Artificial Intelligence VS Machine Learning VS Deep Learning With Real Life Example

Both Machine Learning and Deep Learning are subsets of Artificial Intelligence (AI).

Let’s explore each of these terms with real-life examples to gain a clearer understanding.

What is Artificial Intelligence?

Artificial Intelligence refers to the capability of machines to simulate human intelligence. This involves enabling machines to understand, reason, learn, and make decisions like humans. AI systems can process vast amounts of data, identify patterns, and adjust their responses based on their learning. Unlike traditional programming, where explicit instructions are provided for every task, AI systems can adapt and improve their performance over time by learning from the data they interact with. AI encompasses various techniques and approaches, and its goal is to create systems that can think and act intelligently.

What is Machine Learning?

Machine learning involves training a computational system to improve its performance on a specific task by learning patterns and relationships from historical or past data, without requiring explicit programming. This process enables the machine to make predictions or decisions based on new data by generalizing from its learned knowledge of the old data.

What is deep learning?

Deep learning is a subset of machine learning that uses artificial neural networks to learn from data. These networks are inspired by the human brain and are capable of learning complex patterns in various types of data, such as images, videos, audios, and Natural Language Processing (NLP). This capability allows deep learning to tackle tasks that would be challenging or even impossible for traditional machine learning algorithms to learn.

In short, machine learning and deep learning are both types of AI, but they differ in the way they learn from data. Machine learning algorithms typically use supervised learning, which means that they are trained on data that has been labeled with the desired output. Deep learning algorithms, on the other hand, can use either supervised learning or unsupervised learning. Unsupervised learning means that the data is not labeled, and the algorithm must learn to identify patterns in the data on its own.

Let’s use real-life examples to easily understand the differences between these concepts.

Imagine you are driving on a bustling street. You are always watching out for possible obstacles like traffic signs, cars, and people crossing. You are also paying attention to the lane markings and the speed limit. As you drive, you are constantly making decisions about how to react to the changing conditions on the road.

For example, if you see a traffic sign that says “Stop” you need to brake and come to a complete stop. If you see a car coming up behind you too quickly, you need to change lanes to avoid being rear-ended. And if you see a pedestrian crossing the street, you need to slow down or stop to avoid hitting them.

All these decisions are made in real time, and they require a lot of information processing and quick thinking. As a human driver, you have the benefit of years of experience and training to help you make these decisions safely.

Self-driving cars need to learn to do the same thing, but they don’t have the same experience and training as human drivers. That is where machine learning, deep learning, and artificial intelligence comes in.

Machine learning algorithms can be used to train self-driving cars to detect and identify objects on the road, such as traffic signs, other cars, and pedestrians.

Deep learning algorithms can be used to extract more complex features from the data that is collected by the car’s sensors. This allows the car to make more accurate predictions about the environment and to take better actions.

Artificial intelligence algorithms can be used to combine the output of the machine learning and deep learning algorithms to make decisions about how to drive the car. AI algorithms are also used to plan the car’s route and to control the car’s speed and steering.

Machine Learning Algorithms in Self-Driving Cars:

• Identifying objects on the road: Detecting pedestrians, vehicles, signs, and obstacles using sensor data.
• Predicting the behavior of other vehicles: Anticipating movements of surrounding vehicles based on historical data.

Deep Learning Algorithms in Self-Driving Cars:

• Recognizing objects in images: Analyzing camera images to classify and identify objects in the environment.

Artificial Intelligence (AI) Tasks in Self-Driving Cars:

• Planning routes and optimizing paths: Determining the most efficient and safe route considering traffic conditions and road rules.
• Controlling the car’s speed and steering: Directing the car’s movements and speed to ensure safe navigation.
• Making driving decisions in various scenarios: Deciding actions like changing lanes and merging based on real-time conditions.
• Adapting behavior based on changing conditions: Modifying driving strategies when encountering unexpected situations or road changes.
• Enabling human-vehicle interaction: Facilitating communication between the car and passengers, pedestrians, or other drivers.
• Collision avoidance: AI algorithms are used to detect potential collisions and to take action to avoid them.
• Adapting to different driving conditions: AI algorithms are used to adapt to different driving conditions, such as bad weather or heavy traffic.
• Learning from experience: AI algorithms are used to learn from experience and to improve their performance over time.

In summary, Artificial Intelligence represents the broader concept of crafting intelligent machines. Within the scope of AI, Machine Learning serves as a technique focused on learning from data, while Deep Learning emerges as a subset of Machine Learning that uses neural networks to comprehend intricate patterns, especially adept at handling unstructured data like Images, Text, Audios.