🧠 Introduction
Welcome to Day 3 of the 30 Days of AI series! Over the last two days, we’ve explored what
Artificial Intelligence is and the different types it can take. Today, we’re going to clear up
one of the most common points of confusion: the difference between Artificial Intelligence
(AI), Machine Learning (ML), and Deep Learning (DL).
These three terms are often used interchangeably, but they refer to different—though
closely related—concepts. Let’s explore how they’re related, how they’re different, and
where each one fits in the big picture of modern technology.
🧠 What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) is an expansive area in computer science that aims at creating
machines or software programs which can accomplish certain tasks which require human
intelligence on the average. Such activities may comprise:
Learning from experience or data
Reasoning logically
Problem-solving in dynamic or unfamiliar environments
Understanding natural language (like reading or listening to humans)
Recognizing images or objects
Making decisions, sometimes with limited or incomplete information
In contrast to conventional programming, to which each command needs to be described
explicitly, AI-based systems are partially independent. They are able to change, react or get
better with time according what they are processing.
🔍Real-Wohttps://ai.google/https://ai.google/rld Examples of AI
Smart Assistants (such as Siri, Alexa, and Google Assistant): Interpret verbal
communication, respond to queries and complete assignments.
Google maps & navigation: Forecast traffic, rerouting and estimating arrival time.
Face Recognition: It is utilized in security systems and smartphones in order to
identify people.
Recommendation Engines: Netflix, YouTube, Amazon make recommendations or
suggestions according to your taste.
Artificial Intelligence in Healthcare: AI devices scan health care images to diagnose
illnesses such as cancer.
Key Insight: Artificial Intelligence is never a single technology but rather a mixture of a
variety of techniques that interact to produce simulation of human intelligence
AI is the umbrella term under which Machine Learning and Deep Learning fall.
What is Machine Learning (ML)?
Machine Learning (ML) is a branch of AI which gives systems the capacity to undertake
learning automatically and enhance through experience without requiring human
programmers to write lines of code.
That is, rather than communicating to the machine on a step-by-step basis what it is you
want done, you come in with the data and the machine figures out how to do the thing given
the patterns it discovers in the data.
What is Machine Learning? How it works?
In a very simplified way, this is how it goes:
Input Data: e.g., mark name emails as spam or non-spam).
Model Training: The algorithm interprets the data to get information on patterns.
Prediction: The model is predictive (gives prediction on new data (e.g. spam or not on a new
email).
Feedback: The performance of the model becomes better with time as its predictions are
measured to determine how accurate or accurate they are.
ML can be of three major types:
Supervised Learning: Trains on labeled information labels (e.g. house prices).
Unsupervised Learning: Discovers hidden patterns in unlabelled data (e.g. Customer
segmentation).
Reinforcement Learning: Trial and error learning, (e.g. robotics or game-playing AIs), is
rewarded or punished.
Findings and typical usage of Machine Learning:
Spam Filtering: Gmail is able to quickly detect which messages are probable to be spam
through the past information.
Predictive Text: Word suggestions as you are typing (e.g. smartphones).
Credit Scoring: Banks forecast whether a hand will pay off a loan.
Product If you have previously bought a product, Amazon advises you to buy additional
products.
Medical Diagnosis: ML models identify the early diagnoses of diseases through medical
records.
Note: It is important to note that Machine Learning is potent yet confined. It gets used to
what it has been taught to learn and it finds it hard with absolutely new situations.
🧠 What is Deep Learning (DL)?
Deep Learning is a higher form of Machine Learning and applies deep neural networks to
huge and complex data.These neural nets are based on the human brain structure and its
operation. Deep Learning has a particular ability to learn with unstructured information
such as:
Images
Audio
Video
Natural language (text)
Why is “Deep” the name?
The term “deep” in Deep Learning describes the neural network’s number of layers. More
intricate features are extracted by each layer:
Simple edges in a picture may be detected by early layers.
Later layers are able to identify forms.
Specific objects, such as a car or cat, can be identified by final layers.
Examples of Deep Learning:
Autonomous Vehicles: Base driving decision on GPS, sensor and real-time video
Voice assistants: Provide voice responses using voice commands and understand them
in a natural language.
Image Classification: Identify objects in images and tag it (e.g. in Google Pictures,
Facebook).
Chatbots and Language Models: ChatGPT or the Google Bard along with other chatbots
help to understand the language and generate it in a human-like manner.
Facial Recognition: Use it to find faces of crowd or unlock cellphones.
Even the extremely powerful models of deep learning that could achieve human-like
accuracy in a variety of tasks, often require a great volume of information and powerful
GPUs to train.
How They Relate to Each Other
You can think of the relationship between AI, ML, and DL like a set of nested circles:
In other words, Deep Learning is a part of Machine Learning, which in turn is a part of
Artificial Intelligence.
Final Thoughts
Understanding the difference between AI, ML, and DL is important for anyone exploring the
tech world. While AI is the broad goal of building intelligent systems, Machine Learning
gives them the ability to learn, and Deep Learning takes it even further by using advanced
structures inspired by the human brain.
Join me tomorrow for Day 4, where we’ll explore real-life applications of AI in healthcare,
education, agriculture, and beyond!