This summary of already superhuman game playing AIs impressed me since two weeks. But only until yesterday. John McCarthy was attributed in Vardi(2012) to have said: “As soon as it works, no one calls it AI anymore.” (p13)
There is more truth in it than McCarthy expected it to be:
A tailor made game playing algorithm, developed and optimized by generations of scientists and software engineers is no entity of AI. It is an algorithm. Human beings analyzed the rule set, found abstractions of it, developed evaluation schemes and found heuristics to prune the un-computable large search tree. With brute force and megawatts of computational evaluation power they managed to fill a database with millions of more or less favorable game situations. In direct competion of game playing algorithm vs. human being these pre-computed situations help to find short cuts in the tree search to achieve superhuman performance in the end.
Is this entity an AI or an algorithm?
Game concept development: human.
Game rule definition and negotiation: human.
Game rule abstraction and translation into computable form: human designed algorithm.
Evaluation of game situation: human designed algorithm, computer aided optimization.
Search tree heuristics: human designed algorithm, computer aided optimization.
Database of favorable situations and moves: brute force tree search on massive parallel supercomputer.
Detection of favorable situations: human designed algorithm for pattern matching, computer aided optimization.
Active playing: Full automatic use of algorithms and information of points 3-7. No human being involved.
Unsupervised learning, search optimization and pattern matching (points 5-7) make this class of entities weak AIs. A human being playing against this entity will probably attribute intelligence to it. “Kasparov claims to have seen glimpses of true intelligence and creativity in some of the computers moves” (p12, Newborn[2011]).
But weak AI is not our focus. Our focus is strong AI, HLAI and superintelligence. It is good to know that human engineered weak AI algorithms can achieve superhuman performance. Not a single game playing weak AI achieved human level of intelligence. The following story will show why:
Watch two children, Alice and Bob, playing in the street. They found white and black pebbles and a piece of chalk. Bob has a faint idea of checkers (other names: “draught” or German: “Dame”) from having seen his elder brother playing it. He explains to Alice: “Lets draw a grid of chalk lines on the road and place our pebbles into the fields. I will show you.” In joint effort they draw several strait lines resulting in a 7x9 grid. Then Bob starts to place his black pebbles into his starting rows as he remembered it. Alice follows suit—but she has not enough white pebbles to fill her starting rows. They discuss their options and searched for more white pebbles. After two minutes of unsuccessful search Bob said: “Lets remove one column and I take two of my black pebbles away.” Then Bob explained to Alice how to make moves with her pebbles on the now smaller 7x8 board game grid. They started playing and enjoyed their time. Bob did win most of the games. He changed the rules to give Alice a starting advantage. Alice did not care losing frequently. They laughed a lot. She loves Bob and is happy for every minute being next to him.
This is a real game. It is a full body experience with all senses. These young children manipulate their material world, create and modify abstract rules, develop strategies for winning, communicate and have fun together.
The German Wikipedia entry for “Dame_(Spiel)” lists 3 4 4 (3 + many more) 2 = 288+ orthogonal rule variants. Playing Doppelkopf (popular 4-player card game in Germany) with people you have never played with takes at least five minutes discussion about the rules in the beginning. This demonstrates that developing and negotiating rules is central part of human game play.
If you would tell 10 year old Bob: “Alice has to go home with me for lunch. Look, this is Roboana (a strong AI robot), play with her instead.” You guide your girl-alike robot to Bob.
Roboana: “Hi, I’m Roboana, I saw you playing with Alice. It seems to be very funny. What is the game about?”
You, member of the Roboana development team, leave the scene for lunch. Will your maybe-HLAI robot manage the situation with Bob? Will Roboana modify the rules to balance the game if her strategy is too superior before Bob gets annoyed and walks away? Will Bob enjoy his time with Roboana?
Bob is assumingly 10 years old and qualifies only for sub human intelligence. Within the next 20 years I do not expect any artificial entity to reach this level of general intelligence. To know that algorithms meet the core performance for game play is only the smallest part of the problem. Therefore I prefer calling weak AI what it is: Algorithm.
In our further reading we should try not to forget that aspects of creativity, engineering, programming and social interaction are in most cases more complex than the core problem. Some rules are imprinted into us human beings: how a face looks like, how a fearful face looks like, how a fearful mother smells, how to smile to please, how to scream to alert the mother, how spit out bitter tasting food to protect against intoxication. To play with the environment is imprinted into our brains as well. We enjoy to manipulate things and observe with our fullest curiosity its outcome. A game is a regulated kind of play. For AI development it is worth to widen the focus from game to playing.
