Answers.Body: <p><strong>Machine learning</strong> has been defined by many people in multiple (often similar) ways [<a href="http://noiselab.ucsd.edu/ECE228/Murphy_Machine_Learning.pdf" rel="noreferrer">1</a>, <a href="http://www.cs.ubbcluj.ro/%7Egabis/ml/ML-books/McGrawHill%20-%20Machine%20Learning%20-Tom%20Mitchell.pdf" rel="noreferrer">2</a>]. One definition says that machine learning (ML) is the field of study that gives computers the <em>ability to learn</em> without being explicitly programmed.</p>
  <p>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.</p>
  <p><strong>Artificial intelligence</strong> has many more aspects, where machines may not get better at tasks by learning from data, but may exhibit <em>intelligence</em> through rules (e.g. expert systems like <a href="https://en.wikipedia.org/wiki/Mycin" rel="noreferrer">Mycin</a>), <a href="https://silp.iiita.ac.in/wp-content/uploads/PROLOG.pdf" rel="noreferrer">logic</a> or algorithms, e.g. path-finding).</p>
  <p>The book <a href="http://aima.cs.berkeley.edu/" rel="noreferrer"><em>Artificial Intelligence: A Modern Approach</em></a> shows more research fields of AI, like <em>Constraint Satisfaction Problems</em>, <em>Probabilistic Reasoning</em> or <em>Philosophical Foundations</em>.</p>
  
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Answers.UserDisplayName: miku
Answers.Body: <p>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.</p>
  
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Answers.Body: <h3>Artificial intelligence</h3>
  <p>According to the book <a href="https://www.cin.ufpe.br/%7Etfl2/artificial-intelligence-modern-approach.9780131038059.25368.pdf#page=27" rel="nofollow noreferrer">Artificial Intelligence: A Modern Approach</a> (section 1.1), artificial intelligence (AI) has been defined in <em>multiple</em> ways, which can be organized into 4 categories.</p>
  <ol>
  <li><strong>Thinking Humanly</strong></li>
  <li><strong>Thinking Rationally</strong></li>
  <li><strong>Acting Humanly</strong></li>
  <li><strong>Acting Rationally</strong></li>
  </ol>
  <p>Figure 1.1 (of the same book) contains 8 definitions (by renowned people like Bellman, Winston or Kurzweil).</p>
  <p><a href="https://i.sstatic.net/O9J5Q.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/O9J5Q.png" alt="enter image description here" /></a></p>
  <p>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 <strong>thought-processes</strong> and <strong>reasoning</strong>, while the ones in the bottom are concerned with <strong>behaviour</strong>. The definitions on the left are associated with <strong>human intelligence</strong>, while the ones on the right with an idealized version of intelligence, which the authors of the AIMA book call <strong>rationality</strong>. So, for example, the definitions in the top-left corner are based on <strong>thinking humanly</strong>, while the definitions on the bottom-right corner are based on <strong>acting rationally</strong>.</p>
  <p>There is also a definition of AI by <a href="http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html" rel="nofollow noreferrer">John McCarthy</a>, who is <a href="https://en.wikipedia.org/wiki/Dartmouth_workshop" rel="nofollow noreferrer">one of the official founders of the AI field in 1956</a>.</p>
  <blockquote>
  <p>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.</p>
  </blockquote>
  <h3>Machine learning</h3>
  <p>There have been also multiple (similar) definitions of machine learning (ML). For example, Tom Mitchell, in section 1.1. of his book <a href="http://www.cs.ubbcluj.ro/%7Egabis/ml/ML-books/McGrawHill%20-%20Machine%20Learning%20-Tom%20Mitchell.pdf#page=14" rel="nofollow noreferrer">Machine Learning</a>, defines machine learning as follows.</p>
  <blockquote>
  <p>A computer program is said to learn from experience <span class="math-container">$E$</span> with respect
  to some class of tasks <span class="math-container">$T$</span> and performance measure <span class="math-container">$P$</span>, if its performance at tasks in <span class="math-container">$T$</span>, as measured by <span class="math-container">$P$</span>, improves with experience <span class="math-container">$E$</span>.</p>
  </blockquote>
  <h3>What is the difference between AI and ML?</h3>
  <p>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.</p>
  
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Answers.UserDisplayName: Ugnes
Answers.Body: <p>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.<sup><a href="https://www.quora.com/What-are-the-main-differences-between-artificial-intelligence-and-machine-learning/answer/Phillip-Rhodes" rel="noreferrer">Q</a></sup></p>
  
  <h3>Artificial intelligence</h3>
  
  <p>Artificial intelligence is '<strong>the theory and development of computer systems able to perform tasks normally requiring human intelligence</strong>' (such as visual perception, speech recognition, decision-making, and translation between languages).</p>
  
  <p>We can think of AI as the concept of non-human decision making<sup><a href="https://www.quora.com/What-are-the-main-differences-between-artificial-intelligence-and-machine-learning/answer/Yuval-Ariav" rel="noreferrer">Q</a></sup> which aims to simulate cognitive human-like functions such as problem-solving, decision making or language communication.</p>
  
