Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

Full Bio 
Follow on

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

3 Best Programming Languages For Internet of Things Development In 2018
248 days ago

Data science is the big draw in business schools
421 days ago

7 Effective Methods for Fitting a Liner
431 days ago

3 Thoughts on Why Deep Learning Works So Well
431 days ago

3 million at risk from the rise of robots
431 days ago

Top 10 Hot Artificial Intelligence (AI) Technologies
301758 views

Here's why so many data scientists are leaving their jobs
79407 views

Want to be a millionaire before you turn 25? Study artificial intelligence or machine learning
73437 views

2018 Data Science Interview Questions for Top Tech Companies
72513 views

Google announces scholarship program to train 1.3 lakh Indian developers in emerging technologies
60195 views

A Short History of Machine Learning

By Nand Kishor |Email | Apr 9, 2018 | 9321 Views

It's all well and good to ask if androids dream of electric sheep, but science fact has evolved to a point where it's beginning to coincide with science fiction.  No, we don't have autonomous androids struggling with existential crises - yet - but we are getting ever closer to what people tend to call "artificial intelligence."

Machine Learning is a sub-set of artificial intelligence where computer algorithms are used to autonomously learn from data and information. In machine learning computers don't have to be explicitly programmed but can change and improve their algorithms by themselves.

Today, machine learning algorithms enable computers to communicate with humans, autonomously drive cars, write and publish sport match reports, and find terrorist suspects. I firmly believe machine learning will severely impact most industries and the jobs within them, which is why every manager should have at least some grasp of what machine learning is and how it is evolving.

In this post I offer a quick trip through time to examine the origins of machine learning as well as the most recent milestones.

1950 - Alan Turing creates the "Turing Test" to determine if a computer has real intelligence. To pass the test, a computer must be able to fool a human into believing it is also human.

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.

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.

Source: DSC