These two terms seem to be related, especially in their application in computer science and software engineering. Is one a subset of another? Is one a tool used to build a system for the other? What are their differences and why are they significant?
Machine learning has been defined by many people in different ways. One definition says that machine learning (ML) is the field of study that gives computers the ability to learn without being explicitly programmed.
Given the above definition, we might say that machine learning is geared towards problems for which we have (lots of) data (experience), from which a program can learn and can get better at a task.
Artificial intelligence has many more aspects, where machines do not get better at tasks by learning from data, but may exhibit intelligence through rules (e.g. expert systems like Mycin), logic or algorithms, e.g. finding paths.
The book Artificial Intelligence: A Modern Approach shows more research fields of AI, like Constraint Satisfaction Problems, Probabilistic Reasoning or Philosophical Foundations.
Definitions of Artificial Intelligence can be categorized into four categories, Thinking Humanly, Thinking Rationally, Acting Humanly and Acting Rationally. The following picture (from Artificial Intelligence: A Modern Approach) will shed light on over these definitions:
The definition which I like is by John McCarthy, "It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."
Machine Learning, on the other hand, is the field of AI which deals with making software to make better predictions for the output without being explicitly programmed. Various algorithms are used over a set of data to predict the future. Machine Learning is data-driven and data-oriented. Machine Learning is evolved from the study of pattern recognition and computational learning theory of AI.
In a nutshell Artificial Intelligence is a field of Computer Science which deals with providing machines the ability to perform rational tasks. Natural Language Processing, Automation, Image Processing, and many others are part of it.
Machine Learning is a subset of AI which is data oriented and deals with predicting. Used in search engines, Youtube recommendation list, etc.
The machine learning is a sub-set of artificial intelligence which is only a small part of its potential. It's a specific way to implement AI largely focused on statistical/probabilistic techniques and evolutionary techniques.Q
Artificial intelligence is 'the theory and development of computer systems able to perform tasks normally requiring human intelligence' (such as visual perception, speech recognition, decision-making, and translation between languages).
We can think of AI as the concept of non-human decision makingQ which aims to simulate cognitive human-like functions such as problem-solving, decision making or language communication.
Machine learning (ML) is basically a learning through doing by the implementation of build models which can predict and identify patterns from data.
According to Prof. Stephanie R. Taylor of Computer Science and her lecture paper, and also Wikipedia page, 'machine learning is a branch of artificial intelligence and it's about construction and study of systems that can learn from data' (like based on the existing email messages to learn how to distinguish between spam and non-spam).
According to Oxford Dictionaries, the machine learning is 'the capacity of a computer to learn from experience' (e.g. modify its processing on the basis of newly acquired information).
We can think ML as computerized pattern detection in the existing data to predict patterns in future data.Q
In other words, machine learning involves the development of self-learning algorithms and artificial intelligence involves developing systems or software to mimic human to respond and behave in a circumstance.Quora
Many terms have 'mostly' the same meanings, and so the differences are just in emphasis, perspective, or historical descent. People disagree as to which label refers to the superset or the subset; there are people who will call AI a branch of ML and people who will call ML a branch of AI.
I typically hear Machine Learning used as a form of 'applied statistics' where we specify a learning problem in enough detail that we can just feed training data into it and get a useful model out the other side.
I typically hear Artificial Intelligence as a catch-all term to refer to any sort of intelligence embedded in the environment or in code. This is a very expansive definition, and others use narrower ones (such as focusing on artificial general intelligence, which is not domain-specific). (Taken to an extreme, my version includes thermostats.)
How is Artificial Intelligence different from Machine Learning https://www.linkedin.com/pulse/how-artificial-intelligence-different-from-machine-learning-singh
Machine learning is a science that involves development of self-learning algorithms. These algorithms are more generic in nature that it can be applied to various domain related problems.
Artificial Intelligence is a science to develop a system or software to mimic human to respond and behave in a circumference. As field with extremely broad scope, AI has defined its goal into multiple chunks. Later each chuck has become a separate field of study to solve its problem.
Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably.
Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart" and Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
You can find a further information about Machine Learning and Artificial Intelligence.
Theory-based AI is What led to the development of Machine Learning. Often referred to as a subset of AI, it’s really more accurate to think of it as the current state-of-the-art technology.
Machine Learning is acquiring knowledge about data using some self-learning algorithms and A.I is a field where machine accomplishes tasks without human support based on that knowledge acquired through learning. So this is what ML is the subset of AI mean.
Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion. So I thought it would be worth writing a piece to explain the difference.
Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves.
First of all, I encountered the term MachineLearning much more in my Business Intelligence classes than in my AI classes.
My AI Professor Rolf Pfeifer would have put it that way: (after having a long speach about what intelligence is, how it can be defined, different types of intelligence, etc.). ML is more static and "dumb", unaware of it's fisical environment and not made to interact with it, or only on an abstract basis. AI has a certain awareness of its environment and interacts with it autonomously, making thereby autonomous decisions with feedback loops. From that point of view, Ugnes Answer would be probably the closest. Besides that, off course, ML is a subset of AI.
Machine Learning is not a real intelligence (imho), it's mostly human intelligence reflected in logical algorithms, and as my Business Intelligence Prof would put it: about data and its analysis. Machine Learning has a lot of supervised algorithms which actually do need humans to support the learning process by telling what's right and what's wrong, so they're not independent. And once they're applied, algorithms are mostly static until humans reajust them. In ML you mostly have black boxes designs and the main aspect is data. Data comes in, Data gets analized ("Intelligently"), Data goes out, and Learning in most times applies to a pre-implementation/Learning fase. In most cases ML doesn't care about the environment a machine is in, it's about data.
AI instead is about mimicking human or animal intelligence. Following my Prof's approach, AI is not necessarily about self-consciousness but about interaction with the environment, so to build AI you need to give the machine sensors to perceive the environment, a sorto of intelligence able to keep on learning, and elements to interact with the environment (arms, etc.). The interaction should happen in autonomous way and ideally, as in humans, learning should be an autonomous, ongoing proces.
So a drone which scans fields in a logical scheme for color patterns to find weeds within crops would be more ML. Specially if the data is later analized and verified by humans or the algorithm used is that a static algorithm with built in "intelligence" but not cabable of rearranging or adapting to its environment. A drone which flies autonomosuly, charges itself up when battery's down, scans for weeds, learns to detect unknown ones and rips them out by itself and brings them back for verification, would be AI...
Before getting into difference is important to get clear on what does they mean exactly.
Artificial intelligence is the science and engineering of making computers behave in ways that, to mimic human behavior - Andrew Moore
Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience - Tom Mitchell
If Artificial Intelligence is making machines to exhibit human intelligence, then machine learning is an approach to achieve that artificial intelligence in which machine can learn by its own without being explicitly programmed. Simply, Machine learning is a part of AI.