Artificial Intelligence vs Machine Learning vs Deep Learning

The last few months of 2022 and the entire of 2023 have been dominated by a single term ‘Artificial Intelligence. It all started with OpenAI releasing their famous chatbot ChatGPT and since then we are seeing at least one new AI-powered tool being released per day. March 2023 alone saw 1000+ AI tools released.

To provide evidence for my claim about the sharp interest in Artificial Intelligence, the below snapshot taken from Google Trends is sufficient. November 2022 was when OpenAI released ChatGPT, the spike started in November. Numbers and data never lie.

Not only did ChatGPT spike the interest in the word Artificial Intelligence but also two other words started doing rounds on the internet, Machine Learning (ML) and Deep Learning. Again, I am going to let the numbers do the talking courtesy of Google Trends.

In this blog, we will explore in detail what the three terms Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning mean. What are their applications? What are the similarities and differences if any?  So, let’s get started first with Artificial Intelligence.

What Is Artificial Intelligence?

To keep it simple, artificial intelligence more commonly referred to as AI is defined as the ability to program computers that enable them to perform tasks that require human-level intelligence. These tasks can include a variety of things such as seeing and understanding language, translation, recommendation and so on.

For those who prefer a more standard definition, John McCarthy, the father of AI provided one in his 2004 paper. “AI is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to biologically observable methods.” 

Since then, numerous papers have been published that try to provide various definitions of AI. A notable one comes from Stuart Russell and  Peter Norvig in a popular AI textbook ‘Artificial Intelligence: A modern approach’. They delve into four potential goals or definitions of AI, which differentiates computer systems based on rationality and thinking vs. acting:

Human approach:

• Systems that think like humans
• Systems that act like humans

Ideal approach:

• Systems that think rationally
• Systems that act rationally

What Are the Applications Of Artificial Intelligence?

AI is all around us now. Various industries including banking, healthcare, automotive and entertainment are all leveraging AI. From improving life expectancy by identifying potential diseases early to providing personalized feeds on OTT apps, AI is everywhere.

Let’s explore some popular real-world applications of AI next.

Virtual Assistants
Virtual assistants like Apple’s Siri, Amazon’s Alexa, and Google Assistant use AI to understand natural language, recognize speech patterns, and respond to user queries. These assistants can perform a wide range of tasks, from setting reminders and playing music to controlling smart home devices.

Autonomous Vehicles
Self-driving cars and other autonomous vehicles use AI to navigate roads, recognize objects, and make decisions in real time. AI-powered sensors and algorithms can detect obstacles, pedestrians, and other vehicles, making driving safer and more efficient.

Fraud Detection
Banks and financial institutions use AI to detect fraud and other forms of financial crime. AI algorithms can analyze large volumes of data, identify patterns of suspicious behaviour, and flag potentially fraudulent transactions for review.

Healthcare
AI is being used in healthcare to improve patient outcomes and reduce costs. AI-powered diagnostic tools can analyze medical images and data, identify patterns of disease, and provide personalized treatment recommendations. AI-powered chatbots can also provide patients with basic medical advice and support.

Personalization
AI is being used to create personalized experiences for consumers in a variety of industries, including e-commerce, entertainment, and advertising. AI algorithms can analyze user behaviour and preferences to recommend products, content, and services that are tailored to individual users.

So, that is all about AI a.k.a Artificial Intelligence. Let’s now talk about the other two-term commonly being used interchangeably with Artificial Intelligence, Machine Learning a.k.a ML and Deep Learning

What Is Machine Learning?

Machine Learning (ML) is a subfield of Artificial Intelligence. ML involves building algorithms and models that enable computers to learn from data and predict outcomes and discover patterns. Historical data is fed to algorithms in the hope that it helps the computer learn from it and uncovers insights and patterns. 

The rule of ML is simple, the outcome and patterns that the algorithm can uncover entirely depend on the quality of the data that has been used to train the model. Let’s understand two important concepts when it comes to data

Labeled Data

Labeled data is data that has been labeled with a specific category or outcome. For example, a dataset of customer reviews might be labeled with positive or negative sentiment scores, or a dataset of images might be labeled with categories like “cat” or “dog.” 

Unlabeled Data

Unlabeled data is data that has not been labeled with any specific category or outcome. For example, a dataset of customer behaviour might include data like clickstreams or purchase histories, without any labels indicating which customers are likely to make a purchase. 

Now that we understand data, let’s explore 3 types of Machine Learning algorithms 

Supervised Learning

In supervised learning, the algorithm is trained on a labeled dataset, using the labels to learn to predict outcomes for new, unlabeled data. The algorithm uses statistical methods to learn the relationship between the input features and the output labels.

Examples of supervised learning algorithms include linear regression, decision trees, and neural networks.

Unsupervised learning

In unsupervised learning, the algorithm is trained on an unlabeled dataset, using clustering or other techniques to identify patterns and relationships in the data. The algorithm doesn’t know the correct output, so it tries to find hidden patterns and relationships in the data on its own.

Examples of unsupervised learning algorithms include k-means clustering, principal component analysis (PCA), and association rule learning.

Reinforcement learning

In reinforcement learning, the algorithm learns by receiving feedback in the form of rewards or punishments for certain actions. The algorithm learns to maximize rewards and minimize punishments over time. Reinforcement learning is often used in gaming and robotics, where the algorithm needs to learn how to make decisions in complex and dynamic environments.

Examples of reinforcement learning algorithms include Q-learning and deep reinforcement learning.

What Are the Applications Of Machine Learning?

Machine Learning systems have been used for quite some time now. Let’s explore some real-world use cases of Machine Learning.

