Often, the terms artificial intelligence (AI) and machine learning (ML) are used interchangeably. However, they are not the same thing. AI allows machines to carry out tasks in a way that mimics human intelligence. According to a recent study by Statista, the chances for the AI global market are expected to reach $1,344 billion by 2030.
ML is one of the most important technologies responsible for the growth of AI. But what exactly is machine learning vs artificial intelligence? Let’s explore in this blog.
Machine Learning vs artificial intelligence definitions
Before we discuss what is artificial intelligence vs machine learning, here are simple definitions of the terms.
What is artificial intelligence?
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. The processes include learning, reasoning, and self-correction. AI is being widely used in various fields, such as robotics, natural language processing, machine learning, and computer vision.
According to an online report, the global AI market is valued at over $279 billion as of December 2024. Moreover, 83% of companies claim that AI is a top priority in their business plans. AI can be classified into two main categories.
- Narrow AI
Also called weak AI, this is designed to perform specific tasks such as facial recognition and language translations.
- General AI
This theoretical form of AI is also referred to as strong AI, and it would be capable of understanding and performing any intellectual tasks that a human can do.
What is machine learning?
Machine learning is one of the most commonly used concepts nowadays. Did you know that the global market for machine learning was valued at $19.20 billion in 2022 and is projected to reach $225.95 billion by 2030? This shows that the industry is growing at a CAGR of 36.2%.
Machine learning (ML) is a subset of artificial intelligence that allows systems to automatically learn and improve from experience without being explicitly programmed. It involves the use of algorithms and statistical models for data and pattern analysis. ML allows computers to make predictions or decisions based on the data available. These systems improve their performance over time as they are exposed to more data and can handle tasks like classification and regression.
For example, platforms like Amazon and Netflix use machine learning models to recommend products and movies based on user behaviour. There are three main categories of ML.
- Supervised learning
The model is trained on labelled data, where you provide both the input and the desired output. Based on the examples in the training data, the system will learn to map inputs to the correct output.
- Unsupervised learning
The model is given unlabelled data and must find patterns or structures within the data on its own. Examples of tasks in unsupervised learning include clustering and dimensionality reduction.
- Reinforcement learning
In this approach, the system learns as it interacts with an environment and receives feedback in the form of rewards or penalties. This type of learning is commonly used in robotics and gaming.
How AI and ML work together
Although machine learning vs artificial intelligence implies, the relationship between them is complementary. ML plays a central role in advancing AI capabilities. To understand how they work together, let’s see some of the ways in which ML is applied to AI.
| AI Without ML | AI With ML |
|---|---|
| Traditional AI systems may rely on pre-programmed rules and decision trees. For example, expert systems are a form of AI that uses a set of predefined rules to make decisions. They do not learn from data. | Many modern AI systems use ML to improve decision-making capabilities. For instance, a self-driving car might use AI to interpret sensor data, but it relies on ML algorithms to improve its abilities. |
ML acts as the engine behind AI
While traditional artificial intelligence relies on explicit programming rules, ML allows AI systems to learn and adapt based on the data. For example, in natural language processing (NLP), AI systems need to understand and generate human languages.
While AI provides the framework for understanding linguistic structures, machine learning allows these systems to learn language patterns from data. The result is advanced AI models like GPT or BERT.
ML improves the decision-making capabilities of AI
In many AI systems, algorithms are used to refine decision-making by learning from past outcomes. For example, in autonomous vehicles, the AI system relies on sensors and cameras to perceive the environment. What ML algorithms do is process this sensory data to make real-time decisions about object detection and other tasks.
AI for data analysis
AI provides the tools to process data, but machine learning helps to identify meaningful patterns and correlations in that data. An example of this is in healthcare. AI and AL are used for data analysis, such as medical records and clinical trial results, to predict patient outcomes. Machine learning algorithms can uncover patterns in data that may not be immediately apparent to human analysts.
Natural Language Processing (NLP) is an AI technology that allows computers to interpret and understand human language. For instance, in sentiment analysis, AI systems can do text analysis to see whether the sentiment is positive, negative, or neutral. As machine learning models are trained on large datasets of labelled data, the accuracy of the system can be improved.
