Evolution of Machine Learning

Machine Learning is one of the widely talked topic of present time. It has gain popularity because of its wide scope and applications. Almost every field whether it is education, health, research or business there is huge application of machine Learning.

Machine Learning is a sub-set of artificial intelligence that uses different algorithms to learn from data and information autonomously. Computers (or any machines) need not be explicitly programmed in machine learning, but can alter and enhance their algorithms on their own. Nowadays, machine learning algorithms allow machines to interact with people, navigate vehicles autonomously, compose and post news, predict stocks, monitor and punish against traffic rule breaking, fraud identification, defense and many more. I strongly think that machine learning will have a major effect on most sectors and their employment, which is why everyone should at least have some understanding of what machine learning is and how it evolves. Below are some of the major events that took place in past helping the evolution of Machine learning.

Date Event
1943 A paper on neurons and neural network was written by neurophysiologist Warren McCulloch and mathematician Walter Pitts. They decided to mimic the model using an electric circuit and thus neural network was born.
1950 Alan Turing created the world famous Turing test, which was fairly simple. It has only to convince a human that it is a human but not a computer.
1952 Arthur Samuel, person who coined the term Machine Learning, wrote the first machine learning program. It was the game of checker that can improve on playing more.
1957 Creation of perceptron. Frank Rosenblatt- at Cornell Aeronautical Laboratory designed the first artificial neural network. It was designed for pattern and image recognition for the IBM 704.
1959 At Stanford University, Bernard Widrow and Marcian Hoff created two neural network models. The first one was called ADELINE and the second one is its next generation called MADELINE.
1967 The ‘nearest neighbor algorithm’ was developed by Marcello Pelillo. The algorithm was the starting of basic pattern recognition. The algorithm was used to map routes.
1969 A book titled ‘Perceptron’ was published by Marvin Minsky and Seymour Papert that describes the limitations of Neural networks and perceptrons.
1970 A paper on Automatic Differentiation which corresponds to modern Backpropagation algorithm was published by Seppo Linnainmaa.
1979 Student at Stanford University developed a cart that can navigate and avoid obstacle in a room.
1981 Gerald Dejong presents Explanation-based learning in which a computer algorithm analyzes information and generates a particular law that can be followed and discards unimportant information
1982 Hopfield network, a sort of recurrent neural network that can function as content-addressable memory structures was developed by John Hopfield.
1985 Invention of NetTalk by Terry Sejnowski. It learned to pronounce words the same way as baby does.
1989 Q-learning was developed by Christopher Watkins, which greatly improves the practicality and feasibility of reinforcement learning.
1995 A paper describing Random Forest was published by Tin Kam Ho.
1995 Paper on Support Vector Machine by Corinna Cortes and Vladimir Vapnik
1997 IBM’s Deep Blue beats the world champion at chess.
1997 Invention of LSTM, a form of Recurrent neural network by Sepp Hochreiter and Jurgen Schmidhuber.
2006 The term ‘Deep Learning’ was coined by Geoffrey Hinton.
2009 ImageNet is created
2010 Kaggle, an online platform for learning machine learning and competitions is launched.
2011 IBM’s Watson beats its human competitors at Jeopardy. It used Knowledge of Machine Learning, NLP and information retrieval technique.
2011 Google Brain is developed by the team led by Andrew Ng and Jeff Dean. It is a deep neural network that can learn to discover and classify objects much the way 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 researchers developed Deepface, a deep neural network algorithm that is able to identify faces with 97.35% accuracy.
2015 Amazon Launched its own machine Learning Platform as a part of Amazon Web Service.
2015 ResNet, was developed.
2015 Microsoft created the Distributed Machine Learning Toolkit, which enables the efficient distribution of machine learning problems across multiple computers.
2016 Google’s AI algorithm beats a professional Go (a Chinese board game) player.
2017 Google released Sonnet, an open source Deep Learning framework. A team of AI researchers published a pivotal  paper on Wasserstein GAN, a material improvement on traditional GAN. Paper on PatternNet and PatternAttribution: Learning how to explain neural network was published.
2018 Advancements in NLP and Computer vision. Popularity of Applied Machine learning has increased
2019 Researches are going on in advancements of Machine learning. Every day hundreds of papers are being published on Machine Learning and AI. Use of ML in Business and commercial field is gaining popularity.

So these are some of the major achievements in Machine learning till date. Thousands of researchers are working day and night and billions of dollars are being spent in research in Machine learning. People are focusing on applied machine learning and AI. Computers’ capabilities to see, comprehend and communicate with the world around them are increasing at a notable pace. And as the amount of data we generate continues to grow exponentially, so will the capacity of our machines to handle, analyze and derive from that data grows and expand.

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