Companies around the world are using these concepts to build smart, valuable machines that can make life easier.

Artificial Intelligence ( AI ) is a “smart” way to build intelligent machines, Machine Learning ( ML ) is a part of AI that helps build AI-based applications, and Deep Learning ( DL ) is again part of machine learning that trains a model with complex algorithms and vast volumes of data.

They play a vital role in industries that focus on providing unique user experiences.

Since they are related, most people confuse Artificial Intelligence, machine learning, and deep learning. But these terms are not the same.

In this article, you will understand the similarities and differences between these technologies.

So let’s start digging.

AI vs Machine Learning vs Deep Learning: What Are They?

AI, ML, and Deep Learning are somewhat identical, but not in their scope, working procedure, and interchangeability features.

Let’s discuss them one by one to understand what they are and their daily applications in today’s life.

What is Artificial Intelligence (AI)?

You cannot define Intelligence as a set of skills. It is a process of learning new things for yourself intelligently and quickly. A human uses intelligence to learn from education, training, work experiences, and more.

Transferring human intelligence to a machine is what we call artificial intelligence (AI). Many IT industries are using AI to develop automatic development machines that act like humans. AI machines learn from human behavior and perform tasks accordingly to solve complex algorithms.

In simple words, it is developed into a computer system to control other computer systems. In the 1940s the first digital computers emerged, and in the 1950s the possibility of AI appeared.

Nowadays, artificial intelligence is used in weather forecasting, image processing, search engine optimization, medicine, robotics, logistics, online research, etc. Based on current functionality, artificial intelligence is classified into four types:

Scenario: When you talk with Siri or Alexa, you get frequent replies and replies. This is only due to the AI ​​inside the machine. He listens to your words, interprets them, understands them, and responds immediately.

Other applications are autonomous vehicles, AI robots, machine translations, speech recognition, and more.

What is Machine Learning (ML)?

Before digging for machine learning, you need to understand the concept of data mining. Data mining derives actionable insights by using mathematical analysis techniques to uncover trends and patterns within data.

Organizations can use a lot of data to improve machine-learning techniques. ML provides a way to find a new path or algorithm from a data-driven experience. It is the study of techniques that automatically extract data to make trading decisions more carefully.

It helps to design and develop a machine that can grab specific data from the database to give valuable results without using any code. Thus, ML provides a better way to make predictions from the information.

Thus, ML learns data and algorithms to understand how to perform a task. It is the subset of AI.

Scenario: In your everyday life, when you open a platform that you use frequently, such as Instagram, you may see product recommendations. Websites track your behavior based on previous searches or purchases, ML gets the data and shows you products based on the same pattern.

Many industries use ML to detect, correct, and diagnose abnormal application behavior in real time. It has multiple applications in various industries, from small facial recognition applications to large search engine refining industries.

What is deep learning?

If we compare artificial intelligence to human intelligence, then Deep Learning corresponds to the neurons inside a human brain. Rather, it is more complex than machine learning because it uses deep neural networks.

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Here the machines use the technique of multiple layers to learn. The network consists of an input layer to accept data inputs and a hidden layer to find hidden features. Finally, the output layer provides the final information.

In other words, Deep Learning uses a simple technique called sequential learning. Many industries use the technique of Deep Learning to create new ideas and new products. Deep Learning differs from Machine Learning in terms of impact and scope.

AI is the present and the future of our growing world. Deep Learning enables practical applications by extending the global use of AI. Thanks to Deep Learning, many complex tasks seem possible, such as driverless cars, better movie recommendations, healthcare, and more.

Scenario: When you think of a driverless car, you have to ask yourself how it drives on the road without human assistance. Deep Learning provides human-like expertise in understanding road structure, pedestrians, speed limits in various scenarios, and more.

With big data and efficient computation, a car drives by itself, which means it has a better decision-making flow.

Difference Between AI, Machine Learning, And Deep Learning vs Machine Learning vs Deep Learning: How They Work?

Now you know what AI, ML, and Deep Learning are individually. Let’s compare them according to how they work.

How does AI work?

Think of artificial intelligence as a way to solve problems, answer questions, suggest something, or predict something.

