Machine Learning vs Deep Learning vs Artificial Intelligence (ML vs DL vs AI)

In the realm of cutting-edge technologies, Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) stand as pivotal forces, driving innovation across industries. Yet, their intricate interplay and unique characteristics often spark confusion. In this article, we embark on a journey to demystify the trio, exploring the fundamental differences and symbiotic relationships between ML vs DL vs AI. Unravel the intricacies of each domain and gain a comprehensive understanding of how these transformative technologies collectively shape the future of intelligent systems and drive unparalleled advancements in our digital landscape.

First of all, let’s look at some of the facts and figures of the impact of AI on the IT industry at large!

•  60% of videos watched on YouTube are a result of recommendations (AI).
• 40% of apps installed from PlayStore are a result of AI-driven recommendations.
• AI can accelerate business productivity by 40%.
• The number of AI-based start-ups has amplified to 14% since the 2000s.

This article was published as a part of the Data Science Blogathon

ML vs DL vs AI: Overview

What is Artificial Intelligence?

AI is a broader term that describes the capability of the machine to learn and solve problems just like humans. In other words, AI refers to the replication of humans, how it thinks, works and functions.

On the lower ground, AI can be seen as a program that instructs the machine to function in a certain way according to the situation. So, we can call a bunch of if-else statements, AI. For instance, self-driving car moving on the road. A small algorithmic rule for such a car can be:

if distance_from_in_front <100m:
    stop();
else:
    keep_moving();

Relation of AI with ML and DL

There are two ways of incorporating intelligence in artificial things i.e., to achieve artificial intelligence. One is through machine learning and another is through deep learning. That means DL and ML are ways of achieving AI.

What is Machine Learning?

Now that we have understood the term “AI”, we can take a closer look on ML and DL.

ML comprises algorithms for accomplishing different types of tasks such as classification, regression, or clustering. The accuracy of algorithms increases with an increase in data.

“Technique to learn from data through training and then apply learning to make an informed decision”

Analyzing and learning from data comes under the training part of the machine learning model. During the training of the model, the objective is to minimize the loss between actual and predicted value. For example, in the case of recommending items to a user, the objective is to minimize the difference between the predicted rating of an item by the model and the actual rating given by the user.

“Difference between the predicted and actual value is computed using loss-function or objective function. Therefore, defining the objective/loss function is the gist of ML model.”

In today’s era, ML has shown great impact on every industry ranging from weather forecasting, Netflix recommendations, stock prediction, to malware detection. ML though effective is an old field that has been in use since the 1980s and surrounds algorithms from then. Below is a small snippet of the ML model.

 from sklearn.linear_model import LinearRegression predictor = LinearRegression(n_jobs=-1) predictor.fit(X=TRAIN_INPUT, y=TRAIN_OUTPUT) 

What is Deep Learning?

Deep learning is an emerging field that has been in steady use since its inception in the field in 2010. It is based on an artificial neural network which is nothing but a mimic of the working of the human brain.

Just like the ML model, the DL model requires a large amount of data to learn and make an informed decision and is therefore also considered a subset of ML. This is one of the reasons for the misconception that ML and DL are the same. However, the DL model is based on artificial neural networks which have the capability of solving tasks which ML is unable to solve.

The future is driven by DL models. Without DL, Alexa, Siri, Google Voice Assistant, Google Translation, Self-driving cars are not possible. To learn more about building DL models, have a look at my blog on Deep Learning in-depth.

Advantages of DL over ML

1. Feature Extraction

Machine learning algorithms such as Naive Bayes, Logistic Regression, SVM, etc., are termed as “flat algorithms”. By flat, we mean, these algorithms require pre-processing phase (known as Feature Extraction which is quite complicated and computationally expensive) before been applied to data such as images, text, CSV. For instance, if we want to determine whether a particular image is of a cat or dog using the ML model. We have to manually extract features from the image such as size, color, shape, etc., and then give these features to the ML model to identify whether the image is of a dog or cat.

However, DL models do not any feature extraction pre-processing step and are capable of classifying data into different classes and categories themselves. That is, in the case of identification of cat or dog in the image, we do not need to extract features from the image and give it to the DL model. But, the image can be given as the direct input to the DL model whose job is then to classify it without human intervention.

