BY NISHANT ARORA
The most buzzed-about disruptive technologies that are changing business landscapes today are Machine Learning (ML) and Artificial Intelligence (AI). Almost all of us have heard or read about them but do we actually know what the fuss is all about? The enterprises are trying to harness the explosion of digital data and computational power with advanced algorithms to enable collaborative and natural interactions between people and machines.
However, there’s still a lot of confusion within the public and the media regarding what is ML and AI. People prefer to write Artificial Intelligence and ML technologies — and not ML and AI — and the argument goes that the former syncs well with the human mind. Both the terms are often being used as synonyms and in some cases as discrete, parallel advancements.
In reality, ML is to AI what neurons are to human brain. Let us start with ML. According to Roberto Iriondo, Editor of Machine Learning Department at Carnegie Mellon University in Pennsylvania, ML is a branch of AI. As coined by computer scientist and machine learning pioneer Tom M. Mitchell, “ML is the study of computer algorithms that allow computer programmes to automatically improve through experience”.
For instance, if you provide an ML model with songs that you enjoy, along with audio statistics (dance-ability, instrumentality, tempo or genre), it will be able to automate and generate a system to suggest you music that you’ll enjoy in the future, similarly as to what Netflix, Spotify and other companies do.
The ML model will look at each one of the pictures in the data-set, and find common patterns in pictures that have been labelled with comparable indications. AI, on the other hand, is exceptionally wide in scope and is a system in itself and not just independent data models. In simpler terms, AI means creating computers that behave in the way humans do. What AI systems today are doing reflects an important characteristic of human beings which separates us from traditional computer systems - human beings are prediction machines. Many AI systems today, like human beings, are mostly sophisticated prediction machines. Most ML algorithms are trained on static data sets to produce predictive models, so ML algorithms only facilitate part of the dynamic in the definition of AI. (IANS)
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