Use cases of AI/ML/DL

Description

Although AI, ML, and DL are closely related, each has distinct real-world applications based on its capabilities. These examples help distinguish what falls under general AI, what specifically uses machine learning, and what demands the depth of deep learning.

Examples (Code)

Below is a simple example 

# AI Example – Rule-based expert system (no ML)
def traffic_light_control(sensor_input):
    if sensor_input == "vehicle_detected":
        return "Turn green"
    return "Stay red"

# ML Example – Predicting house prices
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
print(model.predict(X_test))

# DL Example – Image recognition using CNN
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
model = Sequential([
    Conv2D(32, (3,3), activation='relu', input_shape=(64, 64, 3)),
    MaxPooling2D(pool_size=(2,2)),
    Flatten(),
    Dense(64, activation='relu'),
    Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy')
model.fit(X_train, y_train, epochs=5)

Real-World Applications

Artificial Intelligence

Virtual assistants like Alexa, rule-based expert systems in healthcare, and game AI (e.g., chess engines).

Machine Learning

Netflix recommendations, spam filters, and predictive maintenance systems in manufacturing.

Deep Learning

Self-driving car vision, facial recognition systems, and real-time language translation.

Where topic Is Applied

Use Case AI ML DL
Customer Support Chatbots with rule-based replies Sentiment analysis for better support Voice assistants with speech recognition
Healthcare Expert systems for diagnostics Disease prediction using patient data Medical image classification (MRI scans)
Finance Fraud rule detection systems Credit scoring models Fraud detection using anomaly detection via DL
Transportation Traffic signal automation Vehicle route optimization Autonomous driving with object detection
Retail Personalized ad banners Recommendation engines Vision-based product recognition

Resources

Additional resources

Interview Questions with Answers

How can you practically distinguish between AI, ML, and DL?

AI includes systems that simulate intelligence like expert systems. ML focuses on data-driven learning like prediction models. DL goes further, handling unstructured data like images and voice using neural networks.

Can a system use AI without ML or DL?

Yes, a rule-based chatbot or a decision tree without learning capabilities is AI but not ML or DL.

Why is deep learning preferred for image recognition?

DL uses convolutional neural networks that can automatically extract spatial hierarchies from images, making it better than traditional ML methods.

Give an example where ML is more suitable than DL.

Predicting house prices using structured tabular data—ML algorithms like linear regression are lightweight and effective.

What makes DL more computationally intensive than ML?

DL models have many parameters, layers, and require large datasets and GPU computation, unlike simpler ML models.