These two terms seem to be related, especially in their application in computer science and software engineering.
Machine learning has been defined by many people in multiple (often similar) ways [1, 2]. 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 may 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. path-finding).
The book Artificial Intelligence: A Modern Approach shows more research fields of AI, like Constraint Satisfaction Problems, Probabilistic Reasoning or Philosophical Foundations.
Machine learning is a subset of artificial intelligence. Roughly speaking, it corresponds to its learning side. There is no "official" definitions, boundaries are a bit fuzzy.
According to the book Artificial Intelligence: A Modern Approach (section 1.1), artificial intelligence (AI) has been defined in multiple ways, which can be organized into 4 categories.
Figure 1.1 (of the same book) contains 8 definitions (by renowned people like Bellman, Winston or Kurzweil).
Each box contains 2 similar definitions (i.e. both fall into the same category). These definitions vary along 2 dimensions. The definitions in the top row are concerned with thought-processes and reasoning, while the ones in the bottom are concerned with behaviour. The definitions on the left are associated with human intelligence, while the ones on the right with an idealized version of intelligence, which the authors of the AIMA book call rationality. So, for example, the definitions in the top-left corner are based on thinking humanly, while the definitions on the bottom-right corner are based on acting rationally.
There is also a definition of AI by John McCarthy, who is one of the official founders of the AI field in 1956.
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.
There have been also multiple (similar) definitions of machine learning (ML). For example, Tom Mitchell, in section 1.1. of his book Machine Learning, defines machine learning as follows.
A computer program is said to learn from experience $E$ with respect to some class of tasks $T$ and performance measure $P$, if its performance at tasks in $T$, as measured by $P$, improves with experience $E$.
ML is a subfield of AI, which is data-oriented (or experience-driven). AI is not just ML, but it's also composed of Natural Language Processing, and other subfields.
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
Machine learning is a subfield of artificial intelligence, as the following diagram (taken from this blog post) illustrates.
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.)
In simple words, Artificial intelligence is a field of science that is trying to mimic humans or other animals behavior.
Machine Learning is one of the key tool/technology behind Artificial intelligence.
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 speech about what intelligence is, how it can be defined, different types of intelligence, etc.). ML is more static and "dumb", unaware of its physical 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, of course, ML is a subset of AI.
Machine Learning is not 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 readjust them. In ML you mostly have black boxes designs and the main aspect is data. Data comes in, Data gets analyzed ("Intelligently"), Data goes out, and Learning 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 sort of intelligence able to keep on learning, and elements to interact with the environment (arms, etc.). The interaction should happen in an autonomous way and ideally, as in humans, learning should be an autonomous, ongoing process.
So a drone that scans fields in a logical scheme for colour patterns to find weeds within crops would be more ML. Especially if the data is later analyzed and verified by humans or the algorithm used is that a static algorithm with built-in "intelligence" but not capable of rearranging or adapting to its environment. A drone that flies autonomously, charges itself up when the 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...
