The tech world has seen unprecedented change in recent years. And with that change comes a host of new acronyms. AI, ML, deep learning, and more – it’s a lot to keep track of! Today, many people use these terms interchangeably, without understanding how different they really are.

In this post, we’re going to discuss the difference between artificial intelligence (AI), machine learning (ML), and deep learning, and discuss the applications of each in the modern world.

Let’s dive in.

What is Artificial Intelligence?

For most people, AI is the most familiar term on the list. And as the name suggests, artificial intelligence can be loosely interpreted as the application of human intelligence to machines.

According to Andrew Moore, the Dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.”

There are currently two kinds of artificial intelligence: general and narrow. General AI is highly intelligent and adaptive and proficient at solving complex problems. Narrow AI, meanwhile, is ideal for solving simple tasks but is limited in scope. By definition, AI is fluid. Just a few years ago, something as simple as a graphing calculator was considered AI. Now, AI is a top feature in custom software development and consumer products alike.

Today, AI is behind many of the services we rely on daily, including ridesharing apps like Uber and Lyft, spam filters, plagiarism checkers, mobile check deposit features, social networking, and more. According to Adobe, the number of jobs requiring advanced AI has increased by 450% since 2013. Adobe has also determined that 47% of “digitally mature organizations” have defined AI strategies.

What is Machine Learning?

Machine learning can be loosely understood as computer systems equipped with the ability to learn.

Machine learning is a subset of artificial intelligence. ML is responsible for the creation of algorithms that can modify themselves without human intervention. ML produces desired output by feeding itself through structured data. At a high level, there are two main types of ML algorithms:

Supervised learning (Classification, Regression)

Unsupervised learning (Clustering, Association)

Today, training in ML means providing an algorithm with a massive amount of data and allowing it to learn more about the information.

Examples of ML in our daily lives include virtual personal assistants (like Siri and Alexa), traffic predictions on GPS or navigation services, and purchasing suggestions. ML is also behind face recognition, malware filtering, online customer support, and more.

What is Deep Learning?

While ML is a subset of AI, deep learning is a subset of ML. To be more specific, deep learning is a technique for implementing and improving machine learning. It is used for more complex query scenarios where a huge amount of unstructured and unlabeled data is required. Remember, machine learning algorithms always require structured and labeled data.

Interestingly, DL algorithms are based on the pattern of information processing found in the human brain. Like the brain processes information by labeling and assigning categories, DL uses artificial neural networks (ANNs) to imitate the way the human brain makes decisions. Each ANN provides a different interpretation of the data it receives, thereby creating a layered understanding.

Examples of deep learning in our current world include translation features on websites, language recognition, autonomous vehicles, text generation, and more.

Final thoughts

AI, ML and DL will allow us to create a robust technology platform that will help us make sense of vast amounts of data. Additionally, these technologies improve our quality of lives. They help us work smarter, and solve many of the small problems that impede us daily – both in our personal lives and at work.

These technologies are here to stay and companies will keep investing in it. A report from “Narrative Science” says that 80% of executives believe that Artificial Intelligence boosts productivity. The artificial intelligence market will grow to a $5.05 billion industry by the year 2020.

We at TangoCode itself are investing in AI in Digital Marketing and Digital Transformation. As we’ve heard our CEO, Dane Drotts, quote Jerry Rice, “Today I will do what others won’t, so tomorrow I can do what others can’t.”