Original article was published on Artificial Intelligence on Medium
Instead of focusing much on the implementation side of things, this article will be much of a meaningful explanation of what AI is and what it isn’t, how we should perceive AI, the myths related to it and how many people are misinformed about the meaning of Artificial Intelligence. And lastly how AI is much more than just an ordinary science topic. So without further delays, lets get straight to the point.
Understanding What It Actually Is
AI isn’t one such thing that have emerged in the past or have evolved into existence in last decade or so. Researchers and mathematicians and philosophers have been developing upon the idea of what they call borrowing the human mind since centuries. And to actually understand the idea and need behind Artificial Intelligence, you need to take a look at various historical developments and inventions regarding AI.
1308 Catalan poet and theologian Ramon Llull publishes Ars generalis ultima (The Ultimate General Art), further perfecting his method of using paper-based mechanical means to create new knowledge from combinations of concepts.
1666 Mathematician and philosopher Gottfried Leibniz publishes Dissertatio de arte combinatoria (On the Combinatorial Art), following Ramon Llull in proposing an alphabet of human thought and arguing that all ideas are nothing but combinations of a relatively small number of simple concepts
1763 Thomas Bayes develops a framework for reasoning about the probability of events. Bayesian inference will become a leading approach in machine learning.
1854 George Boole argues that logical reasoning could be performed systematically in the same manner as solving a system of equations.
1898 At an electrical exhibition in the recently completed Madison Square Garden, Nikola Tesla makes a demonstration of the world’s first radio-controlled vessel. The boat was equipped with, as Tesla described, “a borrowed mind.”
1950 Alan Turing publishes “Computing Machinery and Intelligence” in which he proposes “the imitation game” which later became the “Turing Test.”
1959 Arthur Samuel coins the term “machine learning,” reporting on programming a computer “so that it will learn to play a better game of checkers than can be played by the person who wrote the program.”
1965 Hubert Dreyfus publishes “Alchemy and AI,” arguing that the mind is not like a computer and that there were limits beyond which AI would not progress.
1976 Computer scientist Raj Reddy publishes “Speech Recognition by Machine: A Review” in the Proceedings of the IEEE, summarizing the early work on Natural Language Processing (NLP).