Systems using AI concepts work by consolidating large datasets with iterative, intelligent algorithms and analyzing the data to learn characteristics and patterns. It continues to test and determine its performance by processing data and makes it smarter to develop more expertise.

AI systems can perform thousands and millions of tasks at incredible speeds without requiring a break. Therefore, they quickly learn to be able to perform a task effectively. AI aims to create computer systems that mimic human behavior to think like humans and solve complex questions.

To do this, AI systems rely on various processes, techniques, and technologies. Here are the different components of AI systems:

  • Neural Networks: It is like a large neural network found in the human brain. It allows AI systems to use large datasets, analyze them to find patterns, and solve problems.
  • Cognitive Computing: It mimics the way the human brain thinks while performing tasks to facilitate communication between machines and humans.
  • Machine Learning: It is a subset of AI that enables computer systems, applications, and programs to automatically learn and develop results based on experience. It allows AI to detect patterns and reveal insights from data to improve results.
  • Deep learning: This is a subset of machine learning that allows AI to process data and learn and improve using AI neural networks.
  • Computer vision: AI systems can analyze and interpret image content through deep learning and pattern recognition. Computer vision allows AI systems to identify components of visual data.

For example, captchas learn by asking you to identify bicycles, cars, traffic lights, etc.

  • Natural Processing Language (NPL): It allows systems to recognize, analyze, interpret, and learn a human language in spoken and written form. It is used in systems that communicate with humans.

So, for an AI system to work, it must have all of these capabilities. Along with this, AI systems require certain technologies:

  • Larger and more accessible datasets as artificial intelligence feed on them
  • Intelligent data processing through advanced algorithms to analyze data at simultaneous speeds understand complex issues and predict events.
  • Application Programming Interfaces (APIs) to add AI functions to a system or application and make it smarter.
  • Graphics processing units (GPUs) power AI systems to perform heavy calculations for data processing and interpretation.

How does machine learning work?

Machine learning uses large amounts of data using various techniques and algorithms to analyze, learn, and predict the future. It involves a lot of complex coding and math that performs a mathematical function.

He explores the data and identifies patterns to learn and improve based on his previous experiences. It teaches AI systems to think like humans. Machine learning helps automate tasks that are performed with a set of data-defined rules and patterns. In this way, companies can use AI systems to perform tasks at high speed. ML uses two main techniques:

  • Unsupervised learning: It helps to find known patterns in collected data
  • Supervised teaching: It allows data collection or produces output from previous ML deployments.
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How does deep learning work?

It starts with designing a deep learning model to continuously observe and analyze data involving a logical structure like how humans conclude.

To make this analysis complete, deep learning systems use a layered algorithmic structure known as an artificial neural network that can mimic the human brain. This allows systems to be more capable of performing tasks than traditional systems.

However, a deep learning model needs to be continuously trained to evolve and improve its capabilities so that it can draw correct conclusions.

AI vs Machine Learning vs Deep Learning: Applications

To fully understand how AI, ML, and deep learning work, it is important to know how and where they are applied.

AI systems are used for various purposes such as reasoning and problem-solving, planning, learning, knowledge presentation, natural language processing, general intelligence, social intelligence, perception, etc.

For example, AI is used in online advertisements, search engines like Google, etc.

Let’s look at it in detail.

Internet, e-commerce, and marketing

  • Search Engines: Search engines like Google use AI to display results.
  • Recommendation systems: It is also used by recommendation systems such as YouTube, Netflix, and Amazon to recommend content based on user preferences or ratings.

AI is used to generate playlists, display videos, recommend products and services, etc.

  • Social Media: Sites like Facebook, Instagram, Twitter, etc. use AI to display relevant posts you can interact with, automatically translate languages, remove hateful content, and more.
  • Ads: AI is leveraged for targeted web advertisements to persuade people to click on ads and increase their time spent on sites by displaying subtractive content. AI can predict personalized offers and customer behavior by analyzing their digital signatures.
  • Chatbots: Chatbots are used to control devices, communicate with customers, etc.

For example, Amazon Echo can translate human speech into appropriate actions.

  • Virtual Assistants: Virtual assistants such as Amazon Alexa use AI to process natural language and help users with queries.
  • Translation: AI can automatically translate text documents and spoken languages.