This summary of already superhuman game playing AIs impressed me since two weeks. But only until yesterday. John McCarthy was attributed in Vardi(2012) to have said: “As soon as it works, no one calls it AI anymore.” (p13)
There is more truth in it than McCarthy expected it to be: A tailor made game playing algorithm, developed and optimized by generations of scientists and software engineers is no entity of AI. It is an algorithm. Human beings analyzed the rule set, found abstractions of it, developed evaluation schemes and found heuristics to prune the un-computable large search tree. With brute force and megawatts of computational evaluation power they managed to fill a database with millions of more or less favorable game situations. In direct competion of game playing algorithm vs. human being these pre-computed situations help to find short cuts in the tree search to achieve superhuman performance in the end.
Is this entity an AI or an algorithm?
Game concept development: human.
Game rule definition and negotiation: human.
Game rule abstraction and translation into computable form: human designed algorithm.
Evaluation of game situation: human designed algorithm, computer aided optimization.
Search tree heuristics: human designed algorithm, computer aided optimization.
Database of favorable situations and moves: brute force tree search on massive parallel supercomputer.
Detection of favorable situations: human designed algorithm for pattern matching, computer aided optimization.
Active playing: Full automatic use of algorithms and information of points 3-7. No human being involved.
Unsupervised learning, search optimization and pattern matching (points 5-7) make this class of entities weak AIs. A human being playing against this entity will probably attribute intelligence to it. “Kasparov claims to have seen glimpses of true intelligence and creativity in some of the computers moves” (p12, Newborn[2011]).
But weak AI is not our focus. Our focus is strong AI, HLAI and superintelligence. It is good to know that human engineered weak AI algorithms can achieve superhuman performance. Not a single game playing weak AI achieved human level of intelligence. The following story will show why:
Watch two children, Alice and Bob, playing in the street. They found white and black pebbles and a piece of chalk. Bob has a faint idea of checkers (other names: “draught” or German: “Dame”) from having seen his elder brother playing it. He explains to Alice: “Lets draw a grid of chalk lines on the road and place our pebbles into the fields. I will show you.” In joint effort they draw several strait lines resulting in a 7x9 grid. Then Bob starts to place his black pebbles into his starting rows as he remembered it. Alice follows suit—but she has not enough white pebbles to fill her starting rows. They discuss their options and searched for more white pebbles. After two minutes of unsuccessful search Bob said: “Lets remove one column and I take two of my black pebbles away.” Then Bob explained to Alice how to make moves with her pebbles on the now smaller 7x8 board game grid. They started playing and enjoyed their time. Bob did win most of the games. He changed the rules to give Alice a starting advantage. Alice did not care losing frequently. They laughed a lot. She loves Bob and is happy for every minute being next to him.
This is a real game. It is a full body experience with all senses. These young children manipulate their material world, create and modify abstract rules, develop strategies for winning, communicate and have fun together.
The German Wikipedia entry for “Dame_(Spiel)” lists 3 4 4 (3 + many more) 2 = 288+ orthogonal rule variants. Playing Doppelkopf (popular 4-player card game in Germany) with people you have never played with takes at least five minutes discussion about the rules in the beginning. This demonstrates that developing and negotiating rules is central part of human game play.
If you would tell 10 year old Bob: “Alice has to go home with me for lunch. Look, this is Roboana (a strong AI robot), play with her instead.” You guide your girl-alike robot to Bob.
Roboana: “Hi, I’m Roboana, I saw you playing with Alice. It seems to be very funny. What is the game about?”
You, member of the Roboana development team, leave the scene for lunch. Will your maybe-HLAI robot manage the situation with Bob? Will Roboana modify the rules to balance the game if her strategy is too superior before Bob gets annoyed and walks away? Will Bob enjoy his time with Roboana?
Bob is assumingly 10 years old and qualifies only for sub human intelligence. Within the next 20 years I do not expect any artificial entity to reach this level of general intelligence. To know that algorithms meet the core performance for game play is only the smallest part of the problem. Therefore I prefer calling weak AI what it is: Algorithm.
In our further reading we should try not to forget that aspects of creativity, engineering, programming and social interaction are in most cases more complex than the core problem. Some rules are imprinted into us human beings: how a face looks like, how a fearful face looks like, how a fearful mother smells, how to smile to please, how to scream to alert the mother, how spit out bitter tasting food to protect against intoxication. To play with the environment is imprinted into our brains as well. We enjoy to manipulate things and observe with our fullest curiosity its outcome. A game is a regulated kind of play. For AI development it is worth to widen the focus from game to playing.
Now we have something! We have something we can actually use! AI must be able to interact with emotional intelligence!