Artificial Intelligence, machine learning, and deep learning have taken the modern world by storm.
Businesses across the globe are using these concepts to build smart, valuable machines that can ease lives.
Artificial Intelligence (AI) is a “smart” way to create intelligent machines, machine learning (ML) is a part of AI that helps in building AI-driven applications, and Deep Learning (DL) again is a part of machine learning that trains a model with complex algorithms and vast data volumes.
They play a vital role in the industries focusing on providing unique experiences to the users.
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 the same but not in their scope, working procedure, and their interchangeability functionality.
Let’s discuss them one by one to understand what they are and their day-to-day applications in present lives.
What is Artificial Intelligence (AI)?
You cannot define Intelligence as a skill set. It is a process of learning new things on your own with smartness and speed. 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 use AI to develop self-developing machines that act like humans. AI machines learn from human behavior and perform tasks accordingly to solve complex algorithms.
In simple terms, it is developed in a computer system to control other computer systems. In the 1940s, the first digital computers came into existence, and in the 1950s, the possibility of AI came into existence.
Nowadays, artificial intelligence is used in weather prediction, image processing, search engine optimization, medicine, robotics, logistics, online search, and more. Based on the current functionality, artificial intelligence is classified into four types:
- Reactive Machines AI
- Limited Memory AI
- Theory of Mind AI
- Self-Aware AI
Example: When you talk with Siri or Alexa, you get frequent answers and responses. This is only due to AI inside the machine. It listens to your words, interprets them, understands them, and responds immediately.
Other applications are self-driving vehicles, AI robots, machine translations, speech recognition, and more.
What is Machine Learning (ML)?
Before digging for Machine Learning, you must understand the concept of data mining. Data mining derives actionable information by using mathematical analysis techniques to discover trends and patterns inside the data.
Organizations can use lots of data to improve machine learning techniques. ML provides a way to find a new path or algorithm from data-based experience. It is the study of the technique that extracts data automatically to make business decisions more carefully.
It helps in designing and developing a machine that can grasp specific data from the database to give valuable results without using any code. Thus, ML gives a better way to make predictions from the insights.
So, ML learns from the data and algorithms to understand how to perform a task. It is the subset of AI.
Example: In your day-to-day life, when you open any platform which you frequently use, like Instagram, you can see product recommendations. Websites track your behavior based on the previous search or purchase, ML gets the data and shows you products based on the same pattern.
Many industries use ML to detect, remediate, and diagnose anomalous application behavior in real-time. It has multiple applications in various industries starting from small face recognition applications to big search engine refining industries.
What is Deep Learning
If we are comparing artificial intelligence with human intelligence, then Deep Learning is the neurons inside a human brain. It is rather more complex than machine learning as it uses deep neural networks.
Here, the machines use the technique of several layers to learn. The network consists of an input layer to accept inputs from data and a hidden layer to find the hidden features. At last, the output layer provides the final information.
In other words, Deep Learning uses a simple technique called sequence learning. Many industries use the Deep Learning technique to build new ideas and products. Deep Learning differs from Machine Learning in terms of impact and scope.
AI is the present and future of our growing world. Deep Learning enables practical applications by extending the overall use of AI. Due to Deep Learning, many complex tasks seem possible, such as driverless cars, better movie recommendations, healthcare, and more.
Example: When you think of a driverless car, you must be wondering about how it drives on the road without human assistance. Deep Learning provides human-like expertise in understanding the road structure, pedestrians, speed limits in various scenarios, and more.
With the large data and efficient calculation, a car drives on its own, which means it has a better decision-making flow.
AI vs Machine Learning vs Deep Learning: How They Work?
Now, you know what AI, ML, and Deep Learning are individually. Let’s compare them based on 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 data sets with iterative and intelligent algorithms and analyzing the data to learn features and patterns. It keeps on testing and determining its own performance by processing data and makes it smarter to develop more expertise.
AI systems can run thousands and millions of tasks at incredible speeds without requiring a break. Therefore, they learn quickly to be capable of accomplishing a task efficiently. AI aims at creating computer systems mimicking human behavior to think like humans and solve complex questions.
To do this, AI systems leverage various processes, techniques, and technologies. Here are different components of AI systems:
- Neural networks: It’s like a large network of neurons found in human’s brains. It allows AI systems to use large sets of data, analyze them to find patterns, and solve problems.
- Cognitive computing: It imitates 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 allows computer systems, applications, and programs to automatically learn and develop experience-based results. It enables AI to detect patterns and reveal insights from the data to enhance results.
- Deep learning: It is a subset of machine learning that enables AI to process data and learn and improve by 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 visual data’s components.
For example, captchas learn by asking you to identify bicycles, cars, traffic lights, etc.
