Deep Learning vs. AI: Is Deep Learning Artificial Intelligence?

activated alexa on top of book
Spread the love

Technology is fast advancing, with computing and processing power doubling every 18 months and the amount of data created increasing exponentially. In recent years, there has been a lot of talk about artificial intelligence (AI) and the different learning algorithms that make up AI. In particular, deep learning has been getting attention for its potential to revolutionize various fields, such as computer vision, natural language processing and self-driving cars.

How can you describe the relationship between the different concepts?

Here are some essential details to help you understand the vital determining factors between AI, machine learning and deep learning.

Artificial Intelligence vs. Machine Learning vs. Deep Learning

male engineer field testing agriculture robotics
Photo by ThisisEngineering RAEng on Unsplash

The relationship between AI, machine learning and deep learning can be illustrated using a simple analogy. Think of your brain as the AI with the ability to reason, make decisions and solve problems. The various regions of your brain can be thought of as different machine learning algorithms. Finally, the neurons in your brain are comparable to the deep learning networks.

In other words, machine learning is a subset of AI and deep learning is a subset of machine learning.

AI is based on the idea that machines can accomplish tasks that require human intelligence, and with its countless applications, it has the potential to change the world in a big way.

Here are some examples of AI applications:


Robots are increasingly used in various industries, including manufacturing, healthcare, and even home automation. Some of the most notable examples of AI-powered robots include the Roomba vacuum cleaner, the Da Vinci surgical robot and Amazon’s delivery drones.


Using tech for business growth is nothing new, but with the advent of AI, e-commerce is evolving rapidly. Some of the biggest names in the industry, such as Amazon and Alibaba, are using AI to personalize the shopping experience for their customers and make recommendations based on their purchase history.

Remote Work

The rise of technology has led to the prevalence of innovative workplaces. Now, employees can work from anywhere worldwide, thanks to tools like video conferencing and online collaboration. With AI, businesses can automate various tasks, such as customer support and data entry, which makes remote work more efficient.


The education sector is also benefiting from AI. For instance, online learning platforms, such as Coursera and Udacity, are using AI to provide personalized learning experiences for students. In addition, AI is being used to develop new teaching methods, such as adaptive learning.


The finance industry uses AI for various purposes, such as managing risks and automating financial processes. These days, AI is even being used to develop new financial products, such as Robo-advisors, which help people invest their money.

How Does Deep Learning Work?

AI and machine learning are mainly concerned with making a computer system smarter. In contrast, deep learning is focused on making that system more accurate.

With a deep learning model, a computer system can automatically improve its accuracy by increasing the number of layers in its algorithms. These algorithms require training big data sets, which is why deep learning is often used in combination with big data.

In other words, deep learning algorithms enable a machine to improve its performance by increasing the number of layers in its algorithms. The more data sets it is trained on, the better it learns.

How is Deep Learning Used?

yellow autonomous vehicle with passengers on the road
Photo by Falco on Pixabay

A deep learning algorithm plays a significant role in several applications. And unlike other machine learning algorithms, deep learning is much more reliable on complex tasks.

Here are some examples of deep learning applications:

Autonomous Vehicles

Deep learning is being used to develop autonomous vehicles. With the help of sensors and cameras, these vehicles can detect objects and make decisions accordingly. For instance, if an autonomous car detects a pedestrian crossing the street, it will stop to avoid an accident.

Self-driving cars developed by Tesla and Waymo use deep learning algorithms for object detection. And although these cars are still in their introductory stage, it is estimated that they will be widely available in the next few years.

Speech Recognition

Deep learning is also used for speech recognition. Using this technology, a machine can understand human speech and respond accordingly. For instance, Siri and Alexa are two popular examples of speech recognition applications.

In addition, deep learning is being used to develop new language-learning methods. For instance, Google Translate uses natural language processing to detect human language and provide accurate translations.

News Aggregation and Fraud Detection

With the help of deep learning algorithms, a machine can automatically detect fake news and flag it accordingly. Companies like Google and Facebook are using this technology to curb the spread of fake news.

In addition, deep learning is being used for fraud detection. For instance, banks use this technology to detect fraudulent transactions and prevent them from happening. The deep learning algorithm is trained on a dataset of past fraudulent transactions. And when a new transaction is made, the algorithm can determine whether it is fraud or not.


