Answers.Body: <p>Machine learning techniques are usually using a lot of statistical approaches, like <a href="https://en.wikipedia.org/wiki/Neural_network_software" rel="nofollow noreferrer">neural networks</a>: a book like <a href="https://www.cs.huji.ac.il/%7Eshais/UnderstandingMachineLearning/" rel="nofollow noreferrer">this one</a> (<em>Understanding Machine Learning: From Theory to Algorithms</em> ISBN 978-1-107-05713-5) is full of mathematical equations.</p>
  <p><a href="https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence" rel="nofollow noreferrer">Symbolic artificial intelligence</a>, e.g. classical <a href="https://en.wikipedia.org/wiki/Expert_system" rel="nofollow noreferrer">expert system</a> approaches (with some knowledge base), is more related to <em>logic</em>: a book like <a href="https://rads.stackoverflow.com/amzn/click/com/1848211015" rel="nofollow noreferrer" rel="nofollow noreferrer">that one</a> (<em>Artificial Beings: the Conscience of a Conscious Machine</em> ISBN 978 1848211018) has mostly no complex equations, but simple ones.</p>
  <p>An AI software can combine both approaches (in particular with meta-programming: programs that generate other programs; or even <a href="https://www.sciencedirect.com/science/article/abs/pii/0004370280900430" rel="nofollow noreferrer">meta-rules</a>): the <a href="http://refpersys.org/" rel="nofollow noreferrer">RefPerSys</a> project tries to do so.</p>
  <p>Artificial neural networks are somehow &quot;working&quot;, but you cannot understand why.</p>
  <p>Knowledge-based systems are more <a href="https://en.wikipedia.org/wiki/Explainable_artificial_intelligence" rel="nofollow noreferrer">explainable artificial intelligence</a>.</p>
  <p>Classical AI systems could <em>generate</em> code (in C, C++, Common Lisp, machine code) using <a href="https://en.wikipedia.org/wiki/Metaprogramming" rel="nofollow noreferrer">metaprogramming</a> techniques, and could be mixed with machine learning or <a href="https://en.wikipedia.org/wiki/Deep_learning" rel="nofollow noreferrer">deep learning</a> approaches, and/or use existing machine learning libraries.</p>
  <p>Notice that the difference between statistical AI and classical AI is <em>not</em> a matter of programming languages or of operating systems. For example, <a href="https://en.wikipedia.org/wiki/Garbage_collection_(computer_science)" rel="nofollow noreferrer">garbage collection</a> may be (and perhaps is not) relevant to both approaches.</p>
  
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Answers.Body: <p><strong>Statistical AI</strong>, arising from machine learning, tends to be more concerned with <em>inductive</em> thought: given a set of patterns, induce the trend.</p>
  <p><strong>Classical AI</strong> is the branch of artificial intelligence research that concerns itself with attempting to explicitly represent human knowledge in a <em>declarative</em> form, i.e given a set of constraints, <em>deduce</em> a conclusion.</p>
  <p>Another difference is that C++, Python, and R tend to be a favorite language for statistical AI, while LISP and PROLOG dominate in classical AI.</p>
  <p>A system can't be more intelligent without displaying properties of both inductive and deductive thought. This leads many to believe that, in the end, there will be some kind of synthesis of statistical and classical AI.</p>
  
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Answers.Text: Many people are coding AI software in Ocaml or Haskell or C.
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Body: <p>What are the differences in scope between statistical AI and <a href="https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence" rel="nofollow noreferrer">classical AI</a>?</p>
  
  <p>Real-world examples would be appreciated.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-04T16:48:14.303Z
Comments.Text: Please add context to your question. What do you mean by "statistical AI" and "classical AI"? These are not conventional terms. With regards to the former, do you mean statistical learning?
Comments.UserDisplayName: The Pointer
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Comments.CreationDate: 2018-11-05T21:28:27.347Z
Comments.Text: See also [What are some examples of Statistical AI applications?](https://ai.stackexchange.com/q/81/2444) and [What are some examples of Classical AI applications?](https://ai.stackexchange.com/q/103/2444).
Comments.UserDisplayName: DukeZhou
Tags: <comparison><statistical-ai><symbolic-ai>
Title: What are the differences in scope between statistical AI and classical AI?
UserDisplayName: dua fatima
ThreadText: # What are the differences in scope between statistical AI and classical AI?
  