- Natural Processing Language (NLP): It allows systems to recognize, analyze, interpret, and learn human language in spoken and written form. It is used in systems that communicate with humans.
So, in order for an AI system to work, it must have all these capabilities. Along with these, AI systems requires some technologies:
- Larger, accessible data sets since AI thrives on it
- Intelligent data processing through advanced algorithms to analyze data at speeds simultaneously and understand complex problems and predict events.
- Application Programming Interfaces (APIs) to add AI functions to a system or application and make them smarter.
- Graphical Processing Units (GPUs) to provide power to AI systems to perform heavy computations to data processing and interpretation.
How Does Machine Learning Work?
Machine learning uses a large amount of data by using various techniques and algorithms to analyze, learn, and predict the future. It involves lots of complex coding and maths that serve some mathematical function.
It explores data and identifies patterns in order to learn and improve based on its previous experiences. It teaches AI systems to think as humans do. Machine learning helps automate tasks that are completed with a set of rules and data-defined patterns. This way, businesses can use AI systems to perform tasks at speeds. ML uses two primary techniques:
- Unsupervised learning: It helps find known patterns in gathered data
- Supervised learning: It enables data collection or produces output from past ML deployments.
How Does Deep Learning Work?
It starts by designing a deep learning model to continually observe and analyze data involving a logical structure like the way humans draw conclusions.
For this analysis to complete, deep learning systems utilize a layered algorithmic structure known as an artificial neural network that can mimic the human brain. This enables the systems to be more capable at performing tasks than traditional systems.
However, a deep learning model must continuously be trained to evolve and enhance its capabilities so that it can draw correct conclusions.
AI vs Machine Learning vs Deep Learning: Applications
To completely understand how AI, ML, and deep learning work, it’s 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, and more.
For example, AI is used in online advertisements, search engines like Google, etc.
Let’s look at it in detail.
Internet, eCommerce, and Marketing
- Search engines: Search engines such as 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 preference or ratings.
AI is used to generate playlists, show videos, recommend products and services, and more.
- Social media: Sites like Facebook, Instagram, Twitter, etc. use AI to show relevant posts you can engage with, automatically translate languages, remove hateful content, etc.
- Ads: AI is leveraged for targeted web advertisements to persuade people to click on the ads and increase their time spent on sites by displaying attractive content. AI can predict personalized offers and customer behavior by analysing their digital signatures.
- Chatbots: Chatbots are used to control appliances, communicate with customers, etc.
For example, Amazon Echo can translate human speech into suitable actions.
- Virtual assistants: Virtual assistants such as Amazon Alexa use AI to process natural language and help users with their queries.
- Translation: AI can automatically translate textual documents and spoken languages.
Example: Google Translate.
Other use cases include spam filtering, image labeling, facial recognition, and more.
Gaming
The gaming industry uses AI heavily to produce advanced video games, including some of them with superhuman capabilities.
Example: Chess-like Deep Blue and AlphaGo. The latter once defeated Lee Sedol, who is a world champion in GO.
Socio-Economical
AI is being leveraged to address social and economic challenges like homelessness, poverty, etc.
Example: Researchers at Stanford University utilized AI to identify poverty areas by analyzing satellite images.
Cybersecurity
Adopting AI and its subfields ML and deep learning, security companies can create solutions to safeguard systems, networks, applications, and data. It’s applied for:
- Application security to counter attacks like cross-site scripting, SQL injection, server-side forgery, distributed denial of service, etc.
- Network protection by identifying more attacks and improve intrusion detection systems
- Analyze user behavior to identify compromised apps, risks, and frauds
- Endpoint protection by learning common threat behavior and thwart them to prevent attacks like ransomware.
Agriculture
AI, ML, and deep learning are helpful for agriculture to identify areas requiring irrigation, fertilization, and treatments to increase yield. It can help agronomists carry out research and predict crop ripening time, monitor moisture in the soil, automate greenhouses, detect pests, and operate agricultural machines.
Finance
Artificial neural networks are used in financial institutions to detect claims and charges outside the norm and the activities for investigation.
Banks can use AI for fraud prevention to counter debit card misuse, organize operations like bookkeeping, manage properties, invest in stocks, monitor behavioral patterns, and react immediately to changes. AI is also used in online trading apps.
Example: Zest Automated Machine Learning (ZAML) by ZestFinance is a platform for credit underwriting. It uses AI and ML for data analysis and assigns people credit scores.
Education
AI tutors can help students learn while eliminating stress and anxiety. It can also help educators to predict behavior early in a virtual learning environment (VLE) like Moodle. It is especially beneficial during scenarios like the current pandemic.
Healthcare
AI is applied in healthcare to evaluate an electrocardiogram or CT scan to identify health risks in patients. It also helps regulate dosing and choose the most suitable treatments for diseases like cancer.
