In this tutorial, we will learn the Difference Between Machine Learning and Artificial Intelligence (ML vs AI). So, let’s dig in.
Difference Between Machine Learning and Artificial Intelligence
Artificial Intelligence (AI) and Machine Learning (ML) are two closely related but distinct fields within the broader field of computer science. AI is a discipline that focuses on creating intelligent machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and natural language processing. It involves the development of algorithms and systems that can reason, learn, and make decisions based on input data.
On the other hand, Machine Learning (ML) is a subfield of AI that involves teaching machines to learn from data without being explicitly programmed. ML algorithms can identify patterns and trends in data and use them to make predictions and decisions. ML is used to build predictive models, classify data, and recognize patterns, and is an essential tool for many AI applications.
The development of AI and ML has the potential to transform various industries and improve people’s lives in many ways. AI systems can be used to diagnose diseases, detect fraud, analyze financial data, and optimize manufacturing processes. ML algorithms can help to personalize content and services, improve customer experiences, and even help to solve some of the world’s most pressing environmental challenges.
Despite the many benefits of AI and ML, there are also concerns about the potential risks and challenges associated with these technologies. These include the risk of job displacement, the impact on human autonomy and decision-making, and the potential for AI and ML to be used in harmful ways. As such, it is important to approach the development and use of AI and ML responsibly and ethically and to address the potential risks and challenges associated with these technologies.
Artificial Intelligence (AI)
Artificial Intelligence comprises two words “Artificial” and “Intelligence”. Artificial refers to something which is made by humans or a non-natural thing and Intelligence means the ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system. AI is implemented in the system. There can be so many definitions of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present humans can do better.” Therefore It is an intelligence that we want to add all the capabilities to a machine that human contains.
Based on capabilities, AI can be classified into three types:
- Weak AI
- General AI
- Strong AI
Currently, we are working with weak AI and general AI. The future of AI is Strong AI for which it is said that it will be more intelligent than humans.
Machine Learning (ML)
Machine Learning is the learning in which a machine can learn on its own without being explicitly programmed. It is an application of AI that provides the system with the ability to automatically learn and improve from experience. Here we can generate a program by integrating the input and output of that program. One of the simple definitions of Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”
Machine learning works on an algorithm which learns on its own using historical data. It works only for specific domains if we are creating a machine learning model to detect pictures of dogs, it will only give results for dog images, but if we provide new data like cat images then it will become unresponsive. Machine learning is being used in various places such as for online recommender systems, Google search algorithms, Email spam filters, Facebook Auto friend tagging suggestions, etc.
It can be divided into three types:
- Supervised learning
- Reinforcement learning
- Unsupervised learning
Key Differences Between Artificial Intelligence (AI) and Machine Learning (ML):
The Difference Between Machine Learning and Artificial Intelligence are down below:
| ARTIFICIAL INTELLIGENCE | MACHINE LEARNING |
|---|---|
| 1956 The terminology “Artificial Intelligence” was originally used by John McCarthy, who also hosted the first AI conference. | The terminology “Machine Learning” was first used in 1952 by IBM computer scientist Arthur Samuel, a pioneer in artificial intelligence and computer games. |
| AI stands for Artificial intelligence, where intelligence is defined as the ability to acquire and apply knowledge. | ML stands for Machine Learning which is defined as the acquisition of knowledge or skill |
| AI is the broader family consisting of ML and DL as its components. | Machine Learning is the subset of Artificial Intelligence. |
| The aim is to increase the chance of success and not accuracy. | The aim is to increase accuracy, but it does not care about; the success |
| It works as a computer program that does smart work. | Here, the tasks systems machine takes data and learns from data. |
| The goal is to simulate natural intelligence to solve complex problems. | The goal is to learn from data on certain tasks to maximize the performance on that task. |
| AI has a very broad variety of applications. | The scope of machine learning is constrained. |
| AI is decision-making. | ML allows systems to learn new things from data. |
| It is developing a system that mimics humans to solve problems. | It involves creating self-learning algorithms. |
| AI will go for finding the optimal solution. | ML will go for a solution whether it is optimal or not. |
| AI leads to intelligence or wisdom. | ML leads to knowledge. |
| AI is a broader family consisting of ML and DL as its components. | ML is a subset of AI. |
| Three broad categories of AI are: 1. Artificial Narrow Intelligence (ANI), 2. Artificial General Intelligence (AGI), 3. Artificial Super Intelligence (ASI) | Three broad categories of ML are: 1. Supervised Learning, 2. Unsupervised Learning, 3. Reinforcement Learning |
| AI can work with structured, semi-structured, and unstructured data. | ML can work with only structured and semi-structured data. |
| The most common uses of machine learning are- 1. Facebook’s automatic friend suggestions, 2. Google’s search algorithms, 3. Banking fraud analysis, 4. Stock price forecast, 5. Online recommender systems, and so on. | The most common uses of machine learning- 1. Facebook’s automatic friend suggestions, 2. Google’s search algorithms, 3. Banking fraud analysis, 4. Stock price forecast, 5. Online recommender systems, and so on. |
We hope you have learned in detail about the Difference Between Machine Learning and Artificial Intelligence and can do better in your examination.
