The difference between Artificial Intelligence and Machine Learning

The difference between Artificial Intelligence and Machine Learning.

Artificial Intelligence (AI) and Machine Learning (ML) are two hot buzzwords right now and often seem to be used interchangeably. These two are not quite the same thing, but the perception that they are can sometimes lead to some confusion, reported Forbes

Both terms crop up frequently when the topic is big data, analytics, and the broader waves of technological change sweeping through our world.

In short, artificial intelligence is the broader concept of machines being able to carry out tasks in a way that we consider “smart.” Machine learning is a current application of AI based on the idea that we should be able to give machines access to data and let them learn for themselves.

Artificial Intelligence – devices designed to act intelligently – are often classified into one of two fundamental groups – applied or general. Applied AI is far more common – systems designed to trade stocks and shares or maneuver an autonomous vehicle intelligently would fall into this category.

Generalized AIs – systems or devices which can, in theory, handle any task – are less common, but this is where some of the most exciting advancements are happening today. It is also the area that has led to the development of Machine Learning. Often referred to as a subset of AI, it’s more accurate to consider it the current state-of-the-art.

Machine Learning applications can read the text and determine whether the person who wrote it is making a complaint or offering congratulations. They can also listen to a piece of music, decide whether it will likely make someone happy or sad, and find other music details to match the mood. In some cases, they can even compose their music expressing the same themes, or which they know is likely to be appreciated by the admirers of the original piece.

Two significant breakthroughs led to the emergence of Machine Learning as the vehicle driving AI development forward at the current speed.

One of these was the realization that rather than teaching computers everything they need to know about the world and how to carry out tasks, it might be possible to teach them to learn for themselves.

The second, more recently, was the emergence of the internet, and the massive increase in the amount of digital information being generated, stored and made available for analysis.

Once these innovations were in place, engineers realized that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like human beings and then plug them into the internet to give them access to all of the information in the world.

Neural Networks

A Neural Network is a computer system designed to work by classifying information in the same way a human brain does. It can be taught to recognize, for example, images and rank them according to the elements they contain.

Essentially it works on a system of probability – based on data fed to it; it can make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables “learning” – by sensing or being told whether its decisions are right or wrong, it modifies the approach it takes in the future.

These are all possibilities offered by systems based around ML and neural networks. Thanks in no small part to science fiction, the idea has also emerged that we should be able to communicate and interact with electronic devices and digital information as naturally as we would with another human being. To this end, another field of AI, Natural Language Processing (NLP), has become a source of hugely exciting innovation in recent years and is heavily reliant on ML.

NLP applications attempt to understand natural human communication, either written or spoken and communicate with us using similar, natural language. ML is used here to help machines understand the vast nuances in human language and to learn to respond in a way that a particular audience is likely to comprehend. 

Artificial Intelligence has a lot to offer. With its promise of automating mundane tasks and offering creative insight, industries in every sector, from banking to healthcare and manufacturing, are reaping the benefits.

Machine Learning has certainly been seized as an opportunity by marketers. After AI has been around for so long, it’s possible that it started to be seen as something that’s in some way “old hat” even before its potential has ever been achieved. There have been a few false starts along the road to the “AI revolution,” and the term Machine Learning certainly gives marketers something new, shiny, and, importantly, firmly grounded in the here-and-now.

The fact that we will eventually develop human-like AI has often been treated as an inevitability by technologists. Indeed, today we are closer than ever and are moving towards that goal with increasing speed. Much of the exciting progress we have seen in recent years is thanks to the fundamental changes in how we envisage AI working, which ML has brought about. 

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