The difference between artificial intelligence, machine learning, and deep learning

The tech world today is talking about three important terminologies: Artificial Intelligence, Machine Learning  and Deep Learning. These names often create confusions. Many think the three terms are one and the same when there are significant differences between them. They are often used interchangeably but that isn’t the case.

So, what exactly is the distinction between the three – Artificial Intelligence, Machine Learning and Deep Learning? To visualize the difference between them first try to picture the relationship between the three terms.

Visualize them as 3 concentric circles where Deep Learning is a sub set of Machine Learning which in turn is a subset of Artificial Intelligence. Artificial Intelligence as the ‘idea’ popped up first, then comes Machine Learning that flourished later and finally Deep Learning- that came with extra spaces and as a breakthrough that can drive the AI boom.

Let’s dive in:

Artificial Intelligence (AI): AI is “Machine exhibiting Human Intelligence.” Artificial Intelligence or AI is the broad and advanced term for computer intelligence. The Merriam-Webster dictionary defines it as “a branch of computer science dealing with the simulation of intelligent behavior in computers “or “the capability of a machine to imitate intelligent human behavior

Artificial intelligence can be referred to anything pertaining to a computer program. For example- a computer program playing rummy or a game of chess, or Facebook recognizing picture of a friend before you manually tag them or voice recognition inventions like Google Home or Amazon Echo –  powerful speakers and home assistants which answer to human questions or commands.

If you go deeper, AI can be categorized into 3 broader terms- Narrow AI, Artificial General Intelligence (AGI) and Superintelligence AI. The Narrow AI is the technology that performs a task better than that of the humans themselves can. Image classification on Pinterest is one of the examples of Narrow AI technologies in practice. Don’t you think that these technologies, interestingly, exhibit some dimensions of human intelligence? If yes, then how?

The ‘how’ part takes us to the next concentric circle and the space of ‘Machine Learning’.

Machine Learning (ML): ML is “The construction of Algorithm that helps achieve Artificial Intelligence.”  Machine Learning is a subset of Artificial Intelligence. It is one of the most promising AI techniques that takes all the data, learns (makes algorithm) and predicts results. The whole premise of ML is that the system simply gets trained by itself using algorithms with large amount of data to perform tasks.

What is Not a Machine Learning? – A hand-coded software that works with specific instructions to perform a specific task.

A large set of data helps ML to outclass AI technologies of facial, object, image and speech recognition, etc… A Machine Learning system works or makes predictions based on patterns. Computer vision is till date, one of Machine Learning’s finest application areas. However, it requires hand-coded classifiers like edge detection to get the task done. It produces results which are good but not something that could beat human intelligence.

Deep Learning: DL is “A subset of Machine Learning”. Deep Learning is the technique for implementing Machine Learning. It works with new and next level of accuracy for many important issues like recommender systems, sound recognitions, etc… It uses a set of algorithms inspired by the structure and function of the brain called “neural networks”.

Some Machine Learning techniques that Deep Learning uses combining it with neural networks help in influencing human decisions. It requires a huge set of data and number of parameters which make it expensive.

A deep learning algorithm could practice learning how a crocodile looks like. It may use a huge number of resources (datasets) of crocodile images to understand how it differs from an alligator.

A device with Deep Learning capabilities can scan humongous amounts of data (a fruit’s shape, its color, size, season, origin, etc.) to define the difference between an Orange and an Apple.

Two major differences between ML and DL:

  • Deep learning automatically finds out the features which are important for classification, where in Machine Learning, these features should be given manually;
  • As against Machine Learning, Deep Learning requires significantly large volume of data to work well and thereby require heavy high-end machines.

Of course, the differences between Artificial Intelligence, Machine Learning and Deep Learning are subtle and not as obvious as that of determining a difference between two fruits! This is because Deep Learning is the next evolution of Machine Learning!  And Machine Learning is one of the ways to achieve artificial intelligence!

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