Artificial Intelligence (AI) and Machine Learning (ML) are two terms that have become popular in recent years. Both AI and ML are technologies that allow machines to perform tasks that normally require human intelligence. It can be confusing trying to figure out the difference between artificial intelligence (AI) and machine learning (ML). They both sound pretty complex, but at their core, they’re fairly simple concepts, and the relationship between them is relatively uncomplicated.
Let’s understand what exactly they are, how they relate to each other, and how they might be able to help your business succeed.
What is Artificial intelligence (AI)
Artificial intelligence can be focused on tasks that require common sense, such as learning how to walk or solve puzzles, or more abstract domains such as planning and reasoning. Some researchers have argued that only human-level intelligence has been achieved by any form of artificial life, such as in its ability to mimic the behavior of humans.
Types of Artificial intelligence
The ability of AI technologies to replicate human qualities, the technology they employ to do so, the applications they have in the actual world, and the theory of mind—which we'll cover in more detail below—are used to classify them.
All hypothetical and practical artificial intelligence systems can be classified into one of three categories using these traits as a guide:
- Artificial narrow intelligence (ANI)
ANI is a subfield of AI that deals with creating machines that can perform tasks that ordinarily require human intelligence, such as understanding natural language and recognizing objects. ANI is also sometimes referred to as weak AI.
- Artificial general intelligence (AGI)
AGI is a subfield of AI that deals with the creation of machines that can reason, learn, and solve problems like humans. However, AGI is still in its early stages, and current AI technology is far from being able to create true AGI. This type is also referred to as General AI.
- Artificial superintelligence (ASI)
ASI is referred to as strong AI. ASI is a hypothetical form of AI that is smarter than any human. ASI could be used to solve problems that are unsolvable by humans or to create technologies that are far beyond our current understanding. ASI could also be used for evil purposes, such as taking over the world or wiping out humanity. However, it is also possible that ASI could be used for good, such as solving all of humanity's problems or creating a utopia.
What is Machine Learning (ML)
Machine learning is a branch of artificial intelligence that is used to solve problems by making use of data and algorithms. As the name suggests, machine learning involves the use of computers to learn and make decisions.
Machine learning is closely related to data mining, although some authors contrast the two terms while others use them interchangeably. Data mining uses special algorithms to extract useful knowledge from data, whereas machine learning focuses on producing generalizable knowledge that can be applied to new problems by adapting existing knowledge.
Types of Machine Learning (ML)
Machine learning can be divided into three categories:
- Supervised learning
Supervised learning uses a set of rules or examples to teach the computer something new. This type of machine learning is commonly used for image recognition and natural language processing. Supervised learning works well when we have many examples of what we want to train our algorithm on — for example, having hundreds or thousands of images from which to find a specific object — or when there's already a large amount of training data (like if you're trying to identify objects in photos).
- Unsupervised learning
Unsupervised learning does not require us to provide examples at all! This type of machine learning technique can be very useful for situations where there isn't enough training data available or when we don't know exactly what category something belongs in (e.g., detecting whether a dog is a cat or vice versa).
- Reinforcement learning
Reinforcement learning is a type of machine learning that uses a reward function to determine the actions a machine should take to maximize its future rewards. The learning process occurs in real-time, and it requires feedback from the environment, which can be achieved by using sensors or other external devices. Reinforcement learning is one of the most widely used types of machine learning in robotics because it allows for continuous control over an autonomous system.
Difference between Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) and Machine learning (ML) are two terms that have become quite popular in recent times. They are both used to describe technology that can process data and make predictions based on past experiences.
However, there are some key differences between the two that you should be aware of before deciding which one is better for your project.
- Machine learning generally requires less data than AI.
- A human might need to train a machine with AI whereas machines can train themselves with ML.
- ML produces more accurate results than AI but this could change in the future as AI advances in accuracy due to better processing power and algorithmic development.
- AI has a very broad variety of applications. The scope of machine learning is limited.
- The two primary subdivisions of AI are machine learning and deep learning. Machine learning's primary subset is deep learning.
- AI completely handles structured, semi-structured, and unstructured data. Machine learning represents semi-structured and structured data
Your Takeaway?
Though they are often used interchangeably, there is a big difference between artificial intelligence and machine learning. Artificial intelligence is based on making a computer system that can do things that ordinarily require human intelligence, like understanding natural language and recognizing objects. On the other hand, machine learning is a technique for instructing computers to learn from data without being explicitly programmed. In other words, machine learning algorithms create models from data that may be applied to forecast the results of data in the future. As a result, not all AI is machine learning, even though machine learning is AI.