Is it AI or ML? Let’s Understand the Distinctions and Know When to Use Them
In today’s data-driven world, Artificial Intelligence (AI) has become a catch-all phrase for any application powering intelligent decisions. Since Machine Learning is an approach to achieving AI, the two terms are naturally connected.
However, people often use the term AI when they are specifically referring to machine learning processes, and the two terms end up getting used interchangeably. It’s important to note that while AI and Machine Learning are related, they are not synonymous. They are separate ideas with distinct roles and applications.
This is the first article in our AI series.
The article highlights the distinctions between AI and ML and offers clarity on how to use each term appropriately. It also explores how to recognize an AI program from an ML one.
What is Artificial Intelligence (AI)?
AI is a discipline aimed at creating systems capable of performing activities that would normally need human intelligence. These activities include reasoning, learning, problem-solving, perception, and language comprehension.
It encompasses various technologies including ML, but also includes non-ML methods like rule-based systems, expert systems, search algorithms, and so on.
Since its emergence in the mid-20th century, AI has advanced remarkably, resulting in the creation of complex algorithms and applications.
Types of AI
- Narrow AI
Currently, when we talk about AI, we are mostly referring to Narrow AI. These systems are designed for very specific tasks and applications, using specialized algorithms and models. Also known as Weak AI, they have limited cognitive abilities compared to human intelligence. While they excel at performing well-defined functions, they cannot generalize knowledge or skills across different domains. Narrow AI uses machine learning, natural language processing, artificial neural networks, and deep learning to classify data. It is crucial in automation and decision-making, with examples including speech and visual recognition systems, recommendation algorithms, chatbots, and virtual assistants. - General AI
General AI, also known as Strong AI, is still theoretical. When developed, it would possess generalized human cognitive abilities, allowing it to learn across domains and perform any intellectual task that a human can. In the future, General AI could augment human capabilities and revolutionize research, productivity, and personal assistance. - Superintelligent AI
Superintelligent AI is a topic of ongoing research and speculation, with its development presenting both exciting possibilities and potential risks. This type of AI would surpass human intelligence and capabilities in all areas. The path to Superintelligent AI is uncertain, and it is essential to approach its development with caution to ensure its immense potential is utilized responsibly.
What is Machine Learning (ML)?
Machine Learning (ML) is a subset of Narrow AI that aims to allow systems to learn from data, recognize patterns, and make decisions with little human input.
The origins of ML trace back to early artificial intelligence research in the 1950s and 1960s. However, it wasn’t until the emergence of big data and advanced computing power that ML started to thrive.
Types of ML
- Supervised Learning involves training algorithms on labeled datasets to recognize patterns and predict outcomes. Supervised Learning uses the technique of Classification to separate the data, and Regression to fit the data.
- Unsupervised Learning deals with unlabeled and unstructured data, where the system tries to learn the underlying structure without predefined labels. It is utilized for Clustering and Association tasks.
- Reinforcement Learning models learn by interacting with their environment and training themselves through trial and error. This approach is often used in game play and robotics.
| Where does Generative AI fit in? Generative AI is a subset of Narrow AI which leverages Machine Learning techniques to learn from existing data and create new, realistic content, including text, images, music, videos, and even code. This intersection of generative AI and machine learning opens up a wide range of applications in content creation, data augmentation, and beyond. Examples of Generative AI include GPT-3, DALL-E, Sora and others. |
Many people confuse AI with ML, thinking they are synonymous. This confusion is understandable because ML is an essential part of many AI systems, making it difficult to see where AI ends and ML begins.
However, AI and ML have a number of differences. Understanding these differences is important for businesses to be able to use these technologies more effectively.
