Understanding AI vs Machine Learning vs Deep Learning

Artificial Intelligence (AI) is working its way into almost every industry you can think of – including video games, healthcare, autonomous vehicles, cybersecurity, retail, and banking. With the growth of AI came the introduction of other terms such as “Machine Learning” and “Deep Learning”. You may have heard them before but knowing how they relate to AI and how they are different can be confusing.

Thanks to astonishing advancements in artificial intelligence (AI) and its sub-segments machine learning and deep learning, companies are achieving new levels of efficiency in data analysis that impact their entire business.

The term, “artificial intelligence” was first created in 1956, but has become more popular today. In general, AI is a computer system able to perform tasks that ordinarily require human intelligence. Most of us think of human-like robots when we think about artificial intelligence because of how it is portrayed in many movies and science fiction novels. However, AI is used for more specific benefits in varying applications – two good examples are self-driving cars or chess-playing computers.

Artificial intelligence is organized under two broad categories, narrow and general.

Narrow AI is often focused on performing a single task extremely well. These machines seem extremely intelligent, but they actually have many constraints and limitations. A few examples of Narrow AI include Siri and Alexa, image recognition software, and Google searches.

Artificial General Intelligence (AGI) exhibits characteristics of intelligence similar to a human being and can use that intelligence to solve problems. AGI is the kind of artificial intelligence you see in movies such as the robots from Westworld or The Terminator. AGI isn’t new but developing it continues to be a difficult task.

A recent survey of retailers worldwide identified cost savings, enhanced decision-making, and process automation as some of the main areas that AI has the potential to impact meaningfully.

Simply put, machine learning (ML) is a branch of AI that uses data to learn, make decisions, and improve without explicitly being programmed to.

This leads to minimal human interaction and eliminates the need for thousands of lines of written code. Machine learning began to pick up traction in the 80’s, so ML algorithms have been around for quite some time. However, the capability to automatically take advanced mathematical calculations and apply them to big data repeatedly and more rapidly is a recent development.

There are many examples of machine learning in use today that you may be familiar with. One is speech recognition software which recognizes the words spoken in an audio clip and translates them into text. You may already be using this when you use the voice to text feature in messages on your smartphone.

Another widely publicized application of machine learning is the online recommendations on retail websites such as Amazon. These sites use ML to gain data and analyze it to give the user a personalized shopping experience.

American Express processes $1 trillion in transaction and has 110 million AmEx cards in operation. They rely heavily on data analytics and machine learning algorithms to help detect fraud in near real time, therefore saving millions in losses. Additionally, AmEx is leveraging its data flows to develop apps that can connect a cardholder with products or services and special offers. They are also giving merchants online business trend analysis and industry peer benchmarking.

Netflix has had a really important impact on the development of Machine Learning technology. The story begins back in 2006, with the announcement of The Netflix Prize:

“In 2006 we announced the Netflix Prize, a machine learning and data mining competition for movie rating prediction.