  <h3>Machine learning</h3>
  
  <p>Machine learning (ML) is basically <strong>a learning through doing</strong> by the implementation of build models which can predict and identify patterns from data.</p>
  
  <p>According to Prof. <a href="http://www.cs.colby.edu/srtaylor/" rel="noreferrer">Stephanie R. Taylor</a> of Computer Science and her <a href="http://cs.colby.edu/courses/S15/cs251/LectureNotes/Lecture_15_MLandDMintro_03_09_2015.pdf" rel="noreferrer">lecture paper</a>, and also <a href="https://en.wikipedia.org/wiki/Learning#Machine_learning" rel="noreferrer">Wikipedia page</a>, 'machine learning is a branch of artificial intelligence and <strong>it's about construction and study of systems that can learn from data</strong>' (like based on the existing email messages to learn how to distinguish between spam and non-spam).</p>
  
  <p>According to <a href="http://www.oxforddictionaries.com/definition/english/machine-learning" rel="noreferrer">Oxford Dictionaries</a>, the machine learning is '<strong>the capacity of a computer to learn from experience</strong>' (e.g. modify its processing on the basis of newly acquired information).</p>
  
  <p>We can think ML as computerized pattern detection in the existing data to predict patterns in future data.<sup><a href="https://www.quora.com/What-are-the-main-differences-between-artificial-intelligence-and-machine-learning/answer/Yuval-Ariav" rel="noreferrer">Q</a></sup></p>
  
  <hr>
  
  <p>In other words, <strong>machine learning involves the development of self-learning algorithms</strong> and <strong>artificial intelligence involves developing systems or software</strong> to mimic human to respond and behave in a circumstance.<sup><a href="https://www.quora.com/What-are-the-main-differences-between-artificial-intelligence-and-machine-learning/answer/Sakthi-Dasan-2" rel="noreferrer">Quora</a></sup></p>
  
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Answers.UserDisplayName: kenorb
Answers.Body: <p>Machine learning is a subfield of artificial intelligence, as the following diagram (taken from <a href="https://www.linkedin.com/pulse/how-artificial-intelligence-different-from-machine-learning-singh" rel="nofollow noreferrer">this blog post</a>) illustrates.</p>
  <p><a href="https://i.sstatic.net/5i2z7.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/5i2z7.png" alt="enter image description here" /></a></p>
  
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Answers.Body: <p>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.</p>
  <p>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.</p>
  <p>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 <em>general</em> intelligence, which is not domain-specific). (Taken to an extreme, my version includes thermostats.)</p>
  
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Answers.Body: <p>In simple words, <strong>Artificial intelligence</strong> is a field of science that is trying to mimic humans or other animals behavior. </p>
  
  <p><strong>Machine Learning</strong> is one of the key tool/technology behind Artificial intelligence.</p>
  
  <p><a href="https://i.sstatic.net/OkLbE.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/OkLbE.jpg" alt="enter image description here"></a></p>
  
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Answers.Body: <p>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.</p>
  
  <p>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.</p>
  
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Answers.Body: <p>First of all, I encountered the term MachineLearning much more in my Business Intelligence classes than in my AI classes.</p>
  <p>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 &quot;dumb&quot;, 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.</p>
  <p>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 (&quot;Intelligently&quot;), 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.</p>
  <p>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.</p>
  <p>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 &quot;intelligence&quot; 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...</p>
  
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Body: <p>These two terms seem to be related, especially in their application in computer science and software engineering.</p>
  <ul>
  <li>Is one a subset of another?</li>
  <li>Is one a tool used to build a system for the other?</li>
  <li>What are their differences and why are they significant?</li>
  </ul>
  
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Comments.Text: You should accept an answer, so that the best answer is on top of the others. In my view, the best answer is also the most upvoted one, i.e. [this one](https://ai.stackexchange.com/a/53/2444), which I think you should accept, as it's objectively correct and simple. However, I've changed [this answer](https://ai.stackexchange.com/a/2233/2444) to make it even more valuable too, so you may consider it too. Take a look at https://ai.stackexchange.com/help/someone-answers for more info about how to act when you get answers to your own question.
Comments.UserDisplayName: nbro
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Comments.Text: You can also read the related questions [What is machine learning?](https://ai.stackexchange.com/q/12558/2444) and [What is artificial intelligence?](https://ai.stackexchange.com/q/7838/2444).
Comments.UserDisplayName: nbro
Tags: <machine-learning><comparison><terminology><ai-field>
Title: What is the difference between artificial intelligence and machine learning?
UserDisplayName: intcreator
ThreadText: # What is the difference between artificial intelligence and machine learning?
  
  (Asked by: username_0 on 2016-08-02)
  
  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?
  
