroscience,proposing the first artificial neuron which, years later, gave rise to the first computational intelligence algorithm, namely“the perceptron”. This idea generated great interest among prominent scientists of the time, such as Von Neumann,who was the pioneer of modern computers and set the  foundation for the connectionism movement.It has been well recognised that AI amplifies human potential as well as productivity and this is reflected in the rapid increase of investment across many companies and organisations. These include sectors in healthcare,manufacturing, transport, energy, banking, financial services,management consulting, government administration and marketing/advertising. The revenues of the AI market worldwide, were around 260 billion US dollars in 2016 and this is estimated to exceed $3,060 billion by 2024 [23].This has had a direct effect on robotic applications, including exoskeletons, rehabilitation, surgical robots and personal care-bots. The economic impact of the next 10 years is estimated to be between $1.49 and $2.95 trillion. These estimates are based on benchmarks that take into account similar technological achievements such as broadband,mobile phones and industrial robots [28]. The investment from the private sector and venture capital is a measure of the market potential of the underlying technology. In 2016, a third of the shares from software and information technology have been invested in AI, whereas in 2015, 1.16 billion US dollars were invested in start-up companies worldwide, a 10-fold increase since 2009.The idea of creating an artificial machine is as old as the invention of the computer. Alan Turing in the early 1950s proposed the Turing test, designed to assess whether a machine could be defined as intelligent. Two of the main pioneers in this field are Pitts and McCulloch [38] who, in 1943, developed a technique designed to mimic the way a neuron works. Inspired by this work, a few years later,Frank Rosenblatt [39] developed the first real precursor of the modern neural network, called Perceptron. This algorithm describes an automatic learning procedure that can discriminate linearly separable data. Rosenblatt was confident that the perceptron would lead to an AI system in the future. The introduction of perceptron, in 1958, signalled the beginning of the AI evolution. For almost 10 years afterwards, researchers used this approach to automatically learn how to discriminate data in many applications, until Papert and Minsky [3], demonstrated a few important limitations of Perceptron. This slowed down the fervour of AI progress and more specifically, they proved that the perceptron was not capable of learning simple functions,such as the exclusive-or XOR, no matter how long the network was trained.