Artificial Intelligence and Machine Learning have moved beyond the hype cycle. Today, organizations of every size are integrating these technologies into core business operations, from customer service automation to strategic decision-making.
For business leaders, the key distinction matters: AI focuses on enabling machines to make intelligent decisions, while ML enables systems to improve performance through experience and data.
Consider a virtual receptionist that operates 24/7, manages appointments, handles client inquiries, and delivers consistent service quality. This represents how AI agents can transform operational efficiency and workflow optimization.
But here's the question at the heart of the Artificial Intelligence vs Machine Learning debate: Are AI and ML truly different technologies, or complementary components of the same digital transformation toolkit?
Artificial Intelligence vs Machine Learning: What Are They?
A Sneak Peek of Artificial Intelligence
According to Alex Zu, Co-founder and Author of ByteByteGo
"Artificial Intelligence is the uppermost layer where machines learn to perform tasks that humans have been doing for years. These include reasoning, learning, and problem-solving skills.”
AI consists of different fields, and Machine Learning is a subset of that.
Today, Artificial intelligence is present everywhere, from voice assistants like Siri and Alexa to chatbots such as ChatGPT, Bard, etc.
This intelligent system powers self-driving cars, production scheduling systems, and smart assistants.
Large language models such as ChatGPT, Claude, and Bard are the newest types of AI that use machine learning capabilities to perform tasks like translation, text generation, summarization, etc.
Best part? AI systems can work to perform narrow and specialized tasks. Studies say that more than 78% of companies have integrated AI in one way or another. This means its usage is expected to rise in the coming years.
Then, What Does Machine Learning Mean?
Machine learning is a subset of AI wherein machines learn from data, identifying patterns and trends to make predictions without being programmed for specific tasks. It uses historical data as the input set, figuring out trends and relationships to develop new outputs.
The more high-quality data models that have been trained on, the more accurate predictions they will make. The systems will improve based on the experiences they learn along the way.
Unlike Artificial Intelligence, ML learns from data to make pricing predictions and group customers with similar traits for marketing campaigns.
The Netflix recommendation engine is an example of a machine learning algorithm that provides you with personalized movie and series suggestions based on your watch history and past preferences.
Here, the model will perform a better job and improve its performance over time. For example, if you ask the model to recognize a car image, you will provide a couple of images of cars, and it will learn to recognize what a car is and how it looks.
The common use cases of Machine Learning include fraud detection systems, recommendation engines, and predictive analytics.
Is Artificial Intelligence or Machine Learning the same?
Al and ML are closely related to each other, but they are not the same thing. AI's objective is to give computers the capabilities that humans can do.
Machine learning deals with developing models from which computers can learn, analyze data, and make predictions about the world. Consider AI as the toolbox, the brain of the system, within which ML is a tool that takes care of logic and decision-making.
“According to Microsoft, AI is when a computer uses AI technology to think like humans and perform complex tasks. At the same time, ML relates to how a computer system develops its intelligence.
” AI and ML Have a Strong Relationship With Each Other:
- AI and ML focus on building intelligent programs that perform complex tasks and help businesses take over human tasks and finish them quickly.
- AI is an umbrella term for ML, and ML is a subset of AI/ a category of AI. ML is used for various AI apps and even makes AI better.
- Both AI and ML can learn and change over time. They aren't stagnant. AI follows a specific set of rules to handle a certain number of tasks. On the contrary, machine learning learns independently, analyses the data, and predicts a similar range of patterns in the future.
- Artificial intelligence and Machine learning can automate tasks, but you need to include humans in the loop. Just as AI is used for making decisions, ML is used to do data analysis.
- Both AI and ML systems learn from data, and they improve their performance over time as they're exposed to more data.
- Artificial Intelligence and machine learning require more computational resources such as GPUs, for performing complex tasks.
