Machine Learning and AI: The Mystery Isn’t solved yet

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There is still a great deal of confusion among the general public and the press regarding the differences between machine learning (ML) and artificial intelligence (AI).

The Buzz Around ML and AI

The many buzzed-about disruptive technologies that are changing business landscapes today are Machine Learning (ML) and Artificial Intelligence (AI). Virtually everyone has read or heard about them, but do we really understand exactly what the fuss is about?

Businesses want to exploit the explosion of electronic information and computational capability with innovative algorithms to enable natural and collaborative connections between machines and people.

Yet there is still a great deal of confusion among the general public and the press regarding what ML and AI actually are.
People often write AL and ML technology — rather than ML and AI — and the debate goes that the former syncs nicely with the human mind.

ML Versus AI: Clearing Up the Terminology

Both phrases are frequently used as synonyms and in some cases as distinct, parallel developments.
In fact, ML is to AI what neurons are to the human mind. Let’s begin with ML.

According to computer scientist and machine learning leader Tom M. Mitchell, “ML is the study of computer algorithms that allow computer programs to automatically improve through experience”.

As an example, if you feed an ML model with songs you like, along with audio data (danceability, instrumentality, genre or tempo), it will be able to automate and create a method to suggest music you’ll enjoy in the future — much like what Netflix, Spotify and other companies do.

“In a simple example, if you load a Machine Learning program with a substantial data-set of X-ray images along with their descriptions (symptoms), it will be able to help (or perhaps automate) the analysis of X-ray images in the future,” explained Iriondo.

The ML model examines each image in the data-set and identifies common patterns in images that were labelled with similar indications.

Defining Artificial Intelligence Today

AI, on the other hand, is extremely broad in scope and is a system in itself rather than merely independent data units.
In simpler terms, Artificial Intelligence means creating computers that behave in ways humans do.

“Primarily, it is interesting and significant to note that the technical gap between what was known as AI more than 20 years ago and classic computer programs is near zero,” says van Kraay.

What AI systems do today reflects a key characteristic of human beings that separates us from traditional computer technologies — humans are prediction machines.

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Many AI systems today, like human beings, are essentially sophisticated prediction machines.

“The more complex the system, the more it can generate precise predictions based on an intricate collection of data used to train different (ML) models, and the most advanced AI systems are able to continually learn from faulty assertions in order to improve the accuracy of their forecasts, thereby displaying something approximating human intelligence,” van Kraay explained.

Many Machine Learning algorithms are trained on static data-sets to create predictive models, so Machine Learning development only facilitates a portion of the dynamic element in the definition of AI.

Fifty years ago, a chess-playing program was considered a form of AI.
Today, such a program would be viewed as ordinary and outdated, since it can be found on virtually every computer.

“AI today is symbolised by human-AI interaction devices such as Google Home, Apple Siri and Amazon Alexa or ML-powered video recommendation systems that power Netflix, Amazon and YouTube,” states Iriondo.

“Perhaps, in just a few years, today’s advanced Artificial Intelligence developments will be regarded as ordinary as flip-phones are to us now,” quips Iriondo.

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