  **Tags:** machine-learning, comparison, terminology, ai-field
  
  > **Comments:**
  > **username_1**: You should accept an answer, so that the best answer is on top of the others. In my view, the best answer is also the most upvoted one, i.e. [this one](https://ai.stackexchange.com/a/53/2444), which I think you should accept, as it's objectively correct and simple. However, I've changed [this answer](https://ai.stackexchange.com/a/2233/2444) to make it even more valuable too, so you may consider it too. Take a look at https://ai.stackexchange.com/help/someone-answers for more info about how to act when you get answers to your own question.  
  > **username_1**: You can also read the related questions [What is machine learning?](https://ai.stackexchange.com/q/12558/2444) and [What is artificial intelligence?](https://ai.stackexchange.com/q/7838/2444).  
  
  ---
  
  ## 9 answers
  
  ### Answer by username_2 on 2016-08-02. Score: 65
  
  **Machine learning** has been defined by many people in multiple (often similar) ways [[1](http://noiselab.ucsd.edu/ECE228/Murphy_Machine_Learning.pdf), [2](http://www.cs.ubbcluj.ro/%7Egabis/ml/ML-books/McGrawHill%20-%20Machine%20Learning%20-Tom%20Mitchell.pdf)]. 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](https://en.wikipedia.org/wiki/Mycin)), [logic](https://silp.iiita.ac.in/wp-content/uploads/PROLOG.pdf) or algorithms, e.g. path-finding).
  
  The book [*Artificial Intelligence: A Modern Approach*](http://aima.cs.berkeley.edu/) shows more research fields of AI, like *Constraint Satisfaction Problems*, *Probabilistic Reasoning* or *Philosophical Foundations*.
  
  
  ---
  
  ### Answer by username_3 on 2016-08-02. Score: 25
  
  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.
  
  
  ---
  
  ### Answer by username_4 on 2016-10-30. Score: 17
  
  ### Artificial intelligence
  
  According to the book [Artificial Intelligence: A Modern Approach](https://www.cin.ufpe.br/%7Etfl2/artificial-intelligence-modern-approach.9780131038059.25368.pdf#page=27) (section 1.1), artificial intelligence (AI) has been defined in *multiple* ways, which can be organized into 4 categories.
  
  1. **Thinking Humanly**
  2. **Thinking Rationally**
  3. **Acting Humanly**
  4. **Acting Rationally**
  
  Figure 1.1 (of the same book) contains 8 definitions (by renowned people like Bellman, Winston or Kurzweil).
  
  [![enter image description here](https://i.sstatic.net/O9J5Q.png)](https://i.sstatic.net/O9J5Q.png)
  
  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](http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html), who is [one of the official founders of the AI field in 1956](https://en.wikipedia.org/wiki/Dartmouth_workshop).
  
  > 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
  
  There have been also multiple (similar) definitions of machine learning (ML). For example, Tom Mitchell, in section 1.1. of his book [Machine Learning](http://www.cs.ubbcluj.ro/%7Egabis/ml/ML-books/McGrawHill%20-%20Machine%20Learning%20-Tom%20Mitchell.pdf#page=14), 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 $E$.
  
  ### What is the difference between AI and ML?
  
  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.
  
  
  ---
  
  ### Answer by username_5 on 2016-08-15. Score: 12
  
  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](https://www.quora.com/What-are-the-main-differences-between-artificial-intelligence-and-machine-learning/answer/Phillip-Rhodes)
  
  ### Artificial intelligence
  
  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 making[Q](https://www.quora.com/What-are-the-main-differences-between-artificial-intelligence-and-machine-learning/answer/Yuval-Ariav) which aims to simulate cognitive human-like functions such as problem-solving, decision making or language communication.
  
  ### Machine learning
  
  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](http://www.cs.colby.edu/srtaylor/) of Computer Science and her [lecture paper](http://cs.colby.edu/courses/S15/cs251/LectureNotes/Lecture_15_MLandDMintro_03_09_2015.pdf), and also [Wikipedia page](https://en.wikipedia.org/wiki/Learning#Machine_learning), '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](http://www.oxforddictionaries.com/definition/english/machine-learning), 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](https://www.quora.com/What-are-the-main-differences-between-artificial-intelligence-and-machine-learning/answer/Yuval-Ariav)
  
  ---
  
  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](https://www.quora.com/What-are-the-main-differences-between-artificial-intelligence-and-machine-learning/answer/Sakthi-Dasan-2)
  
  
  ---
  
  ### Answer by username_6 on 2017-06-30. Score: 11
  
  Machine learning is a subfield of artificial intelligence, as the following diagram (taken from [this blog post](https://www.linkedin.com/pulse/how-artificial-intelligence-different-from-machine-learning-singh)) illustrates.
  
  [![enter image description here](https://i.sstatic.net/5i2z7.png)](https://i.sstatic.net/5i2z7.png)
  
  
  ---
  
  ### Answer by username_7 on 2016-08-02. Score: 9
  
  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.)
  
  
  ---
  
  ### Answer by username_8 on 2018-09-27. Score: 2
  
  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.
  
  [![enter image description here](https://i.sstatic.net/OkLbE.jpg)](https://i.sstatic.net/OkLbE.jpg)
  
  
  ---
  
  ### Answer by username_9 on 2017-12-27. Score: 1
  
  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.
  
  
  ---
  
  ### Answer by username_10 on 2018-09-27. Score: 0
  
  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, username_4 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...
  
  
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