Evolution of Machine Learning

“Hey Siri, wake me up at 6 AM,” – did you ever give such instructions to your iPhone? Do you know how Google maps are helping you in the navigation? All the commands that we today provide to our IoT work on the Machine Learning and Artificial Intelligence.

ML, as it’s widely referred to as, Machine Learning is enabling the machines to think and act on its own although they may not have cognitive abilities like humans but can mimic a human brain.

The gross idea of Arthur Samuel from IBM to make a machine intelligent turned fruitful when he rolled out the first ever program—Checkers in 1952.  Based on Minimax Strategy, the algorithm he designed enabled the computer to easily assess which team would win. That’s how the initial curtains were pulled on something innovative. However, Machine Learning, although visible, was still veiled until mid-2000s with the emergence of iOS and Android.

Post the development of Checkers in 1952, Machine Learning was retouched by Arthur Samuel in 1957 when he developed The Perceptron –the first neural network. This had the potential to simulate the human brain. Arthur’s innovation was widely hailed in the local dailies then.

The first ever path-breaking navigation technology shot into the limelight after a decade, in 1967.  Designed for salesmen, it would help him reach the destination city through visiting nearby places initially and the same would be marked as V until he arrives at his destination.

In 1979, Stanford University students created waves with the introduction of a unique software that would detect the obstacles in the room without manual intervention. A couple of years later, Terry introduced NetTalk, the first-ever software that would talk like a baby.

The Computer History Museum today has rich tales to narrate about IBM’s dominance in the Machine Learning. In 1997, IBM developed a machine chess named Deep Blue that even defeated Grand Masters of the chess and created a sensation.

The year 2010 witnessed Microsoft’s entry into ML with Kinect, an application that would track the human features in real-time. The following year, IBM hit the headlines yet another time by Watson, question & answer computer system that has the potential to handle heaps of inquiries. In 2014, Facebook rolled out a DeepFace algorithm that has the memory for faces just like human and would provide suggestions to tag them.

Conclusion: Machine Learning continues to make lives easy with the introduction of self-driving cars, which are yet to hit some countries. Nonetheless, almost all enterprises are making the best use of these technologies to stay connected with their workforce and even provide automated customer support. If you would like to leverage machine learning technologies such as ML-based chatbot etc., please feel free to get in touch with info@techwave.net.