Key Distinctions Between AI and ML
While they are related terms, AI and ML differ significantly in scope, methodology, and objectives. Here’s a handy primer on the key differences between these two.
| Artificial Intelligence (AI) | Machine Learning (ML) | |
|---|---|---|
| Scope | AI is the broad discipline concerned with creating intelligent systems. | ML is a specific approach within AI focused on data-driven learning. |
| Methodology | AI involves designing systems with rules and logic to mimic human behavior. | ML involves training models to learn from data and improve over time. |
| Objective | ML involves training models to learn from data and improve over time. | ML aims to develop systems that can automatically improve their performance on tasks through experience. |
Now that we’ve defined the terms and pointed out their differences, let’s try and understand when to use each term.
How to Use the Terms “AI” and “ML” Appropriately
Knowing when to use the terms AI and ML is crucial because they often overlap but refer to different concepts.
Here’s a guide to help clarify when to use each term appropriately.
Use the term AI when:
- Talking about the overall field of intelligent systems and technologies designed to perform tasks that typically require human intelligence.
- Referring to systems or technologies that mimic human intelligence and cognitive functions across a wide range of tasks.
- Discussing applications that require multiple types of intelligence, such as natural language understanding, vision, and decision-making.
Use the term ML when:
- Referring to systems that learn from data. ML involves algorithms that improve their performance on a task over time as they are exposed to more data.
- Discussing particular techniques such as supervised learning, unsupervised learning, reinforcement learning, or specific algorithms like neural networks, decision trees, and clustering.
- The emphasis is on building models that learn from data and make predictions or decisions based on that learning.
In general, AI is the broader concept encompassing the development of systems that exhibit intelligent behavior across various tasks. So we use the term “AI” when referring to the general capability of intelligent systems.
However, ML is a subset of AI focused on developing algorithms that learn from and make predictions or decisions based on data. So we use the term “ML” when discussing specific data-driven techniques and model training processes.
But how do we know whether a program or application is AI or ML?
How to Determine if an Application is AI or ML
To identify whether a program is AI or ML, we need to examine the scope of the program, the level of problem it is solving, the core functionality and methodology used, and the learning mechanisms.
Here’s how to distinguish between the two.
| It is AI if the program… | It is ML if the program… |
|---|---|
| …is tackling a broad range of human-like tasks | …is tackling a specific problem that requires pattern recognition or data analysis |
| …employs predefined rules and logic to make decisions | …uses data to learn and improve over time |
| …uses symbolic reasoning, logic, and possibly expert knowledge | …involves training models on datasets |
| …performs a wide array of tasks that require cognitive functions similar to human intelligence | …focuses on specific tasks and improves through data |
| …primarily follows programmed instructions with some level of adaptability | …uses learning algorithms to adapt to new data and improve accuracy |
While AI and ML are closely related, they serve different purposes and operate in distinct ways. Let’s look at some examples to get a better understanding of their distinctions.
Examples of AI and ML in Use
Artificial Intelligence encompasses a broad range of intelligent behaviors in order to simulate human-like decision-making, reasoning, and problem-solving.
Virtual assistants like Siri or Google Assistant that understand and respond to voice commands, manage tasks, and provide information are examples of AI. As are autonomous vehicles which combine computer vision, sensor fusion, and decision-making algorithms to navigate and drive safely. There are also domain specific AI systems, like those used in medical diagnosis which analyze medical images, patient data, and research to diagnose diseases and recommend treatments.
On the other hand, Machine Learning is specifically about learning from data.
ML algorithms can function as recommendation engines to analyze user behavior and preferences and suggest products, movies, or music. They can analyze transaction data to identify and flag potentially fraudulent activities in real-time, or predict equipment failures in industries by analyzing sensor data and historical maintenance records.
The Intersection of AI and ML Will Shape the Future of Market Research
It is essential for researchers and companies to recognize the distinctions between AI and ML, and know when to use each term in order to navigate the current dynamic landscape.
By leveraging the strengths of both technologies, businesses and insight teams can achieve deeper insights, enhanced automation, and better decision-making capabilities. As the fields continue to evolve and intersect, the synergy between AI and ML will undoubtedly lead to further innovations, enhancing capabilities and transforming the way we do market research.
Want to know how InsightGig is using AI and ML to revolutionize the Market Research process? Talk to our team today >
