Answers.Body: <p>In addition to the answers already posted, I think IBM's <a href="http://ibm.com/watson" rel="nofollow noreferrer">Watson</a> deserves a mention.  It did something pretty impressive with its Jeopardy win, possibly as impressive as AlphaGo.  Sadly, since then, there don't seem to have been a lot of really public demos of Watson, as IBM is positioning the technology as a tool for companies and other organizations, and most of them are pretty secretive about the details of what they're doing.  I think they did publicize a bit of information about using it for medical diagnosis, but that's the only other application I can think of off hand.  I'm sure there are more though.</p>
  
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Answers.Text: The thing with Watson's Jeopardy win is that the questions came in basically two types: search-engine friendly and human-reasoning friendly.  Watson did well with the search-engine friendly.  The success of the human-reasoning friendly was divided among the two human contestants, so neither was able to out-score Watson.  It was more a triumph of marketing than machine intelligence.
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Answers.Body: <p><strong>AlphaGo</strong> is the most sophisticated Artificial Intelligence program created by humans. It is a computer program that is <strong>developed by Google DeepMind</strong> to play the board game &quot;Go&quot;. The game is different than other games, as <strong>The number of potential legal board positions is greater than the number of atoms in the universe</strong>. It has way more legal board positions than the chess. So, <em>AlphaGo</em> requires different technique for it's development.<br><br>
  Program's victories against the best players in the world in March 2016 <strong>is considered a major break through</strong> in the field of AI. Go was previously considered to be a hard problem and many experts believed that current technology is not enough. Experts were saying that it will take atleast 5 years (or may be 10 years) before we will have a well developed Go software player.<br><br>
  The game used sophisticated algorithms of deep learning and reinforcement learning in order to learn the game. What makes this game different from other board game (like Chess, Reversi, etc.) is that moves are often based on intuition. If you ask a Chess player why he make a certain move, you will always be hearing an answer where he will explain you how he thought this move can increase in change of winning. Every move uses certain heuristics, strategy and/ or tricks. This is not the case with Go. Some moves are often taken because of intuition. Coding an AI software that can play a game, where intuition is a integral part of the game makes it different from other AIs that we have today.<br></p>
  <blockquote>
  <p>At present AlphaGo is the closest AI software to Artificial General Intelligence.</p>
  </blockquote>
  <p>You can go through these links for more information:<br></p>
  <ol>
  <li><a href="https://en.wikipedia.org/wiki/AlphaGo" rel="nofollow noreferrer">First</a><br></li>
  <li><a href="https://deepmind.com/research/alphago/" rel="nofollow noreferrer">Second</a></li>
  </ol>
  
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Answers.Text: If you don't edit this answer to fix the issues and **wrong** conclusions and assumptions (such as that AlphaGo is a step towards AGI), I will delete it (disinformation is not allowed here). AlphaGo, as someone already suggested, is a **narrow AI** system. It's still a good example of an "advanced AI" (at least, up until now), so you don't have to delete this answer, but just clarify that AlphaGo is a **narrow AI** system.
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Answers.Text: Your last edit is insufficient. Note that, after AlphaGo, we had AlphaZero, AlphaGo Zero, MuZero, etc., so maybe AlphaGo is not the most advanced AI system at this point. Also, you still have the sentence "At present AlphaGo is the closest AI software to Artificial General Intelligence.", which I would say is not true, though I wouldn't know which system currently is the closest to an AGI system.
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Answers.Text: As far as I'm aware, none of the researchers in the fields of Deep Learning and Monte Carlo Tree Search (the algorithms behind Alpha Go) use the rather ill-defined term 'intuition' to explain the operation of their algorithms.
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Answers.Text: AGI is (by definition) general, i.e. it works across a range of different environments without requiring human intervention to do so. This is not the case for AlphaGo.
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Answers.Text: You say "an AI software that can make a decision due to intuition makes it way different". I am saying that nowhere in the usual explanation of DL or MCTS does anyone use the term 'intuition'. See Hofstadter's "The ineradicable Eliza effect" for why introducing such terms to 'explain' otherwise well-understood mechanisms is not a good idea.
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Answers.Text: @NietzscheanAI. I remember watching a youtube video where it said that intuition is a key part of the software. Also the software was given a video camera and left in a store to observe the outside world. The stuff he did was basic, but was able to do task which were linked to AGI.
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Answers.Text: You need to back those claims up with some concrete evidence.
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Answers.Text: Interestingly, DeepMind claims that applying the method behind AlphaGo to data centers' electricity consumption allows saving energy (40%) automatically (see Hassabis on YouTube at the Riyal Society 2016). I found no hard detail yet, and I bet the utility function must be manually defined. Although impressive, useful, this is still as advanced as most current systems against the AGI idea.
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Answers.Body: <p>In my opinion this would be the <a href="https://en.wikipedia.org/wiki/Google_Search" rel="nofollow noreferrer">Google search engine</a>.</p>
  