Raw Data is given to DL model. Pre-processed data is given to ML model.

2. Big Data

With technology and the ever-increasing use of the web, it is estimated that every second 1.7MB of data is generated by every person on the planet Earth. Therefore, analyzing and learning from data is of utmost importance.

Deep Learning is seen as a rocket whose fuel is data.

The accuracy of ML models stops increasing with an increasing amount of data after a point while the accuracy of the DL model keeps on increasing with increasing data.

Below is a small snippet to build a DL model:

from keras.models import Sequential
from keras.layers.core import Dense, Activationmodel = Sequential()
model.add(Dense(10, activation=’sigmoid’, input_shape=(784,)))
model.add(Dense(10, activation=’softmax’))

ML vs DL vs AI: Examples

In this section will be listing down the examples and use cases of ML vs DL and AI:

Machine Learning Examples

1. Image Recognition: Identifying objects, people, or patterns within images. Used in facial recognition, object detection, and self-driving cars.
2. Natural Language Processing (NLP): Understanding and processing human language. Used in chatbots, sentiment analysis, and language translation.
3. Speech Recognition: Converting spoken language into text. Used in virtual assistants, voice-controlled systems, and transcription services.
4. Recommendation Systems: Recommending products, movies, or content based on user preferences. Used in e-commerce, streaming platforms, and personalized marketing.
5. Anomaly Detection: Identifying unusual patterns or outliers in data. Used in fraud detection, fault monitoring, and cybersecurity.
6. Predictive Maintenance: Predicting when equipment or machinery may fail to enable timely maintenance. Used in manufacturing and industrial settings.
7. Credit Scoring: Assessing creditworthiness of individuals or businesses. Used in financial institutions for loan approvals.
8. Healthcare Diagnostics: Assisting in disease diagnosis and medical image analysis. Used in medical imaging, pathology, and radiology.
9. Autonomous Vehicles: Enabling self-driving cars to navigate and make decisions based on real-time data.
10. Language Generation: Generating text, such as auto-complete suggestions or chatbot responses.
11. Fraud Detection: Identifying fraudulent transactions in banking and online transactions.
12. Virtual Assistants: AI-driven applications that respond to voice commands and perform tasks like scheduling and reminders.
13. Stock Market Prediction: Forecasting stock prices and market trends based on historical data.
14. Customer Segmentation: Dividing customers into groups based on their behaviors and preferences for targeted marketing.
15. Personalized News Feeds: Providing users with tailored news and content based on their interests and reading habits.

Examples of DL

1. Image Classification: Classifying objects, scenes, or animals in images. Used in photo organization, automated tagging, and medical image analysis.
2. Natural Language Processing (NLP) Tasks: Performing advanced NLP tasks, such as language translation, sentiment analysis, and text generation.
3. Speech Recognition: Converting spoken language into text with high accuracy. Used in voice assistants, transcription services, and speech-to-text applications.
4. Object Detection: Identifying and locating multiple objects within an image. Used in autonomous vehicles, surveillance systems, and video analysis.
5. Facial Recognition: Identifying and verifying individuals based on facial features. Used in security systems, unlocking devices, and personalized experiences.
6. Generative Adversarial Networks (GANs): Generating new, realistic data by pitting two neural networks against each other. Used in generating synthetic images, videos, and art.
7. Recommendation Systems: Creating personalized recommendations for users based on their preferences and behavior.
8. Semantic Segmentation: Assigning a semantic label to each pixel in an image, used in advanced image understanding tasks.
9. Style Transfer: Transferring the style of one image to another, creating artistic effects.
10. Autonomous Vehicles: Enabling self-driving cars to navigate and make decisions based on real-time data using DL models like Convolutional Neural Networks (CNNs).
11. Drug Discovery: Assisting in drug discovery and design by predicting molecular properties and interactions.
12. Medical Diagnosis: Assisting doctors in diagnosing medical conditions using DL models trained on medical images and data.
13. Game AI: Training DL models to play games and achieve superhuman performance, such as DeepMind’s AlphaGo.
14. Chatbots: Creating conversational agents that can interact with users and answer queries.
15. Music Generation: Generating new music compositions using DL models.

