The Experts below are selected from a list of 3966 Experts worldwide ranked by ideXlab platform
Redouan Bshary - One of the best experts on this subject based on the ideXlab platform.
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Third-party ranks knowledge in wild vervet monkeys (Chlorocebus aethiops pygerythrus)
PloS one, 2013Co-Authors: Christèle Borgeaud, Erica Van De Waal, Redouan BsharyAbstract:The Machiavellian/Social Intelligence Hypothesis proposes that a complex Social environment selected for advanced cognitive abilities in vertebrates. In primates it has been proposed that sophisticated Social strategies like obtaining suitable coalition partners are an important component of Social Intelligence. Knowing the rank relationships between group members is a basic requirement for the efficient use of coalitions and the anticipation of counter-coalitions. Experimental evidence for such knowledge currently exists in only few species. Here, we conducted rank reversal playback experiments on adult females belonging to three different groups of free-ranging vervet monkeys (Chlorocebus aethiops pygerythrus) to test their knowledge of the female hierarchy. Playbacks simulating rank reversals (subordinate aggressing a dominant) induced longer looking times than playbacks simulating a dominant aggressing a subordinate. Vervet monkey females therefore seem to compute the rank relationships between other females. Our results suggest that detailed Social knowledge about rank relationships may be widespread in primates and potentially also in other species living in stable groups.
Carlos Gershenson - One of the best experts on this subject based on the ideXlab platform.
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learning Social Intelligence and the turing test why an out of the box turing machine will not pass the turing test
Conference on Computability in Europe, 2012Co-Authors: Bruce Edmonds, Carlos GershensonAbstract:The Turing Test checks for human Intelligence, rather than any putative general Intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing machine, which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e., can be implemented as a Turing machine. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a Turing machine and (b) learning a Turing machine, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human Intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (Social learning in context) if an artificial Intelligence is to pass the Turing Test. Whilst it is always possible to ‘compile' the results of learning into a Turing machine, this would not be a designed Turing machine and would not be able to continually adapt (pass future Turing Tests). We conclude three things, namely that: a purely "designed" Turing machine will never pass the Turing Test; that there is no such thing as a general Intelligence since it necessarily involves learning; and that learning/adaption and computation should be clearly distinguished.
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learning Social Intelligence and the turing test why an out of the box turing machine will not pass the turing test
arXiv: Artificial Intelligence, 2012Co-Authors: Bruce Edmonds, Carlos GershensonAbstract:The Turing Test (TT) checks for human Intelligence, rather than any putative general Intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human Intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (Social learning in context) if an artificial Intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general Intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.
Christèle Borgeaud - One of the best experts on this subject based on the ideXlab platform.
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Third-party ranks knowledge in wild vervet monkeys (Chlorocebus aethiops pygerythrus)
PloS one, 2013Co-Authors: Christèle Borgeaud, Erica Van De Waal, Redouan BsharyAbstract:The Machiavellian/Social Intelligence Hypothesis proposes that a complex Social environment selected for advanced cognitive abilities in vertebrates. In primates it has been proposed that sophisticated Social strategies like obtaining suitable coalition partners are an important component of Social Intelligence. Knowing the rank relationships between group members is a basic requirement for the efficient use of coalitions and the anticipation of counter-coalitions. Experimental evidence for such knowledge currently exists in only few species. Here, we conducted rank reversal playback experiments on adult females belonging to three different groups of free-ranging vervet monkeys (Chlorocebus aethiops pygerythrus) to test their knowledge of the female hierarchy. Playbacks simulating rank reversals (subordinate aggressing a dominant) induced longer looking times than playbacks simulating a dominant aggressing a subordinate. Vervet monkey females therefore seem to compute the rank relationships between other females. Our results suggest that detailed Social knowledge about rank relationships may be widespread in primates and potentially also in other species living in stable groups.
Bruce Edmonds - One of the best experts on this subject based on the ideXlab platform.
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learning Social Intelligence and the turing test why an out of the box turing machine will not pass the turing test
Conference on Computability in Europe, 2012Co-Authors: Bruce Edmonds, Carlos GershensonAbstract:The Turing Test checks for human Intelligence, rather than any putative general Intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing machine, which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e., can be implemented as a Turing machine. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a Turing machine and (b) learning a Turing machine, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human Intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (Social learning in context) if an artificial Intelligence is to pass the Turing Test. Whilst it is always possible to ‘compile' the results of learning into a Turing machine, this would not be a designed Turing machine and would not be able to continually adapt (pass future Turing Tests). We conclude three things, namely that: a purely "designed" Turing machine will never pass the Turing Test; that there is no such thing as a general Intelligence since it necessarily involves learning; and that learning/adaption and computation should be clearly distinguished.
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learning Social Intelligence and the turing test why an out of the box turing machine will not pass the turing test
arXiv: Artificial Intelligence, 2012Co-Authors: Bruce Edmonds, Carlos GershensonAbstract:The Turing Test (TT) checks for human Intelligence, rather than any putative general Intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human Intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (Social learning in context) if an artificial Intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general Intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.
Erica Van De Waal - One of the best experts on this subject based on the ideXlab platform.
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Third-party ranks knowledge in wild vervet monkeys (Chlorocebus aethiops pygerythrus)
PloS one, 2013Co-Authors: Christèle Borgeaud, Erica Van De Waal, Redouan BsharyAbstract:The Machiavellian/Social Intelligence Hypothesis proposes that a complex Social environment selected for advanced cognitive abilities in vertebrates. In primates it has been proposed that sophisticated Social strategies like obtaining suitable coalition partners are an important component of Social Intelligence. Knowing the rank relationships between group members is a basic requirement for the efficient use of coalitions and the anticipation of counter-coalitions. Experimental evidence for such knowledge currently exists in only few species. Here, we conducted rank reversal playback experiments on adult females belonging to three different groups of free-ranging vervet monkeys (Chlorocebus aethiops pygerythrus) to test their knowledge of the female hierarchy. Playbacks simulating rank reversals (subordinate aggressing a dominant) induced longer looking times than playbacks simulating a dominant aggressing a subordinate. Vervet monkey females therefore seem to compute the rank relationships between other females. Our results suggest that detailed Social knowledge about rank relationships may be widespread in primates and potentially also in other species living in stable groups.