Artificial neural networks support clinical decisions for medical diagnosis, for instance, concept processing technology used in EMR software. AI can also help in:
- Analyzing medical records
- Medication management
- Planning treatments
- Consultation
- Clinical training
- Creating drugs
- Predicting outcomes
Use case: Hanover AI project by Microsoft helps doctors choose the most effective cancer treatment from 800+ vaccines and medicines.
Government
Government organizations from countries like China use AI for mass surveillance. Similarly, it can also be used for managing traffic signals by using cameras for traffic density monitoring and signal timing adjustment.
For example, in India, AI-managed traffic signaling is deployed to clear and manage traffic in its city of Bengaluru.
Furthermore, many countries are using AI in their military applications to improve communications, command, controls, sensors, interoperability, and integration. It’s also used in collecting and analyzing intelligence, logistics, autonomous vehicles, cyber operations, and more.
Other applications of AI are in:
- Space exploration to analyze vast data for research
- Biochemistry to determine proteins’ 3D structure
- Content creation and automation.
Example: Wordsmith is a platform to generate natural language and transfer data into meaningful insights.
- Automate law-related tasks and search,
- Workplace safety and health management
- Human resources to screen and rank resumes
- Job search by evaluating data related to job skills and salaries
- Customer service with virtual assistants
- Hospitality to automate tasks, communicate with guests, analyze trends, and predict consumer needs.
- Manufacturing of automobiles, sensors, games and toys, and more
AI vs Machine Learning vs Deep Learning: Differences
Artificial intelligence, machine learning, and deep learning correlate with one another. In fact, deep learning is a subset of machine learning, and machine learning is a subset of artificial intelligence.
So, it’s not a matter of really “difference” here, but the scope at which they can be applied.
Let’s look at how they differ.
Artificial Intelligence vs. Machine Learning
| Parameter | AI | ML | |
| Concept | It’s a bigger concept for creating smart machines to simulate human thinking and behavior. | It is a subset of artificial intelligence to help machines learn by analyzing data without explicit programming. | |
| Aim | It aims at creating smarter systems with human-like thinking skills to solve complex questions. It is concerned about increasing success rates. | It aims at allowing machines for data analysis in order to provide accurate output. It is concerned about patterns and accuracy | |
| What they do | AI enables a system to be able to perform tasks like a human would do but without errors and at a faster speed. | Machines are taught continuously to improve and perform a task so that it can provide more accuracy. | |
| Subsets | Its subsets are deep learning and machine learning. | Its subset is deep learningTypesIt is of three types – General AI, Strong AI, and Weak AIIts types are reinforcement learning, supervised, and unsupervisedProcessIt includes reasoning, learning, and self-correctionIt includes learning as well as self-correction for new dataTypes of dataIt deals with unstructured, semi-structured, and structured dataIts deals with semi-structured and structured dataScopeIts scope is wider. AI systems can perform several tasks instead of ML that is trained for specific tasks. | Its scope is limited compared to AI. ML machines perform specific tasks that they are trained for |
| Application | Its applications are chatbots, robots, recommendation systems, games, social media, and many more. | Primary applications are online recommendations, Facebook friend suggestions, Google searching, etc. |
Machine Learning vs. Deep Learning
| Parameter | ML | Deep learning |
| Data dependency | Although ML works on huge data volumes, it also accepts smaller data volumes. | Its algorithms highly work on large data volumes. Hence, if you want to get more accuracy, you must supply more data and allow it to learn continuously. |
| Execution time | Its algorithms require less training time than DL but take longer for model testing. | It takes longer for model training but less longer for model testing. |
| Hardware dependency | ML models don’t need much data essentially; hence, they work on low-end machines. | DL models require huge data for efficient work; hence, they are suitable for only high-end machines with GPUs. |
| Feature engineering | ML models require you to develop a feature extractor for every problem to proceed further. | Since DL is an advanced form of ML, it doesn’t require feature extractors for problems. Instead, DL learns high-level features and insights from collected data by itself. |
| Problem-solving | Traditional ML models break a problem into smaller parts and solve each part separately. Once it solves all the parts, it generates the final result. | DL models take the end-to-end approach to solve a problem by taking the inputs for a given problem. |
| Result interpretation | It’s easy to interpret the results of a problem using ML models along with the complete analysis of the process and reasons. | It can be tricky 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 why and how the result came out. |
| Data | It requires structured and semi-structured data. | It requires both structured and unstructured data as it relies on artificial neural networks. |
| Best for | Suitable to solve simple and bit-complex problems. | Suitable to solve complex problems. |
Conclusion
Artificial intelligence, machine learning, and deep learning are modern techniques to create smart machines and solve complex problems. They are used everywhere, from businesses to homes, making life easier.
DL comes under ML, and ML comes under AI, so it’s not really a matter of difference here, but the scope of each technology.