Answers.Body: <p>Here is an initial list of AI systems that used neural networks and have achieved human-level or superhuman performance. All of these systems are reinforcement learning systems that play videogames.</p>
  <ul>
  <li><a href="https://www.nature.com/articles/nature16961.pdf" rel="nofollow noreferrer">AlphaGo</a> and <a href="https://discovery.ucl.ac.uk/id/eprint/10045895/1/agz_unformatted_nature.pdf" rel="nofollow noreferrer">AlphaGo Zero</a> (an improved version of AlphaGo that does not use human knowledge but learns by playing against itself) have achieved superhuman performance in the game of <strong>go</strong> and, in the case of <a href="https://arxiv.org/pdf/1712.01815.pdf" rel="nofollow noreferrer">AlphaZero</a> (a generalized version of AlphaGo Zero), also in the games of <strong>chess</strong> and <strong>shogi</strong>.</li>
  <li><a href="https://www.cs.toronto.edu/%7Evmnih/docs/dqn.pdf" rel="nofollow noreferrer">DQN</a> has achieved human-level or superhuman performance in many <strong>Atari games</strong></li>
  <li><a href="https://science.sciencemag.org/content/356/6337/508" rel="nofollow noreferrer">DeepStack</a> has achieved human-level performance in poker</li>
  <li><a href="https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii" rel="nofollow noreferrer">AlphaStar</a> defeated a top professional player in the real-time strategy game <strong>StarCraft 2</strong></li>
  <li><a href="https://arxiv.org/pdf/1912.06680.pdf" rel="nofollow noreferrer">OpenAI Five</a> defeated world champions in the game of <strong>Dota</strong></li>
  </ul>
  
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Answers.Text: It's worth understanding that neither alphaStar nor OpenAI Five were able to actually visually understand the games they are playing. They are fed numeric information such as coordinates and unit type. This is significant as visual processing of the game is as important as anything else.
Answers.UserDisplayName: FourierFlux
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Answers.Text: @FourierFlux Thanks for noticing this. To be honest, I am only familiar with the details of DQN and some details of AlphaGo, but not the details of the other two.
Answers.UserDisplayName: nbro
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Answers.Body: <p><strong>AlphaDogfight</strong> - from <a href="https://en.wikipedia.org/wiki/DARPA" rel="nofollow noreferrer">Defense Advanced Research Projects Agency (DARPA)</a> a programme that pitted computers using F-16 flight simulators against one another and later went on to defeat Air Force’s top F-16 fighter pilots.</p>
  <p>Check out <a href="https://www.darpa.mil/news-events/2020-08-26" rel="nofollow noreferrer">this</a> and <a href="https://www.darpa.mil/news-events/2020-08-07" rel="nofollow noreferrer">this</a> news and events by DARPA.</p>
  
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Body: <p>What are examples of problems where <strong>neural networks</strong> have been used and have achieved human-level or higher performance?</p>
  <p>Each answer can contain one or more examples. Please, provide links to research papers or reliable articles that validate your claims.</p>
  
Tags: <neural-networks><reference-request><applications><social>
Title: What are examples of problems where neural networks have achieved human-level or higher performance?
UserDisplayName: nbro
ThreadText: # What are examples of problems where neural networks have achieved human-level or higher performance?
  
  (Asked by: username_0 on 2020-08-30)
  
  What are examples of problems where **neural networks** have been used and have achieved human-level or higher performance?
  
  Each answer can contain one or more examples. Please, provide links to research papers or reliable articles that validate your claims.
  
  **Tags:** neural-networks, reference-request, applications, social
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_0 on 2020-08-30. Score: 2
  
  Here is an initial list of AI systems that used neural networks and have achieved human-level or superhuman performance. All of these systems are reinforcement learning systems that play videogames.
  
  * [AlphaGo](https://www.nature.com/articles/nature16961.pdf) and [AlphaGo Zero](https://discovery.ucl.ac.uk/id/eprint/10045895/1/agz_unformatted_nature.pdf) (an improved version of AlphaGo that does not use human knowledge but learns by playing against itself) have achieved superhuman performance in the game of **go** and, in the case of [AlphaZero](https://arxiv.org/pdf/1712.01815.pdf) (a generalized version of AlphaGo Zero), also in the games of **chess** and **shogi**.
  * [DQN](https://www.cs.toronto.edu/%7Evmnih/docs/dqn.pdf) has achieved human-level or superhuman performance in many **Atari games**
  * [DeepStack](https://science.sciencemag.org/content/356/6337/508) has achieved human-level performance in poker
  * [AlphaStar](https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii) defeated a top professional player in the real-time strategy game **StarCraft 2**
  * [OpenAI Five](https://arxiv.org/pdf/1912.06680.pdf) defeated world champions in the game of **Dota**
  
  > **Comments:**
  > **username_1**: It's worth understanding that neither alphaStar nor OpenAI Five were able to actually visually understand the games they are playing. They are fed numeric information such as coordinates and unit type. This is significant as visual processing of the game is as important as anything else.  
  > **username_0**: @username_1 Thanks for noticing this. To be honest, I am only familiar with the details of DQN and some details of AlphaGo, but not the details of the other two.  
  
  ---
  
  ### Answer by username_2 on 2020-09-15. Score: 1
  
  **AlphaDogfight** - from [Defense Advanced Research Projects Agency (DARPA)](https://en.wikipedia.org/wiki/DARPA) a programme that pitted computers using F-16 flight simulators against one another and later went on to defeat Air Force’s top F-16 fighter pilots.
  
  Check out [this](https://www.darpa.mil/news-events/2020-08-26) and [this](https://www.darpa.mil/news-events/2020-08-07) news and events by DARPA.
  
  
  ---
  
  