  <p>It searches the web.</p>
  
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Answers.Text: The answer has got negative response, probably because it's too brief. But as an ex-Googler, I tend to agree and have to add a few words here. Google search engine runs hundreds of machine-learning jobs for scoring, so it can be easily called AI and it's very sophisticated in terms of performance and parallelization. It's hard compare it to Watson, but search results are so impressive, that Google search deserves an honorable mention in this list.
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Answers.Body: <p>In my opinion, this would be <a href="http://www.foundalis.com/res/diss_research.html" rel="noreferrer">Phaeaco</a>, which was developed by Harry Foundalis at Douglas Hofstadter's CRCC research group.</p>
  
  <p>It takes noisy photographic images of <a href="https://www.theguardian.com/science/2016/apr/25/can-you-solve-it-bongard-picture-puzzles-that-will-bongo-with-your-brain" rel="noreferrer">Bongard problems</a> as input and (using a variant of Hofstadter's 'Fluid Concepts' architecture) successfully deduces the required rule in many cases.</p>
  
  <p>Hofstadter has described the related success of <a href="https://en.wikipedia.org/wiki/Copycat_(software)" rel="noreferrer">CopyCat</a> as being 'like a little kid doing a somersault': i.e. it doesn't have the flashy appeal of systems like AlphaGo. What it <em>does</em> however have is a much more flexible (i.e. not precanned) approach to perception of problem structure than other systems, which Hofstadter claims (and many including Peter Norvig agree) is <em>the really hard problem</em>.</p>
  
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Body: <p>What is the most advanced AI software/system that humans have made to date, and what does it do?</p>
  
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Comments.Text: The problem with this question is that you do not clarify if you're looking for AI systems that are very good at one task (**narrow AI systems**) or that attempt to be good at multiple different tasks (**general AI systems**). So, some people will answer by saying _AlphaGo_, because it beat humans at a combinatorial game with many combinations, while, at the same time, it's completely useless at solving most other problems in the world, though it could potentially be used to solve other similar problems, such as chess (this has already been done with AlphaZero, a successor of AlphaGo).
Comments.UserDisplayName: nbro
Tags: <agi><research><state-of-the-art><narrow-ai>
Title: What is the most sophisticated AI ever made?
UserDisplayName: Lord Thanatos
ThreadText: # What is the most sophisticated AI ever made?
  
  (Asked by: username_0 on 2016-11-06)
  
  What is the most advanced AI software/system that humans have made to date, and what does it do?
  
  **Tags:** agi, research, state-of-the-art, narrow-ai
  > **Comments:**
  > **username_1**: The problem with this question is that you do not clarify if you're looking for AI systems that are very good at one task (**narrow AI systems**) or that attempt to be good at multiple different tasks (**general AI systems**). So, some people will answer by saying _AlphaGo_, because it beat humans at a combinatorial game with many combinations, while, at the same time, it's completely useless at solving most other problems in the world, though it could potentially be used to solve other similar problems, such as chess (this has already been done with AlphaZero, a successor of AlphaGo).  
  
  ---
  
  ## 4 answers
  
  ### Answer by username_5 on 2016-11-06. Score: 11 | Accepted answer
  
  In my opinion, this would be [Phaeaco](http://www.foundalis.com/res/diss_research.html), which was developed by Harry Foundalis at Douglas Hofstadter's CRCC research group.
  
  It takes noisy photographic images of [Bongard problems](https://www.theguardian.com/science/2016/apr/25/can-you-solve-it-bongard-picture-puzzles-that-will-bongo-with-your-brain) as input and (using a variant of Hofstadter's 'Fluid Concepts' architecture) successfully deduces the required rule in many cases.
  
  Hofstadter has described the related success of [CopyCat](https://en.wikipedia.org/wiki/Copycat_(software)) as being 'like a little kid doing a somersault': i.e. it doesn't have the flashy appeal of systems like AlphaGo. What it *does* however have is a much more flexible (i.e. not precanned) approach to perception of problem structure than other systems, which Hofstadter claims (and many including Peter Norvig agree) is *the really hard problem*.
  
  
  ---
  
  ### Answer by username_2 on 2016-11-10. Score: 4
  
  In addition to the answers already posted, I think IBM's [Watson](http://ibm.com/watson) deserves a mention. It did something pretty impressive with its Jeopardy win, possibly as impressive as AlphaGo. Sadly, since then, there don't seem to have been a lot of really public demos of Watson, as IBM is positioning the technology as a tool for companies and other organizations, and most of them are pretty secretive about the details of what they're doing. I think they did publicize a bit of information about using it for medical diagnosis, but that's the only other application I can think of off hand. I'm sure there are more though.
  