Many popular navigation apps, such as Google Maps and Waze, use deep learning. With the help of sensors and cameras, these apps can detect traffic, road closures and accidents. They can then provide accurate routes to the users.

It also provides better traffic predictions by detecting patterns in the data. For instance, if a particular road is regularly congested at a specific time, the app will suggest an alternative route to the users. However, connectivity is required for these apps to work correctly.

Health Care

The application of deep learning in healthcare is vast. It is used for disease detection, drug development and medical image analysis.

For instance, deep learning is being used to develop new cancer treatments. With the help of this technology, doctors can detect cancer at an early stage and treat it accordingly.

In addition, deep learning is being used to develop new drugs. With the help of this technology, scientists can identify new drug targets and develop new medicines accordingly.

And as modern medical applications become more data-driven, improved patient outcomes are expected.

What are the Advantages of Deep Learning vs. Machine Learning?

While there is a close relationship between deep learning and machine learning, there are some critical differences between the two.

Below are areas where deep learning holds an advantage over machine learning:

Deep learning mimics the human brain

Machine learning models involve the use of data to perform tasks. However, deep learning models are designed to mimic the human brain. It has deep neural networks that function similarly to how a human brain processes information. This ability makes them more efficient at solving complex problems.

Deep learning can learn on its own

The artificial neural network in deep learning can learn on its own. That is, it can automatically detect patterns in data and improve its performance. On the other hand, a machine learning algorithm needs to be explicitly told what to do. For instance, if you want to develop a machine-learning model for facial recognition, you need to provide a dataset of faces for the algorithm to learn from.

Deep learning is more accurate

Because deep learning can handle big sets of diverse and unstructured data and learn on its own, it is more accurate than machine learning. Machine learning models can be easily biased if the dataset is not properly cleaned. In addition, they can also overfit the data if the training dataset is small.

Deep learning does not require human interference

Machine learning algorithms require a human to input data, tune parameters, and interpret results. However, deep learning can do all of these things on its own. That is because artificial neural networks are designed to learn and improve on their own.

Deep learning is better at detecting patterns

With large data sets and the ability to learn on their own, deep learning is better at detecting patterns. Machine learning models can only detect linear patterns. However, deep learning can detect non-linear patterns. This ability makes it better at solving complex problems.

What are the Disadvantages of Deep Learning vs. Machine Learning?

Despite the fact that deep learning has some advantages over machine learning, there are also some disadvantages. These include the following:

Deep learning is more complex

The artificial neural network in deep learning is composed of many layers. It makes the model more complex and challenging to understand. In addition, debugging a deep learning model can be tricky if something goes wrong. But with the help of a professional data scientist, deep learning can be used effectively.

Deep learning requires more data

Deep learning algorithms require more data to learn from. They need to be trained on large datasets in order to perform well. If the dataset is small, the deep learning model might not be able to learn anything useful.

Deep learning is computationally expensive

Deep learning models are composed of many layers of artificial neural networks. This makes them computationally expensive to train. In addition, they require special hardware, such as GPUs, to run effectively. However, the cost of deep learning is decreasing as more companies invest in this technology.

Deep learning can lead to machine-driven decisions

Deep learning algorithms are designed to learn on their own. As such, they can make decisions without the need for human intervention. This could lead to unemployment in some sectors as well as ethical concerns.

Deep learning has the potential to malfunction

While deep learning algorithms are designed to learn on their own, they can also malfunction. That is because they are not perfect and can make mistakes. For instance, they might misclassify data or make wrong decisions. So, it is important to have a data scientist on hand to interpret the results.

How Does Deep Learning Technology Matter in Today’s World?

With the rapid development of deep learning technology, its applications are becoming more and more widespread. And as technology continues to improve, it’s not surprising to see even more amazing applications in the future.

Deep learning is already available in various industries, and the benefits it brings are undeniable. It has made a significant impact on how people live and work. By helping humans to automate tasks, it has made lives more accessible and more efficient.

In the future, deep learning will become even more commonplace. And as it does, it’s likely to bring even more benefits to the world.


Spread the love
Scroll to Top