AI > makes decisions
ML > Learn from data to make decisions |
Differences Between Artificial Intelligence(AI) and Machine learning(ML)
Basis of comparison | Artificial Intelligence | Machine learning |
Meaning | AI refers to the technology that brings intelligent capabilities to machines to perform tasks more efficiently. In addition, AI cannot learn from its mistakes. | ML, the subset of AI, allows a machine to learn from past data. |
Flexibility | AI does not rely on datasets. It’s more flexible than ML | ML models are particularly used for doing predictive analytics |
Type of data | AI deals with structured, semi-structured, and unstructured data | Machine learning involves dealing with structured and semi-structured data |
Applications | There are various applications of AI, including Siri, providing customer support through chatbots, machine translation such as Google translate, etc. | Image recognition, speech recognition, self-driving cars, virtual try on etc., are some applications of ML |
Decision making | AI strives to create a system that mimics human behavior | ML is dependent on AI. It creates algorithms that redefine the AI model. |
Goal | The goal of AI is to create systems that possess intelligence that is human-like. | Machine learning focuses on finding data patterns to make future predictions. The more data it is exposed to, the more enhanced the system performance will be. |
Human intervention | There is minimal human intervention involved. | It requires human involvement in training the data models. |
Examples | Analysis of patient records and medical information, such as x-rays, spotting anomalies and gaps. These are the same tasks that doctors used to perform earlier. | Netflix's recommendation engine uses ML algorithms to identify what types of movies and genres users love the most. It then suggests similar movies based on what they liked. |
Benefits of Artificial Intelligence and Machine Learning
1) Reduces Human Errors
Human errors start when humans make mistakes from time to time. In comparison, AI reduces human errors as decisions are already made from previously gathered information. Hence, errors are disappearing because machines are trained to provide accurate results.
For Instance, Robots can perform complex surgical procedures with 100% accuracy. Under this, AI provides surgeons with a new set of eyes, where AI is trained on large volumes of data and acts as a learning tool for surgeons.
2) Works 24*7
A report from CNBC suggests that 67% of workers who spend too much time in their meetings are usually distracted from doing their job.
Various research studies indicate that humans produce up to 3-4 hrs./day. They need breaks to refresh themselves and balance their personal and professional lives.
But AI can perform multiple tasks quickly and does not need any breaks.
For Instance, AI chatbots provide round-the-clock support to customers and are there to answer complex queries about their problems.
3) Automate Repetitive Tasks
AI helps businesses perform repetitive jobs that require little or no creativity. Using AI, businesses automate mundane tasks that free up human staff and let them perform other important tasks which require their participation.
For Instance, AI can perform repetitive tasks such as automated data entry, invoice processing, software testing, visual quality inspection, and providing customer support, etc. Thus, it allows humans to focus on higher-level tasks.
Here Are Some of The Benefits of ML:
1) Helps in Forecasting Demand
Companies are under a burden to analyze current trends and estimate demand in the future, but integrating machine learning models in their AI systems can help businesses to analyze customer behaviors, predict market trends, etc.
For Instance, ML is a lifesaver for e-commerce websites such as Amazon.
Amazon uses ML to analyze customers' browsing behavior and purchase history, and then comes up withan offering of the right set of products and lucrative deals to customers. Thus, Amazon can increase its sales and boost its revenue.
2) Operational Efficiency Growth
Machine learning possesses the capability of automating repetitive tasks. Using ML, chatbots work around the clock, and they always stay energized as they can analyze massive volumes of data without burning out.
3) Detects Fraud
Detecting fraud plays a pivotal role for businesses, especially banks.
Businesses can use machine-learning algorithms to identify potential fraud, like insurance fraud, credit card fraud, etc. ML sends alerts to the bank if someone tries to cheat the banking system or if the person is not authentic.
For Instance, Mastercard uses AI and ML technology to track various processes such as location, time, transaction size, purchase data, etc. The system evaluates account behavior to determine whether the transaction is fraudulent.
What Types of Businesses Can Use AI and ML?
Companies are using AI and ML in every industry, ranging from healthcare and business to supply chains and manufacturing. These are as given below:
1) Healthcare
Artificial intelligence is a game changer for healthcare professionals, such as doctors and healthcare staff.
Every day, the healthcare sector produces 137 terabytes of data, most of which is unstructured, meaning it's challenging for medical staff, such as surgeons, to manage such huge amounts of data.
They need to manage such extensive healthcare data and, at the same time, focus on providing a better patient experience.
Here's how AI in the healthcare industry offers multiple benefits -
- Physicians used to take years to analyze data; Artificial intelligence can take a few minutes to provide insights from electronic healthcare records.
