Answers.Body: <p>Concentration, perhaps easier to grasp as "focus" or "attention", has quite some history in AI. <a href="https://ai.stackexchange.com/a/1440/2444">This answer</a> mentions CopyCat, and there was work with neural networks in the 80s as well (e.g. from <a href="http://link.springer.com/article/10.1007%2FBF00363973" rel="nofollow noreferrer">Fukushima</a>, creator of the Neocognitron).</p>
  
  <p>More recently, <em>attention</em> in neural networks is <a href="http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/" rel="nofollow noreferrer">gaining momentum</a>. The mechanisms are applied to learning in deep neural networks.</p>
  
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Answers.Body: <p>Douglas Hofstadter's <a href="https://pcl.sitehost.iu.edu/rgoldsto/courses/concepts/copycat.pdf" rel="nofollow noreferrer">CopyCat</a> architecture for solving letter-string analogy problems was deliberately engineered to maintain a semantically-informed notion of 'salience', i.e. given a variety of competing possibilities, tend to maintain interest in the one that is most compelling. Although the salience value of (part of) a solution is ultimately represented numerically, the means by which it determined is broadly intended to correspond (at least functionally) to the way 'selective attention' might operate in human cognition.</p>
  
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Body: <p>Humans can do multiple tasks at the same (e.g. reading while listening to music), but we memorize information from less focused sources with worse efficiency than we do from our main focus or task.</p>
  
  <p>Do such things exist in the case of artificial intelligence? I doubt, for example, that neural networks have such characteristics, but I may be wrong.</p>
  
Tags: <neural-networks><reference-request><attention>
Title: Is there any artificial intelligence that possesses "concentration"?
UserDisplayName: Zoltán Schmidt
ThreadText: # Is there any artificial intelligence that possesses "concentration"?
  
  (Asked by: username_0 on 2016-08-06)
  
  Humans can do multiple tasks at the same (e.g. reading while listening to music), but we memorize information from less focused sources with worse efficiency than we do from our main focus or task.
  
  Do such things exist in the case of artificial intelligence? I doubt, for example, that neural networks have such characteristics, but I may be wrong.
  
  **Tags:** neural-networks, reference-request, attention
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2016-08-07. Score: 8 | Accepted answer
  
  Douglas Hofstadter's [CopyCat](https://pcl.sitehost.iu.edu/rgoldsto/courses/concepts/copycat.pdf) architecture for solving letter-string analogy problems was deliberately engineered to maintain a semantically-informed notion of 'salience', i.e. given a variety of competing possibilities, tend to maintain interest in the one that is most compelling. Although the salience value of (part of) a solution is ultimately represented numerically, the means by which it determined is broadly intended to correspond (at least functionally) to the way 'selective attention' might operate in human cognition.
  
  
  ---
  
  ### Answer by username_1 on 2016-08-07. Score: 6
  
  Concentration, perhaps easier to grasp as "focus" or "attention", has quite some history in AI. [This answer](https://ai.stackexchange.com/a/1440/2444) mentions CopyCat, and there was work with neural networks in the 80s as well (e.g. from [Fukushima](http://link.springer.com/article/10.1007%2FBF00363973), creator of the Neocognitron).
  
  More recently, *attention* in neural networks is [gaining momentum](http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/). The mechanisms are applied to learning in deep neural networks.
  
  
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