  (Asked by: username_0 on 2018-11-05)
  
  What are the differences in scope between statistical AI and [classical AI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)?
  
  Real-world examples would be appreciated.
  
  **Tags:** comparison, statistical-ai, symbolic-ai
  
  > **Comments:**
  > **username_2**: See also [What are some examples of Statistical AI applications?](https://ai.stackexchange.com/q/81/2444) and [What are some examples of Classical AI applications?](https://ai.stackexchange.com/q/103/2444).  
  > **username_1**: Please add context to your question. What do you mean by "statistical AI" and "classical AI"? These are not conventional terms. With regards to the former, do you mean statistical learning?  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_4 on 2018-11-05. Score: 4 | Accepted answer
  
  **Statistical AI**, arising from machine learning, tends to be more concerned with *inductive* thought: given a set of patterns, induce the trend.
  
  **Classical AI** is the branch of artificial intelligence research that concerns itself with attempting to explicitly represent human knowledge in a *declarative* form, i.e given a set of constraints, *deduce* a conclusion.
  
  Another difference is that C++, Python, and R tend to be a favorite language for statistical AI, while LISP and PROLOG dominate in classical AI.
  
  A system can't be more intelligent without displaying properties of both inductive and deductive thought. This leads many to believe that, in the end, there will be some kind of synthesis of statistical and classical AI.
  
  > **Comments:**
  > **username_3**: Many people are coding AI software in Ocaml or Haskell or C.  
  
  ---
  
  ### Answer by username_3 on 2021-02-04. Score: 1
  
  Machine learning techniques are usually using a lot of statistical approaches, like [neural networks](https://en.wikipedia.org/wiki/Neural_network_software): a book like [this one](https://www.cs.huji.ac.il/%7Eshais/UnderstandingMachineLearning/) (*Understanding Machine Learning: From Theory to Algorithms* ISBN 978-1-107-05713-5) is full of mathematical equations.
  
  [Symbolic artificial intelligence](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence), e.g. classical [expert system](https://en.wikipedia.org/wiki/Expert_system) approaches (with some knowledge base), is more related to *logic*: a book like [that one](https://rads.stackoverflow.com/amzn/click/com/1848211015) (*Artificial Beings: the Conscience of a Conscious Machine* ISBN 978 1848211018) has mostly no complex equations, but simple ones.
  
  An AI software can combine both approaches (in particular with meta-programming: programs that generate other programs; or even [meta-rules](https://www.sciencedirect.com/science/article/abs/pii/0004370280900430)): the [RefPerSys](http://refpersys.org/) project tries to do so.
  
  Artificial neural networks are somehow "working", but you cannot understand why.
  
  Knowledge-based systems are more [explainable artificial intelligence](https://en.wikipedia.org/wiki/Explainable_artificial_intelligence).
  
  Classical AI systems could *generate* code (in C, C++, Common Lisp, machine code) using [metaprogramming](https://en.wikipedia.org/wiki/Metaprogramming) techniques, and could be mixed with machine learning or [deep learning](https://en.wikipedia.org/wiki/Deep_learning) approaches, and/or use existing machine learning libraries.
  
  Notice that the difference between statistical AI and classical AI is *not* a matter of programming languages or of operating systems. For example, [garbage collection](https://en.wikipedia.org/wiki/Garbage_collection_(computer_science)) may be (and perhaps is not) relevant to both approaches.
  
  
  ---
  
  