- Calculates the number of visits each patient is likely to have.
- Provides personalized medical treatment plans
- Records patient and healthcare provider conversations and generates notes
- Reduces the cost of administrative expenses
2) Manufacturing
The use of AI and ML technologies can automate manufacturing processes. These include:
- Predicting equipment failure before its occurrence
- Telling factory workers when machines need to undergo maintenance
- Saving energy usage by studying HVAC energy optimization patterns
3) Banking Processes
In the banking and financial industry, keeping customer data safe always remains at its heart.
Through AI, Banks can improve their operational efficiency and protect customers' financial information in the following ways -
- Automate 80% of repetitive processes, allowing knowledge workers to focus on high-end tasks through robotic process automation.
- Banks can detect cyberattacks as they can quickly verify identities and process documents at scale.
- The use of chatbots and virtual assistants can automate basic repetitive queries and ease the job of customer support agents.
4) Supply Chain
Supply chains are quite complex as different people are involved, from sourcing raw materials to shipping goods to distribution centers.
Artificial intelligence improves supply chain processes in the following ways:
- Digital assistants or copilots can automate communication processes such as automatically responding to supplier messages, sharing delivery updates, and confirming orders.
- As ML algorithms can analyze multiple data points from various sources, they can predict issues of late shipment if that might be the case.
- Help you manage inventory in real time, track orders, and alert the team about delays in shipment.
When Should You Use AI and ML?
The usage of AI and ML boils down to various factors such as what problem you want to solve, what level of automation you want, and the type of data that’s available.
- Go with AI if you want to create rule-based systems like chatbots.
- An application that can mimic intelligent behavior, such as problem-solving skills, then AI AI-based approach is the feasible option.
- Create systems that can make data predictions (recommendation engine, fraud detection systems), then you need Machine learning.
How Can BigOhTech Help You Build AI/ML Systems?
Though Artificial Intelligence and Machine learning sound the same, they're different in every aspect. AI can give you a bigger picture of how to create smart systems that can think and reason like humans.
Machine learning, on the other hand, can learn from data and make decisions based on experience, just like a chef can cook food by following recipes.
As an app development company, we use AI/ML technologies to create smart systems such as ChatGPT solutions, recommendation engines, fraud detection systems, etc. Whether you're looking to develop custom AI software or an enterprise AI solution, we can help you with that.
Best part? We helped a hospitality client who needed help with handling guest queries on WhatsApp. They wanted an AI-powered bot to automate their responses and at the same time provide a personalized experience for guests.
This WhatsApp bot can identify user intent and assist clients with hotel bookings and food order requests.
Result? Reduced waiting time, freed-up human staff, and a better guest experience.
Need help building a similar AI system for your business?
FAQs
Q1. What is the main difference between AI and Machine Learning? AI deals with developing intelligent programs that mimic human behavior and can perform human-related tasks. Machine learning involves creating algorithms to learn from past data and make future predictions. Deep Learning is a subset of ML that involves using neural networks to perform complex tasks such as natural language recognition and image processing.
Q2. How does Machine Learning contribute to Artificial Intelligence? Machine learning acts as the pathway to AI. ML is a subset of AI. Machine learning is related to how machines develop their intelligence.
Q3. What is the future of AI and Machine Learning? The future of AI looks promising. In the coming few years, AI will be used in a wide array of industries, such as –
1. India is the second most populated country after China. There is a lack of healthcare facilities and a lack of availability of doctors, so AI is there to detect human diseases based on symptoms, even if you don’t go to doctors.
2. AI is going to transform the education system. There is no need for skilled labourers involved in manufacturing industries, as everything is automated through robots.
3. Research studies predicted that AI would be used in cybersecurity, which would identify the origin of cyberattacks.
On the contrary, Machine learning also helps various industries to make informed decisions in the future-
1. Computer vision is a significant ML advancement that allows computers to identify objects in videos and images. As advancements are going on in machine learning, the error rate will likely be reduced from 26% to 3%.
2. ML uses a recommender system to understand the needs and preferences of the target market and then comes up with tailor-made suggestions. This is how Netflix knows which episodes are your favourite.
