MACHINE LEARNING — Part 1:
Machine learning is an idea or system created on a basis similar to a human being in the way he perceives or learns things. It is based on what is called artificial intelligence, which is a set of algorithms within it to create algorithms from others in an automatic way according to analyzes, comparison, statistics, probability and conclusion They are somewhat close to the way biological intelligence works so that artificial intelligence is characterized by speed, accuracy, and analysis of the vast amount of information simultaneously, and in some cases, it may surpass human intelligence in giving more accurate results, especially in statistics and probabilities.
Since the advent of the machine and technology, the idea of artificial intelligence has occupied many researchers and minds, among them the British mathematician “Alan Turing”, and his concept of the universal computer” in 1936, which is called the computer of today. “Alan Turing” was the first to present the rules of machine learning through his article “Computer and Intelligence”, which was published in the year 1950 in which he explained the “Turing test” to discover whether computers have the intelligence or not. In 1952 Arthur Samuel wrote the first computer learning program. The program was the game of checkers, and the IBM computer improved at the game the more it played, studying which moves made up winning strategies and incorporating those moves into its program. 1957 Frank Rosenblatt designed the first neural network for computers (the perceptron), which simulate the thought processes of the human brain. In 1967 The “nearest neighbor” algorithm was written, allowing computers to begin using very basic pattern recognition. This could be used to map a route for traveling salesmen, starting at a random city but ensuring they visit all cities during a short tour.1979 Students at Stanford University invent the “Stanford Cart” which can navigate obstacles in a room on its own.
1981 Gerald Dejong introduces the concept of Explanation Based Learning (EBL), in which a computer analyses training data and creates a general rule it can follow by discarding unimportant data. 1985 Terry Sejnowski invents NetTalk, which learns to pronounce words the same way a baby does. 1990s Work on machine learning shifts from a knowledge driven approach to a data-driven approach. Scientists begin creating programs for computers to analyze large amounts of data and draw conclusions or “learn” from the results.
1997 IBM’s Deep Blue beats the world champion at chess.
2006 Geoffrey Hinton coins the term “deep learning” to explain new algorithms that let computers “see” and distinguish objects and text in images and videos.
2010 The Microsoft Kinect can track 20 human features at a rate of 30 times per second, allowing people to interact with the computer via movements and gestures.
2011 IBM’s Watson beats its human competitors at Jeopardy.
2011 Google Brain is developed, and its deep neural network can learn to discover and categorize objects much the way a cat does.
2012 Google’s X Lab develops a machine learning algorithm that is able to autonomously browse YouTube videos to identify the videos that contain cats.
2014 Facebook develops DeepFace, a software algorithm that is able to recognize or verify individuals on photos to the same level as humans can.
2015 Amazon launches its own machine learning platform. 2015 Microsoft creates the Distributed Machine Learning Toolkit, which enables the efficient distribution of machine learning problems across multiple computers. 2015 Over 3,000 AI and Robotics researchers, endorsed by Stephen Hawking, Elon Musk and Steve Wozniak (among many others), sign an open letter warning of the danger of autonomous weapons which select and engage targets without human intervention. 2016 Google’s artificial intelligence algorithm beats a professional player at the Chinese board game Go, which is considered the world’s most complex board game and is many times harder than chess. The AlphaGo algorithm developed by Google DeepMind managed to win five games out of five in the Go competition. So are we drawing closer to artificial intelligence? Some scientists believe that’s actually the wrong question. They believe a computer will never “think” in the way that a human brain does, and that comparing the computational analysis and algorithms of a computer to the machinations of the human mind is like comparing apples and oranges. Regardless, computers’ abilities to see, understand, and interact with the world around them is growing at a remarkable rate. And as the quantities of data we produce continue to grow exponentially, so will our computers’ ability to process and analyze and learn from that data grow and expand.
A Short History of Machine Learning -- Every Manager Should Read
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