It began with the "heartless" Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason in order to solve problems and make decisions, so why can't machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence.
In the previous article, we studied Tensorflow, its functions, and its python implementations. In this article, we will be studying Artificial Intelligence and more popularly knows as AI. One thing that I believe is that if we are able to correlate anything with us or our life, there are greater chances of understanding the concept. So I will try to explain everything by relating it to humans.
Since the dawn of machinery and the first flickerings of computer technology, humanity has been obsessed with the idea of artificial intelligence - the concept that machines could one day interact, respond and think for themselves as if they were truly alive. Every year, the possibility of an "intelligent technology" future becomes more and more of a reality - as algorithms and machine learning improve at a lightning-fast rate. According to experts across the globe, machines will soon be capable of replacing a variety of jobs - from writing bestsellers, to composing Top 40 pop songs and even performing your open-heart surgery! However, the biggest questions remain: how long until that point, and how did we get to here? When attempting to chart the future, it's always essential to know the past.
In an expanded edition published in 1988, they responded to claims that their 1969 conclusions significantly reduced funding for neural network research: "Our version is that progress had already come to a virtual halt because of the lack of adequate basic theories… by the mid-1960s there had been a great many experiments with perceptrons, but no one had been able to explain why they were able to recognize certain kinds of patterns and not others."