Recommendation systems 

Recommendation systems are used to suggest products, services, or content to users based on their previous behaviour and preferences. For example, Netflix uses machine learning to recommend movies and TV shows to its users based on their viewing history, and Amazon uses machine learning to suggest products to customers based on their purchase history.

Fraud detection

Machine learning algorithms can be used to detect fraudulent transactions in financial transactions, such as credit card payments or insurance claims. These algorithms can learn to identify patterns and anomalies in the data to flag suspicious activity and prevent fraud.

Image recognition

Machine learning algorithms can be used to recognize objects and patterns in images and videos. This is used in a wide range of applications, from facial recognition technology in security systems to self-driving cars that use cameras to detect and avoid obstacles.

Natural language processing

Natural language processing (NLP) is a branch of machine learning that deals with analyzing, understanding, and generating human language. This technology is used in virtual assistants like Siri and Alexa to understand and respond to voice commands, as well as in chatbots and language translation systems.

Predictive maintenance 

Machine learning algorithms can be used to predict when equipment or machinery is likely to fail, allowing for proactive maintenance and reducing downtime. For example, a factory might use machine learning to analyze sensor data from machinery to detect patterns that indicate an upcoming failure, allowing for repairs to be scheduled before the machinery breaks down.

With Machine Learning understood, let’s now move to the third term being talked about widely on the internet, Deep Learning.

What Is Deep Learning?

Deep Learning is a subfield of Machine Learning that uses artificial neural networks to learn and model complex patterns and relationships in large datasets. Deep learning algorithms are designed to automatically learn and improve through experience, without being explicitly programmed to perform a specific task.

The term “deep” refers to the fact that deep learning algorithms typically have multiple layers of interconnected neurons, which allow them to learn increasingly complex features of the data as they move through the layers. These layers enable deep learning algorithms to automatically discover hierarchical representations of the data, with each layer learning more abstract and higher-level features.

Deep learning is particularly well-suited to tasks like image recognition, speech recognition, natural language processing, and machine translation, where the data is high-dimensional and the relationships between inputs and outputs are complex and nonlinear. 

Some popular deep learning algorithms include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs).

Artificial Intelligence vs Machine Learning vs Deep Learning

Having looked at the three terms in isolation, let’s now address the important topic of how these three fields relate to each other. The image below summarizes this. 

Artificial Intelligence is the broadest field that encompasses any techniques that enable computers and machines to do tasks that would require human-level intelligence.

Machine Learning is a subfield of AI that concentrates on developing algorithms that can learn and improve from experience.

Deep Learning is a subfield of ML that uses neural networks to learn complex patterns and relationships between data. 

DL algorithms can learn to extract features from large amounts of unlabeled data, which can be used to improve the accuracy of the algorithm on labeled data. This is known as unsupervised learning and is a key advantage of DL algorithms over traditional machine learning algorithms. This is made possible due to artificial neural networks.

Artificial Neural networks(ANN) are a complex and large topic and need a dedicated article of their own to explain in detail. However, as they are key to making Deep Learning possible it’s important to understand them at least in some capacity.

By definition, neural networks are computing systems inspired by the biological neural networks that make up the human brain. An artificial neural network in its most basic form has three layers as shown in the image below.

Input Layer

This layer receives the input data and passes it on to the next layer. The number of nodes in the input layer is determined by the number of input features in the data.

Hidden Layer

These layers are located between the input and output layers and are responsible for processing the input data. Each node in a hidden layer is connected to every node in the previous layer, and the weights on these connections are adjusted during the training process to optimize the performance of the network.

Output Layer

This layer produces the final output of the network. The number of nodes in the output layer depends on the nature of the problem being solved. For example, a binary classification problem may have one output node, while a multi-class classification problem may have multiple output nodes, each corresponding to a different class label.

When To Use Machine Learning and When To Use Deep Learning?

Here’s a brief overview of when to use machine learning and when to use deep learning.

Machine Learning:

Machine learning is best suited for solving problems where the input data has a clear set of features that can be used to make predictions or decisions. In other words, if the problem can be formulated as a set of rules or if-then statements, machine learning is often the best approach. 

For example, ML algorithms can be used to predict whether a customer will churn or not based on their purchase history or to identify fraudulent transactions based on a set of predefined rules.

Deep Learning:

Deep learning is best suited for solving problems where the input data is high-dimensional and the features are difficult to define or extract. In other words, if the problem requires the algorithm to learn complex patterns or relationships in the data, deep learning is often the best approach. 

For example, DL algorithms can be used to recognize objects in images or to transcribe speech into text.

However, DL algorithms can be more computationally intensive and require larger amounts of training data than ML algorithms, which can make them more difficult to implement in some applications.

Conclusion

In conclusion, AI, ML, and deep learning are three interconnected fields. AI is the broadest field, ML is a subset of AI and deep learning is a subset of ML. Understanding all three terms, how they relate to each other and which to use when are key to staying ahead in the AI race.

Machine learning has proven to be an effective approach for solving problems where the input data has a clear set of features, while deep learning is better suited for problems where the input data is high-dimensional and the features are difficult to define or extract.

Some of the most promising applications of AI, ML, and deep learning include image and speech recognition, natural language processing, autonomous vehicles, and healthcare. As these technologies continue to advance, we can expect to see even more transformative applications emerge in the coming years.

As these fields continue to evolve and grow, we can expect to see even more exciting breakthroughs that have the potential to transform the world in ways we can hardly imagine. Whether it’s developing new healthcare treatments, improving transportation safety, or enhancing our understanding of the universe, AI, ML, and deep learning will play a critical role in shaping the future of our world.