Example: Google Translate.

Other use cases include spam filtering, image tagging, facial recognition, and more.

Gaming

The gaming industry uses AI heavily to produce advanced video games, some of which have superhuman abilities.

Scenario: Deep Blue and AlphaGo chess type. The latter once beat Lee Sedol, who is a Go world champion.

Socioeconomic

AI is being leveraged to address social and economic challenges such as homelessness, poverty, etc.

Background: Researchers at Stanford University used AI to identify areas of poverty by analyzing satellite images.

cyber security

By embracing AI and its ML and Deep Learning subdomains, security companies can create solutions to protect systems, networks, applications, and data. It is applied for:

  • Application security to counter attacks such as cross-site scripting, SQL Injection, server-side tampering, distributed denial of service, etc.
  • Network protection by identifying more attacks and improving intrusion detection systems
  • Analyze user behavior to identify compromised applications, risks, and fraud
  • Endpoint protection by learning common threat behaviors and defeating them to prevent attacks such as ransomware.

Agriculture

AI, ML, and deep learning are useful to agriculture to identify areas that need irrigation, fertilization, and treatments to increase yield. It can help agronomists research and predict crop ripening time, monitor soil moisture, automate greenhouses, detect pests, and operate agricultural machinery.

Finance

Artificial neural networks are used in financial institutions to detect out-of-standard claims and charges and investigative activities.

Banks can use AI to prevent fraud counter the misuse of debit cards, organize operations such as accounting, manage properties, invest in stocks, monitor behaviors, and react immediately to changes. AI is also used in e-commerce applications.

Scenario: Zest Automated Machine Learning (ZAML) from ZestFinance is a credit underwriting platform. It uses AI and ML for data analysis and assigns credit scores to people.

Education

AI tutors can help students learn while eliminating stress and anxiety. It can also help educators predict behavior early in a Virtual Learning Environment (VLE) like Moodle. It is especially beneficial during scenarios like the current pandemic.

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Health care

AI is applied in healthcare to evaluate an electrocardiogram or CT scan to identify health risks in patients. It also helps regulate the dosage and choose the most appropriate treatments for diseases like cancer.

Artificial neural networks support clinical decisions for medical diagnosis, for example, concept processing technology used in EMR software. AI can also help to:

  • Analysis of medical records
  • Medication management
  • Treatment planning
  • Consultation
  • Clinical training
  • Create drugs
  • Predict results

Use case: Microsoft’s Hanover AI project helps doctors choose the most effective cancer treatment from more than 800 vaccines and drugs.

Governmental

Government organizations in countries like China are using AI for mass surveillance. Likewise, it can also be used to manage traffic lights using cameras for traffic density monitoring and signal timing adjustment.

For example, in India, AI-driven road signs are being deployed to clear and manage traffic in the city of Bengaluru.

Additionally, many countries are using AI in their military applications to improve communications, command, controls, sensors, interoperability, and integration. It is also used to collect and analyze intelligence, logistics, autonomous vehicles, cyber operations, etc.

Other applications of AI are in:

  • Space exploration to analyze vast data for research
  • Biochemistry to determine the 3D structure of proteins
  • Content creation and automation.

Background: Wordsmith is a platform for generating natural language and turning data into meaningful information.

  • Automate law-related tasks and research,
  • Occupational safety and health management
  • Human resources to filter and classify resumes
  • Job search by evaluating job skills and salary data
  • Customer service with virtual assistants
  • Hospitality to automate tasks, communicate with customers, analyze trends, and predict consumer needs.
  • Manufacture of automobiles, sensors, games, toys, etc.

AI vs Machine Learning vs Deep Learning: Differences

Artificial intelligence, machine learning, and deep learning are interrelated with each other. Deep learning is a subset of machine learning, and machine learning is a subset of artificial intelligence.

So it’s not a question of “difference” here, but the scope to which they can be applied.

Let’s see how they differ.

