Key Takeaways
- AI is the technology that enables machines to perform tasks that typically require human intelligence, like problem-solving and decision-making.
- ML is a subset of AI that focuses on helping machines learn from data and improve over time.
- ML is always part of AI, but not all AI involves ML, as AI also includes other techniques like rule-based systems.
It’s hard to imagine life without technology—it’s become so deeply embedded in our daily routines that it’s almost impossible to separate ourselves from it. Yet, with constant advancements across countless fields, keeping up can be overwhelming.
Artificial intelligence (AI) and machine learning (ML), for example, are two concepts often used in similar contexts without clear distinctions. This could lead many to believe they are the same.
However, once you explore machine learning vs. AI, it’s evident that, while they have similarities, the two are distinct in the way they operate and are applied.
What Is Artificial Intelligence (AI)?
AI refers to the systems or machines that have the ability to mimic human intelligence to perform tasks such as learning, problem-solving, decision-making, and pattern recognition.
The concept of AI has its roots in the mid-20th century when computer scientist Alan Turing posed the seminal question, “Can machines think?” Since then, AI has evolved from a theoretical idea to an active and transformative force in many industries.
The field of AI is quite broad, and it encompasses several types of intelligence. Two broad categories include narrow AI (weak AI) and general AI (strong AI).
The former is designed for specific tasks like voice assistants or recommendation systems, whereas the latter remains speculative as the kind of AI that would possess the ability to perform any cognitive task that a human can do.
What Is Machine Learning (ML)?
ML is a subset of AI that primarily focuses on the development of algorithms that make it possible for machines to learn from data and use that information to improve their performance over time.
ML systems “learn” from experience, improving their outputs as they are exposed to more data. The machine isn’t given specific instructions on how to perform a task. Instead, it is fed large amounts of data and uses algorithms to identify patterns or relationships within the data.
There are several types of machine learning, with the main ones including:
- Supervised learning: Learns from labeled data (known outcomes) and improves by comparing predictions with actual results.
- Unsupervised learning: Identifies patterns in unlabeled data without any guidance.
- Reinforcement learning: Improves by interacting with its environment and receiving rewards or penalties.
- Semi-supervised and self-supervised learning: Uses both labeled and unlabeled data to learn and find patterns.
Key Differences Between AI and Machine Learning
The primary difference between AI and machine learning is their scope—AI is the broader concept, whereas ML is a specific subset within it. This core difference is reflected in their level of autonomy and the approaches they take.
Autonomy
While AI systems can be autonomous or semi-autonomous, particularly in advanced systems, traditional AI models generally do not have the ability to learn or adapt after their initial programming. For example, a traditional model of chess-playing AI would use a predefined set of rules and algorithms to make moves, but it wouldn’t have the ability to evolve through experience.
ML systems, on the other hand, are inherently autonomous in the sense that they constantly evolve and adapt based on the data they process. Once trained, these systems operate independently and continuously refine their models without human input, making ML more flexible and adaptable than traditional AI.
Decision-making
AI and ML also generally have different ways of dealing with tasks and solving problems. AI utilizes a variety of problem-solving methods that often include rule-based systems or logic-based reasoning, which uses a structured approach to solve problems based on known facts and relationships.
In contrast, ML uses algorithms that focus on learning from patterns in data. Instead of being bound to static rules, ML systems are able to identify relationships within the data they process and adjust their approaches based on these insights. This makes ML particularly suited for solving problems in complex environments where predefined rules are impractical.
How Does Machine Learning Fit into AI?
You can think of AI as a brain and then ML as a representation of all the processes that help it become smarter. Just as any brain would learn and grow through experience, ML enables AI to get better over time by learning from the data it encounters. It’s this learning ability that enables the more complex systems of AI to adapt better.
However, machine learning is just one approach within the broader field of AI. The latter doesn’t always require learning—other methods can also be used to create intelligent systems.
The two are also connected through data science–the discipline that extracts valuable insights from data. Without the foundation of data science, ML would lack the structured data necessary to make accurate predictions, and AI systems would struggle to perform intelligent tasks. So, data science powers both AI and ML, enabling them to grow smarter and more capable.
Real-World Applications of AI vs. Machine Learning
AI and ML are revolutionizing countless industries, from healthcare and finance to entertainment and retail. While the two often work hand-in-hand, they focus and emphasize different aspects, thus often also have different applications.
AI is used to automate various tasks. Its versatility is evident across industries through its application in:
- Chatbots and virtual assistants, like Siri and Alexa
- Robotics
- Decision support systems that assist businesses in making data-driven decisions
- Self-driving cars
- Healthcare diagnosis
- Smart manufacturing, by predicting maintenance needs and optimizing production processes
Machine learning has a more focused role in improving systems through data-driven learning. Therefore, it’s widely used in applications where patterns need to be recognized and predictions made, such as:
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- Fraud detection in transactions
- Recommendation systems for platforms like Netflix and Amazon
- Speech recognition in voice-activated devices, like Google Assistant or transcription services
- Image and video recognition for social media platforms and security systems.
- Medical imaging
- Email filtering and spam detection
- Customer segmentation based on buying behavior and preferences
In many cases, AI and ML work together to enhance the capabilities of systems. For example, in self-driving cars, AI is responsible for the overall decision-making and navigation, but ML helps through image recognition so that the vehicle can detect obstacles and make informed decisions in real time.
Similarly, in healthcare, AI models assist in diagnosing diseases by analyzing patient data, while ML continuously improves the accuracy of these diagnoses by learning from new medical records and patient outcomes.
Benefits of AI and Machine Learning
AI and ML bring significant benefits that go beyond their individual applications. One of the most notable advantages is their ability to improve efficiency. By automating tasks and analyzing such large amounts of data quickly, both AI and ML can reduce human error as well as streamline operations to free up time for more strategic work.
These technologies can also drive innovation and enhance decision-making by providing insights based on data that help businesses and organizations make better-informed choices.
The Future of Machine Learning and AI
Over the past decade, AI and ML have transformed industries, from healthcare breakthroughs to personalized customer experiences. As technology advances, their impact will only grow, creating new career opportunities.
If you want to be part of this revolution, Syracuse University’s iSchool provides world-class programs to prepare you for success. Specifically, the Master’s Degree in Artificial Intelligence and the Master’s Degree in Applied Data Science are great options for pursuing careers in AI or ML.
These programs focus on both the theoretical aspects of these fields and the practical experience the students need to overcome real-world challenges once they begin their careers.
As one of our graduates, SaiKumarReddy Pochireddygari, who earned his Master’s in Applied Data Science, shared, “The coursework in machine learning and big data at Syracuse prepared me well for both theoretical and practical aspects of machine learning, for which I am incredibly grateful.”
As we move forward, you’ll watch these fields evolve; the important thing is to make sure you evolve with them. So, join us at the iSchool and be part of shaping the future.
Frequently Asked Questions (FAQs)
Do data analysts use machine learning?
Yes. Data analysts use machine learning to analyze large datasets, detect patterns, and make more accurate predictions for decision-making.
Is ChatGPT an AI or machine learning?
ChatGPT is a combination of both AI and machine learning—it uses advanced machine learning models to generate responses that feel very human, all powered by AI.
Is it better to learn AI or machine learning?
That really depends on where you want to take your career! If you’re more interested in solving data-related problems, machine learning is a great focus. But if you want to dive into a broader range of intelligent systems, AI might be the better choice.
How do businesses decide whether to use AI or machine learning?
It usually comes down to the problem they’re facing. If they need a system that adapts and learns from data, they’ll likely go with machine learning. But if the goal is more about automating decisions or processes, AI is typically the go-to.