Machine Learning vs. Artificial Intelligence vs. Deep Learning: What’s the Difference?

Data Literacy

While they’re easy to confuse, AI, machine learning, and deep learning are all uniquely different.

Artificial Intelligence, Machine Learning, and Deep Learning—all of these terms are used interchangeably. 

While they’re easy to confuse, each one is uniquely different. And as their wide range of applications are changing the facets of technology in every field (from healthcare to manufacturing, business to banking), it’s a good idea to understand the difference between the technology that’s changing our world.

In this post, we’re going to define each concept thoroughly so you’ll have no doubt what each of these terms mean. By the time you walk away, you’ll have a good understanding of their definitions and their uses in real life.

difference between AI, machine learning, and deep learning

Artificial Intelligence Definition

Artificial Intelligence uses computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

Correctly implemented, artificial intelligence anticipates problems and deal with issues as they arise. It does this by focusing on performing three cognitive skills (just like a human):

  1. Learning
  2. Reasoning
  3. Self-correction

In this way, it enhances human performance and augments people’s capabilities. 

Examples of AI are all around us including how we talk to Siri or Alexa in our households, to how Amazon prices their products dynamically to data scientists building models to predict traffic accidents. 

The bottom line is: AI provides us with endless potential and opportunities. In fact, the only limit to AI is your creativity.

There for four types of AI:

  • Reactive Machines - These are systems that only react. They don’t form memories, and they don’t use any past experiences for making new decisions.
  • Limited Memory - These systems reference the past, and information is added over a period of time. The referenced information is short-lived. 
  • Theory of Mind - This covers systems that are able to understand human emotions and how they affect decision making. They are trained to adjust their behavior accordingly.
  • Self-awareness - These systems are designed and created to be aware of themselves. They understand their own internal states, predict other people’s feelings, and act appropriately.

So if AI uses computers and machines to mimic the problem-solving, we can think of the computer system that mimics human actions, performs predictions, automation, and makes decisions as the application of AI. 

In other words: machine learning

Machine Learning Definition

Machine Learning, as we mentioned earlier, is one way to achieve artificial intelligence. It solves problems using algorithms and statistical models to extract knowledge from data and learn automatically. Then, using its previous experience and data, it changes its behavior accordingly. 

Machine learning uses many different techniques and algorithms to help the computer learn. Commonly, these include:

  • Decision trees
  • Random Forests
  • Support Vector Machines
  • K Means clustering

Machine learning is not the only means for us to create intelligent systems, but it is the most successful so far. Through machine learning, vast amounts of data (structured and unstructured) can be analyzed to find patterns, and businesses can leverage the information output to help make data-based decisions. 

Typically, you’ll see machine learning leveraged in use cases like:

What Types of Machine Learning Are There?

the three types of machine learning are unsupervised learning, supervised learning, and reinforcement learning

There are three types of machine learning, and each of them have their own advantages and disadvantages. 

Supervised Learning

Supervised learning is the most common sub branch of machine learning. It uses a training set to teach models to yield the desired output. Input data is fed into the model and adjusts its weights until said model has been trained appropriately. The objective is to classify and predict outcomes accurately.

Supervised learning can be split into two subcategories: 

  • Classification: This type of supervised machine learning is anything and everything where you take data and try to predict labels.
  • Regression: This type of machine learning is anything and everything where you try to predict a number output for a new item.

Supervised machine learning helps businesses solve challenges at scale. We see this supervised learning used when businesses want to predict housing prices, customer churn, find out whether a loan applicant is high-risk or not, or even classify whether or not an email is spam.  

Unsupervised Learning

Unsupervised learning finds hidden patterns in data. It’s used to draw conclusions from datasets that consist of input data without labeled responses. This is different from supervised learning, where training data includes pre-assigned category labels. 

There are many types of unsupervised learning, but we typically see two main subcategories: 

  • Clustering - This is an unsupervised learning problem that involves finding groups in data.
  • Density Estimation - This type of unsupervised learning problem involves summarizing the distribution of data

Unlike supervised learning, unsupervised learning can handle large volumes of data in real time. It’s useful in situations where a human is looking to find patterns in their data, but doesn’t know quite what they’re looking for, and might have a hard time doing it on their own.

You’re likely to see unsupervised learning in use cases such as:

  • Customer segmentation in marketing
  • Segmenting data by purchase history
  • Anomaly detection, such as fraud

Reinforcement Learning

Reinforcement learning is when we tell a computer agent to perform some tasks without giving it too much guidance. Its algorithm relies on some labeled data as well as active feedback. Instead, the computer is allowed to make its own choices. Depending on whether these choices lead to an outcome that we want or not, these choices are weighted with reward or penalty.

This process is repeated multiple times, allowing the computer to learn the optimal way of doing something by trial and error and repeated iterations.

Examples of reinforcement learning include:

  • Self-driving cars
  • Personalized product recommendations
  • Ad recommendation system

Related reading: The Ultimate Guide to Machine Learning

Deep Learning Definition

Deep Learning is a subfield of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.  Deep learning’s core concept lies in artificial neural networks, which enable machines to make decisions. 

The major difference between deep learning and machine learning is the way data is presented to the machine. Machine learning algorithms usually require structured data, whereas deep learning networks work on multiple layers of artificial neural networks.

How Does Deep Learning Work? 

  1. Calculate the weighted sums. 
  2. The calculated sum of weights is passed as input to the activation function. 
  3. The activation function takes the “weighted sum of input” as the input to the function, adds a bias, and decides whether the neuron should be fired or not. 
  4. The output layer gives the predicted output. 
  5. The model output is compared with the actual output. After training the neural network, the model uses the backpropagation method to improve the performance of the network. The cost function helps to reduce the error rate.

Where are we Today with AI?

As you can see, AI applies machine learning, deep learning and other techniques to solve actual problems. But what do these problems look like in real life, and where are we today with AI applications?

With AI, you can ask a machine questions – out loud – and get answers in real time. AI gives you the the ability to sift through all your data and make logical connections between past actions and different criteria.

Today, we see AI used in industries such as:

  • Transportation
  • Manufacturing
  • Finance
  • Healthcare
  • Urban planning

And as technology advances, it's clear that the power of AI will expand.

According to Forrester, it’s expected that in 2022, traditional businesses will adopt an AI-first approach to platform and digital transformation. The more “AI inside,” the more enterprises can shrink the latency between insights, decisions, and results.

In fact, by 2024, the power of AI will be democratized. It's predicated that as much as 65% of application development will be done on no-code/low-code platforms, according to a Gartner Magic Quadrant report.

Make Your Business Strong with Machine Learning

The bottom line is: AI, machine learning, or deep learning—all are a remarkable technology that delivers the kind of business intelligence required to stay ahead. And as technology continues to advance, companies are turning to platforms to help leverage machine learning capabilities. 

To see the power of machine learning (and how fast no-code tools work to deliver predictions) be sure to book a demo with our team! logo

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