AI vs. ML: The difference between Artificial Intelligence and Machine Learning
Machine learning (ML) is a specific method within the broader field of artificial intelligence (AI). Deciding whether to use AI or ML depends on the problem, data availability, and adaptability required. Recognising the differences helps unlock their full potential to drive innovation.
Introduction
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on enabling computers to learn and make decisions autonomously without being explicitly programmed.
AI systems need to be able to develop their own knowledge, by extracting patterns from unprocessed data. This capability is known as machine learning, a technique that enables computer systems to improve with experience and data.
The advent of machine learning has equipped computers with the ability to address issues that require an understanding of real-world phenomena and make judgments that seem subjective. A straightforward machine learning algorithm, known as logistic regression, can decide whether to suggest cesarean delivery (Mor-Yosef et al., 1990). Similarly, a basic machine learning procedure named naive Bayes can differentiate between valid emails and unsolicited spam emails.
Understanding the Difference between AI and ML
AI is an umbrella term that refers to the development of computer systems capable of performing tasks that would typically require human intelligence. These tasks may include speech recognition, problem-solving, learning, and decision-making. AI can be achieved using a variety of techniques, including rule-based systems, expert systems, and machine learning.
Machine learning, on the other hand, is a specific approach within AI that focuses on creating algorithms that can learn from data and make predictions or decisions based on that data. Instead of being explicitly programmed to perform a specific task, machine learning algorithms "learn" from examples and adapt their behaviour accordingly.
When to Use AI and When to Use ML
- Rule-based or Expert Systems: If a problem domain is well-defined and can be solved using a set of rules or heuristics, then AI techniques like rule-based systems or expert systems can be used. These systems encode human expert knowledge in the form of rules, which are then applied to make decisions or solve problems.
- Large and Complex Data: When dealing with large datasets or complex patterns, machine learning is the preferred approach. ML algorithms can process vast amounts of data and extract valuable insights, making them ideal for tasks such as image recognition, natural language processing, and predictive analytics.
- Adaptable Systems: If the system needs to adapt and evolve over time, machine learning should be considered. ML algorithms can continuously learn from new data and update their behaviour, allowing them to respond to changing environments and requirements.
- Unclear or Evolving Problem Domains: When the problem domain is not well-understood or is constantly changing, machine learning can be a valuable tool. ML algorithms can discover hidden patterns and relationships in the data, helping to uncover new insights or identify emerging trends.
Conclusion
In summary, AI is a broad field that encompasses various techniques and approaches to creating intelligent systems, while machine learning is a specific method within AI that focuses on learning from data. Deciding whether to use AI or ML depends on the nature of the problem, the availability of data, and the adaptability required. By understanding the differences between AI and ML and knowing when to apply each approach, we can unlock the full potential of these powerful technologies to drive innovation and transform industries.