What is artificial intelligence vs machine learning
Both AI and ML aim to create intelligent systems. However, they do have differences in terms of definitions and applications. Here is a simple table to illustrate machine learning vs artificial intelligence.
| Artificial Intelligence (AI) | Machine Learning (ML) | |
|---|---|---|
| Definition and scope | AI is a broader concept of machines being able to carry out tasks that would normally require human intelligence. AI includes both ML and non-ML methods, such as rule-based reasoning. | ML is a subset of AI that focuses on the idea that systems can learn from data and make decisions without being explicitly programmed. ML mostly focuses on algorithms that learn and adapt. |
| Goal | AI is a tool to create intelligent machines that can think and act like humans. This could involve reasoning and planning. | ML improves a system’s performance over time by learning from data. The focus is on algorithms that can identify patterns. |
| Approach | AI can involve both learning and non-learning systems. It may use symbolic reasoning and search algorithms, where predefined rules are used to make decisions. | ML is purely data-driven and involves teaching a system by exposing it to a large amount of data. It relies on statistical techniques to improve with time. |
| Application | Autonomous vehicles, robotics, virtual assistance, tracking, and security | Image recognition, speech recognition, traffic prediction, and product recommendations |
| Dependency on data | AI systems do not always require data to function. | ML systems are highly dependent on data. |
Artificial intelligence engineer vs machine learning engineer
The goal of AI engineers is to create intelligent, decision-making agents. They mostly create and execute complicated AI systems that combine several ML models and AI elements.
Conversely, ML engineers create and implement models that are capable of carrying out operations like data preparation. They are adept in creating and refining models for particular tasks using machine learning frameworks like TensorFlow and PyTorch.
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Book a ConsultationArtificial intelligence vs data science vs machine learning
Machine learning vs artificial intelligence has been discussed in the above paragraphs. Data Science is a combination of multiple disciplines that uses statistics, data analysis, and machine learning to analyse data and to extract knowledge and insights from it.
Data mining vs machine learning vs artificial intelligence
You can read about machine learning vs artificial intelligence in the above-mentioned sections of the blog. The practice of extracting insights and patterns from massive databases is known as data mining. The techniques include clustering and classification.
How can organisations use AI and ML?
Understood machine learning vs artificial intelligence, we will now explore how organisations can use them.
Artificial intelligence and machine learning are used in many different industries for various purposes, from improving efficiency to better deliveries. Some of the key ways in which organisations can use AI and ML are listed below.
Improve customer experience
Machine learning and artificial intelligence can help improve customer experiences through tailored services and support. One such way to do is through chatbots and virtual assistants. They can be easily integrated into customer service systems and can provide 24/7 support and answer frequently asked questions.
Moreover, AI and ML can analyse customer behaviour and preferences and offer custom recommendation. This is commonly used in retail and entertainment platforms such as Amazon and Netflix. Additionally, NLP algorithms can determine the sentiment behind customer comments and help businesses identify their pain points.
Optimise business processes
For better efficiency and reducing human errors, AI and ML can play an important part. They can automate manual data entry tasks such as processing invoices. These technologies can also be used to predict demand and improve delivery routes. Through historical data and weather patterns analysis, machine learning models can forecast more accurately.
AI and ML can also be used for robotic process automation (RPA) for different tasks, from handling simple tasks like data extraction to complex ones like decision-making.
Better decision-making
The technologies allow businesses to make better and faster decisions. ML models can be used for predictive analysis and trained on historical data to predict future outcomes. For example, in the financial sector, AI can predict stock market trends and identify fraud.
Analytical tools that use AI can be used to process large datasets and generate real time insights. You can use these insights for strategy, marketing, sales, product development and more.
Increase marketing and sales
If your company is looking to optimise campaigns and customer acquisition and retention, AI and ML are the new trend. ML algorithms can provide targeted marketing campaigns. They can observe demographics, history, purchase behaviour and other data to segment audiences.
AI models can also adjust process based on market conditions. This dynamic pricing strategy is often used by airlines and e-commerce businesses for revenue optimisation.
Improve healthcare outcomes
From diagnosing diseases to improving patient care, healthcare companies can use artificial intelligence and machine learning. AI systems can perform medical images and diagnostics, such as X-rays, MRIs, and CT scans. They can be used to detect signs of diseases, including cancer and heart conditions.
Through patient data analysis, healthcare providers can create personalised treatment plans.
Detect fraud
In order to increase security and prevent fraud, organisations can make use of machine learning vs artificial intelligence. These are specifically helpful in industries like finance. AI can also help detect potential vulnerabilities and identify cyberattacks such as phishing and malware.
Read More: Has AI avoided problems in the VC industry?