Artificial Intelligence Examples

1. Virtual Assistants: AI-powered assistants like Siri, Google Assistant, and Alexa can understand natural language and perform tasks, such as setting reminders, answering queries, and controlling smart home devices.
2. Recommendation Systems: AI algorithms analyze user preferences and behavior to provide personalized recommendations for products, movies, music, and more.
3. Chatbots: AI-driven chatbots engage in natural language conversations with users, assisting with customer support, answering questions, and providing information.
4. Autonomous Vehicles: AI enables self-driving cars to navigate and make decisions based on real-time data from sensors and cameras.
5. Image and Video Analysis: AI can analyze and interpret visual content, including object detection, facial recognition, and content moderation.
6. Language Translation: AI-powered translation tools automatically convert text between different languages.
7. Fraud Detection: AI systems can detect fraudulent activities and transactions in real-time, helping prevent financial losses.
8. Healthcare Diagnosis: AI assists in diagnosing medical conditions by analyzing medical images and data, aiding in early detection and treatment.
9. Smart Personalization: AI enables personalized experiences in various applications, such as content recommendations, user interfaces, and targeted marketing.
10. Email Filtering: AI algorithms categorize emails as spam or important based on content and user behavior.
11. Gaming: AI agents can play games against human players, offering challenging opponents with advanced strategies.
12. Speech Recognition: AI-powered speech recognition systems convert spoken language into text, used in voice assistants and transcription services.
13. Robotics: AI is used in robotics to enable machines to perform complex tasks, interact with the environment, and learn from experience.
14. Music and Art Generation: AI can compose music, create art, and generate creative content using deep learning algorithms.
15. Security and Surveillance: AI enhances security by analyzing surveillance data, detecting anomalies, and identifying potential threats.

ML vs DL vs AI: Challenges

1. Computing Complexity

ML and DL algorithms require large data to work upon and thus need quick calculations i.e., large processing power is required. However, it came out that limited resources are available to implement these algorithms on large data.

Solution: Cloud computing such as Google Colab, Kaggle, Microsoft Azure brings some hope but as the volume of data goes up, the complexity of algorithms starts increasing and these resources could not help!

2. Lacks support and awareness

Unlike web development and software development, AI is quite a new field and therefore lacks many use-cases which make it difficult for many organizations to invest money in AI-based projects. In other words, there are comparatively fewer data scientists who can make others believe in the power of AI.

Solution: The remedy is to make people aware of the power of AI. Further, instead of building everything from scratch, enabling organizations to take ready-made solutions and just plug and play with data – AI-driven services.

3.  Black-box Nature

AI-based model is black-box in nature which means all data scientists have to do is find and import the right artificial network or machine learning algorithm. However, data scientists lose trust and comfortability because they don’t understand how the model makes decisions.

Solution: One possible solution is making people believe that model really works well. Another possibility that is trending nowadays is “Explainable AI”, letting people know the reason behind the decision.

4. Data breach and Identity Theft

ML and DL algorithms require a large amount of data to learn and thus make informed decisions. However, data often contain sensitive and personal information which makes models susceptible to identity theft and data breach.

Solution: Nowadays, various privacy and security solutions are being provided to protect sensitive information. In addition, the implementation of the General Data Protection Regulation (GDPR) is being done by the European Union to ensure the protection of personal data.

5. Data Sparsity

It is a fact that today data generated is much greater than ever before. But still, there lack datasets with a great density that be used for testing AI algorithms. For instance, the standard dataset used for testing the AI-based recommendation system is 97% sparse.

Solution: Academic and industry researchers have now started working on developing AI models that can work on sparse data without compromising with accuracy.

Conclusion

A clear understanding of the AI ML and DL difference can empower professionals to leverage these technologies effectively.The complexities and opportunities become evident as we delve into ML vs DL vs AI. To harness the full potential of these transformative technologies and stay at the forefront of innovation, equip yourself with in-depth knowledge and hands-on experience. Embrace the transformative power of the Analytics Vidhya BlackBelt Program, curated to master ML, DL, and AI skills. Unlock a world of boundless possibilities, get mentorship from industry experts, and access a vibrant community of learners. Seize the opportunity to elevate your expertise and become a driving force in shaping the future of AI-driven systems. Explore the program today!

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Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.