Artificial Intelligence vs Machine Learning

SettingAI ML
ConceptIt is a broader concept for creating intelligent machines to simulate human thinking and behavior.It is a subset of artificial intelligence to help machines learn by analyzing data without explicit programming.
GoalIt aims to create smarter systems with human thinking skills to solve complex questions.
He is concerned about increasing success rates.
It aims to allow machines to analyze the data to provide an accurate output.
He is concerned with models and accuracy
What they’re doingAI allows a system to be able to perform tasks as a human would, but without errors and at a faster speed.Machines are constantly learning how to improve and perform a task so that they can provide more precision.
SubsetsIts subsets are deep learning and machine learning.Its subset is deep learning
Types of dryerIt is of three types – General AI, Strong AI, and Weak AIIts types are reinforcement learning, supervised and unsupervised
ProcessThis includes reasoning, learning, and self-correctionIt includes learning as well as self-correction for new data
Data TypesIt processes unstructured, semi-structured, and structured dataIt processes semi-structured and structured data
ScopeIts scope is wider.
AI systems can multi-task instead of ML which is trained for specific tasks.
Its range is limited compared to AI.
ML machines perform specific tasks for which they are trained
UsagesIts applications are chatbots, robots, recommendation systems, games, social media, and many more.The main applications are online recommendations, Facebook friend suggestions, Google searches, etc.

Machine Learning vs Deep Learning

SettingMLdeep learning
Data dependencyAlthough ML works on huge volumes of data, it also accepts smaller volumes of data.Its algorithms perform very well on large volumes of data. Therefore, if you want more accuracy, you need to provide more data and allow it to learn continuously.
Execution timeIts algorithms require less training time than DL but take longer for model testing.It takes more time for model training but less time for model testing.
Hardware dependencyML models essentially don’t need a lot of data; therefore, they run on low-end machines.DL models require huge data for efficient work; therefore, they are only suitable for high-end machines with GPUs.
Feature EngineeringML models require you to develop a feature extractor for each problem to drill down.Since DL is an advanced form of ML, it doesn’t require feature extractors for problems. Instead, DL itself learns high-level features and insights from collected data.
Problem-solvingTraditional ML models divide a problem into smaller parts and solve each part separately. Once it has solved all the parts, it generates the final result.DL models take the end-to-end approach to solving a problem by taking the inputs for a given problem.
Results interpretationIt is easy to interpret the results of a problem using ML models along with the full process and reason analysis.It can be difficult to analyze the results of a problem with DL models. Although you may get better results for a problem with DL than traditional ML, you cannot find out why and how the result came out.
DataIt requires structured and semi-structured data.It requires both structured and unstructured data as it relies on artificial neural networks.
Best forSuitable for solving simple and not very complex problems.Suitable for solving complex problems.

Conclusion

Artificial intelligence, machine learning, and deep learning are modern techniques for creating intelligent machines and solving complex problems. They are used everywhere, from businesses to homes, making life easier. 

DL falls under ML, and ML falls under AI, so it’s not really about the difference here, but the scope of each technology.

 

FAQs

Question 1: What is the key difference between AI, machine learning, and deep learning?
Answer 1: AI refers to the general concept of machines performing tasks intelligently, while machine learning is a subset of AI where algorithms learn from data without explicit programming, and deep learning is a subset of machine learning using artificial neural networks.

Question 2: How do AI, machine learning, and deep learning relate to each other?
Answer 2: AI encompasses both machine learning and deep learning. Machine learning uses algorithms to learn from data, while deep learning utilizes artificial neural networks to train models for complex tasks like image or speech recognition.

Question 3: Can you explain the working principle of machine learning?
Answer 3: In machine learning, algorithms analyze data, identify patterns, and learn from them to make predictions or decisions without being explicitly programmed. It focuses on improving performance by learning from examples and iteratively adjusting models.

Question 4: What sets deep learning apart from traditional machine learning?
Answer 4: Deep learning stands out for its use of deep neural networks with multiple layers that can automatically learn hierarchical representations. While traditional machine learning requires manual feature extraction, deep learning can learn features directly from raw data, resulting in more powerful and flexible models.

Question 5: How is AI different from machine learning and deep learning?
Answer 5: AI is the broader concept of machines imitating human intelligence, which includes machine learning and deep learning as specific approaches. While AI covers a wide range of techniques, machine learning and deep learning focus on specific strategies for machines to learn and make