We saw earlier that although machines are stone-hearted, they can learn! As a subset of artificial intelligence (AI), machine learning algorithms enable computers to learn from data, and even improve themselves, without being explicitly programmed. That’s how your Siri communicates with you, or how your super car parks itself, or, partially, how your bank approves your loan request! Machines are getting more and more intelligent and AI is expanding to more businesses and industries. But does your fancy smart phone dream of digital sheep!? No, not yet… In this post, we offer a very quick trip through time to review some pieces of the history of machine learning and its evolution.
It was in 1940s when the first manually operated computer system, ENIAC, was invented. At that time the word “computer” was being used as a name for a human with intensive numerical computation capabilities, so, ENIAC was called a numerical computing machine! Well, you may say it has nothing to do with learning?! WRONG, from the beginning the idea was to build a machine able to emulate human thinking and learning. In the 1950s, we see the first computer game program claiming to be able to beat the checkers world champion. This program helped checkers players a lot in improving their skills! Around the same time, Frank Rosenblatt invented the Perceptron which was a very, very simple classifier but when it was combined in large numbers, in a network, it became a powerful monster. Well, monster is relative to the time and in that time, it was a real breakthrough. Then we see several years of stagnation of the neural network field due to its difficulties in solving certain problems…
Thanks to statistics, machine learning became very famous in 1990s. The intersection of computer science and statistics gave birth to probabilistic approaches in AI. This shifted the field further toward data-driven approaches. Having large-scale data available, scientists started to build intelligent systems that were able to analyze and learn from large amounts of data. As a highlight, IBM’s Deep Blue system beat the world champion of chess, the grand master Garry Kasparov. Yeah, I know Kasparov accused IBM of cheating, but this is a piece of history now and Deep Blue is resting peacefully in a museum…
We can consider the 90s as one of the golden eras of machine learning. During the decade there were significant contributions to the field. Alright, but how about the computers? In addition to the developments being made on the algorithm side, the hardware and the technology were also improving dramatically! For years, you saw computers becoming not only more powerful but smaller in size! This huge advancement also contributed a lot to scientific progress in general, and to AI in particular.
The progress continued into the 21st century as we witnessed several beautiful scientific contributions to AI such as the concept of Deep Learning.