  > **Comments:**
  > **username_3**: The thing with Watson's Jeopardy win is that the questions came in basically two types: search-engine friendly and human-reasoning friendly.  Watson did well with the search-engine friendly.  The success of the human-reasoning friendly was divided among the two human contestants, so neither was able to out-score Watson.  It was more a triumph of marketing than machine intelligence.  
  
  ---
  
  ### Answer by username_4 on 2016-11-07. Score: 2
  
  **AlphaGo** is the most sophisticated Artificial Intelligence program created by humans. It is a computer program that is **developed by Google DeepMind** to play the board game "Go". The game is different than other games, as **The number of potential legal board positions is greater than the number of atoms in the universe**. It has way more legal board positions than the chess. So, *AlphaGo* requires different technique for it's development.  
    
  Program's victories against the best players in the world in March 2016 **is considered a major break through** in the field of AI. Go was previously considered to be a hard problem and many experts believed that current technology is not enough. Experts were saying that it will take atleast 5 years (or may be 10 years) before we will have a well developed Go software player.  
    
  The game used sophisticated algorithms of deep learning and reinforcement learning in order to learn the game. What makes this game different from other board game (like Chess, Reversi, etc.) is that moves are often based on intuition. If you ask a Chess player why he make a certain move, you will always be hearing an answer where he will explain you how he thought this move can increase in change of winning. Every move uses certain heuristics, strategy and/ or tricks. This is not the case with Go. Some moves are often taken because of intuition. Coding an AI software that can play a game, where intuition is a integral part of the game makes it different from other AIs that we have today.
  
  > At present AlphaGo is the closest AI software to Artificial General Intelligence.
  
  You can go through these links for more information:
  
  1. [First](https://en.wikipedia.org/wiki/AlphaGo)
  2. [Second](https://deepmind.com/research/alphago/)
  
  > **Comments:**
  > **username_5**: As far as I'm aware, none of the researchers in the fields of Deep Learning and Monte Carlo Tree Search (the algorithms behind Alpha Go) use the rather ill-defined term 'intuition' to explain the operation of their algorithms.  
  > **username_5**: AGI is (by definition) general, i.e. it works across a range of different environments without requiring human intervention to do so. This is not the case for AlphaGo.  
  > **username_5**: You say "an AI software that can make a decision due to intuition makes it way different". I am saying that nowhere in the usual explanation of DL or MCTS does anyone use the term 'intuition'. See Hofstadter's "The ineradicable Eliza effect" for why introducing such terms to 'explain' otherwise well-understood mechanisms is not a good idea.  
  > **username_4**: @NietzscheanAI. I remember watching a youtube video where it said that intuition is a key part of the software. Also the software was given a video camera and left in a store to observe the outside world. The stuff he did was basic, but was able to do task which were linked to AGI.  
  > **username_5**: You need to back those claims up with some concrete evidence.  
  > **username_6**: Interestingly, DeepMind claims that applying the method behind AlphaGo to data centers' electricity consumption allows saving energy (40%) automatically (see Hassabis on YouTube at the Riyal Society 2016). I found no hard detail yet, and I bet the utility function must be manually defined. Although impressive, useful, this is still as advanced as most current systems against the AGI idea.  
  > **username_1**: If you don't edit this answer to fix the issues and **wrong** conclusions and assumptions (such as that AlphaGo is a step towards AGI), I will delete it (disinformation is not allowed here). AlphaGo, as someone already suggested, is a **narrow AI** system. It's still a good example of an "advanced AI" (at least, up until now), so you don't have to delete this answer, but just clarify that AlphaGo is a **narrow AI** system.  
  > **username_1**: Your last edit is insufficient. Note that, after AlphaGo, we had AlphaZero, AlphaGo Zero, MuZero, etc., so maybe AlphaGo is not the most advanced AI system at this point. Also, you still have the sentence "At present AlphaGo is the closest AI software to Artificial General Intelligence.", which I would say is not true, though I wouldn't know which system currently is the closest to an AGI system.  
  
  ---
  
  ### Answer by username_7 on 2016-11-07. Score: 0
  
  In my opinion this would be the [Google search engine](https://en.wikipedia.org/wiki/Google_Search).
  
  It searches the web.
  
  > **Comments:**
  > **username_8**: The answer has got negative response, probably because it's too brief. But as an ex-Googler, I tend to agree and have to add a few words here. Google search engine runs hundreds of machine-learning jobs for scoring, so it can be easily called AI and it's very sophisticated in terms of performance and parallelization. It's hard compare it to Watson, but search results are so impressive, that Google search deserves an honorable mention in this list.  
  
  ---
  
  