Answers.Body: <p>In my view learning is a useful aspect of an AI system, which makes it more resilient towards a changing environment, but it is not a requirement. Early AI systems (Expert systems, theorem solvers) would usually not use learning algorithms, but had a fixed knowledge base.</p>
  
  <p>It also works a lot better with <a href="https://en.wikipedia.org/wiki/Machine_learning" rel="nofollow noreferrer">numerical models</a> than with symbolic processing; the proliferation of training data which boosts the most recent AI wave (which is based more on statistical and numerical methods) has made it somewhat necessary for modern systems to have a learning/training component.</p>
  
  <p>However, one needs to distinguish between the initial training, which leads to a production system which then becomes fixed (and will not learn from new input) and those which have a learning component built in as an intrinsic part and will continue to adapt throughout their lifetime.</p>
  
  <p>A cautionary example is <a href="https://blogs.microsoft.com/blog/2016/03/25/learning-tays-introduction/" rel="nofollow noreferrer">Microsoft's "Tay" chatbot</a>. This was still actively learning from conversations and was quickly abused by subjecting it to undesirable input. Switching off the learning component would have been prudent in this case, but it would not have taken the AI out of it.</p>
  
  <p>So, to answer the question: learning is not a required part of an AI system once it is operational. While modern systems generally have some element of learning to build their initial models, older systems were simply hand-coded without any automated learning at all. But one would still regard them as AI system.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-11-22T09:21:46.700Z
Answers.LastActivityDate: 2018-11-22T09:21:46.700Z
Answers.UserDisplayName: Oliver Mason
Body: <p>This question assumes a definition of AI based on machine learning, and was inspired by this fun Technology Review post:</p>
  
  <p><a href="https://i.sstatic.net/pRyOW.jpg" rel="noreferrer"><img src="https://i.sstatic.net/pRyOW.jpg" alt="enter image description here"></a></p>
  
  <p><a href="https://www.technologyreview.com/s/612404/is-this-ai-we-drew-you-a-flowchart-to-work-it-out/?utm_campaign=owned_social&amp;utm_source=twitter.com&amp;utm_medium=social" rel="noreferrer"><sub>SOURCE: Is this AI? We drew you a flowchart to work it out (Karen Hao, MIT Technology Review)</sub></a></p>
  
  <p>As the definition of artificial intelligence has been a continual subject of discussion on this stack, I wanted to bring it to the community for perspectives.</p>
  
  <p>The formal question here is:</p>
  
  <ul>
  <li>Is an algorithm that is no longer actively learning an AI?</li>
  </ul>
  
  <p>Specifically, can applied algorithms that are not actively learning be said to reason?  </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-11-22T00:36:07.877Z
Comments.Text: Interesting question, but this boils down to the definition of AI.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-11-22T01:01:33.927Z
Comments.Text: @nbro it does, but asks specifically if learning is the requirement. For instance, is an NN that has been trained but is no longer actively learning an AI?  *(Interesting DeepMind post on [Enabling Continual Learning in Neural Networks](https://deepmind.com/blog/enabling-continual-learning-in-neural-networks/).)*
Comments.UserDisplayName: DukeZhou
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-11-22T09:36:34.657Z
Comments.Text: A usual there needs to be lines drawn between "classic AI", maybe "classic CI (computational intelligence)", "modern AI in practice", the (very vague) AGI. An AGI that could not learn would be severely hobbled - *Human: I am Bob. AGI: Sorry, I forgot, what was your name?* - unless you want to include memory but exclude learning, and the possible difference between the two would be an interesting question in its own right.
Comments.UserDisplayName: Neil Slater
Tags: <machine-learning><terminology><philosophy><definitions>
Title: Is an algorithm that is no longer actively learning an AI?
UserDisplayName: DukeZhou
ThreadText: # Is an algorithm that is no longer actively learning an AI?
  
  (Asked by: username_0 on 2018-11-22)
  
  This question assumes a definition of AI based on machine learning, and was inspired by this fun Technology Review post:
  
  [![enter image description here](https://i.sstatic.net/pRyOW.jpg)](https://i.sstatic.net/pRyOW.jpg)
  
  [SOURCE: Is this AI? We drew you a flowchart to work it out (Karen Hao, MIT Technology Review)](https://www.technologyreview.com/s/612404/is-this-ai-we-drew-you-a-flowchart-to-work-it-out/?utm_campaign=owned_social&utm_source=twitter.com&utm_medium=social)
  
  As the definition of artificial intelligence has been a continual subject of discussion on this stack, I wanted to bring it to the community for perspectives.
  
  The formal question here is:
  
  * Is an algorithm that is no longer actively learning an AI?
  
  Specifically, can applied algorithms that are not actively learning be said to reason?
  
  **Tags:** machine-learning, terminology, philosophy, definitions
  
  > **Comments:**
  > **username_1**: Interesting question, but this boils down to the definition of AI.  
  > **username_0**: @username_1 it does, but asks specifically if learning is the requirement. For instance, is an NN that has been trained but is no longer actively learning an AI?  *(Interesting DeepMind post on [Enabling Continual Learning in Neural Networks](https://deepmind.com/blog/enabling-continual-learning-in-neural-networks/).)*  
  > **username_2**: A usual there needs to be lines drawn between "classic AI", maybe "classic CI (computational intelligence)", "modern AI in practice", the (very vague) AGI. An AGI that could not learn would be severely hobbled - *Human: I am Bob. AGI: Sorry, I forgot, what was your name?* - unless you want to include memory but exclude learning, and the possible difference between the two would be an interesting question in its own right.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2018-11-22. Score: 3
  
  In my view learning is a useful aspect of an AI system, which makes it more resilient towards a changing environment, but it is not a requirement. Early AI systems (Expert systems, theorem solvers) would usually not use learning algorithms, but had a fixed knowledge base.
  
  It also works a lot better with [numerical models](https://en.wikipedia.org/wiki/Machine_learning) than with symbolic processing; the proliferation of training data which boosts the most recent AI wave (which is based more on statistical and numerical methods) has made it somewhat necessary for modern systems to have a learning/training component.
  
  However, one needs to distinguish between the initial training, which leads to a production system which then becomes fixed (and will not learn from new input) and those which have a learning component built in as an intrinsic part and will continue to adapt throughout their lifetime.
  
  A cautionary example is [Microsoft's "Tay" chatbot](https://blogs.microsoft.com/blog/2016/03/25/learning-tays-introduction/). This was still actively learning from conversations and was quickly abused by subjecting it to undesirable input. Switching off the learning component would have been prudent in this case, but it would not have taken the AI out of it.
  
  So, to answer the question: learning is not a required part of an AI system once it is operational. While modern systems generally have some element of learning to build their initial models, older systems were simply hand-coded without any automated learning at all. But one would still regard them as AI system.
  
  
  ---
  
  