AI vs. Machine Learning vs. Deep Learning: How Are They Different from Each Other?

AI vs. Machine Learning vs. Deep Learning: How Are They Different from Each Other?
7 min read

Artificial Intelligence, Machine Learning, and Deep Learning have now become the most commonly heard buzzwords in the existing commercial world. Today’s companies are utilizing these technologies to develop smart machines as well as applications.

As these innovations seem similar, the majority of people have misunderstood that all three terms are the same. But the reality is that all of them are used to develop applications or intelligent machines that act like a human, still, they perform different functionalities.

Want to know how they are different from each other?

Read this blog to gain a clear and in-depth understanding of AI, machine learning, and deep learning, and plan a successful AI career to grow in this field.

Understanding the Terms

Artificial Intelligence

Artificial intelligence or AI is referred to a process of transmitting information, data, and human intelligence to machines. The key objective of Artificial Intelligence is to build self-reliant machines that can think and behave like humans.

As these machines act like humans, they can complete all the tasks by learning and performing problem-solving. The majority of AI systems enable natural intelligence to fix complex problems.

Did you know?

AI-enabled devices are available everywhere. Today, around 77% of devices utilize AI technology in a single form or another.

Mainly, AI performs 3 cognitive skills similar to a human – educating, reasoning, and self-correction. You’ll learn more about this field when you’ll pursue the best artificial intelligence certification.

Machine Learning

Basically, machine learning is referred to a process that allows the system to learn automatically by itself through experiences it had and become better faster without being programmed. There is one thing that you need to know ML is a subset of AI.

ML promotes the development of programs so that they can easily use the data for themselves. The complete process makes observations on data to recognize the possible patterns being created and make the right future decisions according to the examples given to them.

The key objective of ML is to enable systems to learn by themselves with the help of experience without any sort of human intervention.

Even the tech giants such as Amazon or Netflix are standardizing the utilization of machine learning commercially. This means the market penetration of this AI subset has been tremendous.

According to the reports, the global machine-learning market is going to achieve a 38.76% growth rate between 2020-2030.

Deep Learning

Deep learning (DL) is considered as a subset of ML which leverages neural networks to behave like a human. The algorithms of deep learning completely focus on information processing patterns mechanisms to clearly identify various patterns same as our human brain.

Deep learning mainly works on bigger sets of data in comparison to ML and the prediction mechanism is self-managed by machines.

How Do They Work?

Now, it's time to understand how all these three technologies work by making complete use of data.

Working of Artificial Intelligence

To explain how artificial intelligence actually works, let’s take a quick example that most of us might be acquainted with to look at how this technology works.

Amazon Prime’s Warehouses are powered by people and predictably, they could be managed and optimized by robots that work with the help of AI.

The robots that were developed by AI professionals were built using the collected data that shows how products are stored in warehouses, how items can be picked and packed for a specific order, and how more orders come in. This is the way AI works, by using the already collected data and acclimating learnings from it to automate particular tasks.

Working of Machine Learning

To explain the working of ML, let’s take an example of a global not-for-profit organization known as Crisis Text Line that uses a machine learning technique known as entity extraction. It utilizes natural language processing as well as sentiment analysis to find out that the term “ibuprofen” is 14 times most probably to predict suicide than the word “suicide.”

This example shows how machine learning models actually work. If you contain a dataset where some patterns present themselves, you can effectively utilize ML algorithms to understand more about those patterns. Along with this, you can start a learning process to figure out the connections within the data.

In short, there are 7 easy steps involved in machine learning:

  • Data gathering
  • Data pre-processing
  • Choose model
  • Train model
  • Test model
  • Tune model
  • Prediction

Working of Deep Learning

Deep learning uses machine learning algorithms that utilize a nested hierarchy of easy concepts to showcase more complex concepts. For instance, a company known as Dialpad utilizes deep learning which includes natural language processing and also, entity extraction to transcribe huge amounts of phone call data automatically.

Further, deep learning utilizes sentiment analysis (a deep learning method) – to distinguish whether the conversation’s sentiment is negative or positive, in real-time. The people who are using Dialpad gets a chance to give response to negative sentiments with extra data and empathy.

What Are There Applications?

Let’s now have a look at various examples to understand the applications of every one of these technologies. 

Applications of Artificial Intelligence

Before investing in the best AI certification, take a look at the uses of artificial intelligence in the real world:

  • Self-Driving Vehicles like Google’s Waymo
  • Machine Translation like Google Translate
  • AI Robots like Aibo and Sophia
  • Speech Recognition applications such as OK Google or Apple’s Siri

Applications of Machine Learning

Take a look at the uses of machine learning in the real world:

  • Fraud analysis in banking
  • Sales forecasting for a variety of products
  • Stock price forecast
  • Product suggestions

Applications of Deep Learning

Here’s how deep learning is making its strong position in the real world. Here is a list of a few of its applications:

  • Image coloring
  • Object detection
  • Cancer tumor detection
  • Music generation
  • Caption bot for image captioning

In Conclusion

Artificial intelligence is a well-known 5th-generation technology that is transforming the world by utilizing its machine learning, subdomains, and deep learning. AI enables the development of the intelligent system and brings cognitive abilities to the machine.

Whereas, machine learning allows machines to learn on the basis of experience without involving humans. This technology allows the machine to learn and predict outcomes with help of pre-existing data.

Deep learning brought revolutions in the AI field that utilizes various layers of artificial neural networks to receive outstanding results for different problems like text recognition.

We hope now there is no confusion left to differentiate these terms that the majority of people face. This topic must have provided you with sufficient confidence to know the basic difference between AI, machine learning, and deep learning.

In case you have found a mistake in the text, please send a message to the author by selecting the mistake and pressing Ctrl-Enter.
Jennifer wales 2
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