Results for "Author: gary gaulin"

This computer model demonstrates self-organizing, self-learning intelligence. On startup this guess/memory intelligence is like a newborn. It does not know up from down or left from right or what it’s seeing, experiencing. But from trial and error quickly learns how to coordinate motors with sensory information to get where it wants to go. The model also has an angular ring memory that adds awareness of where the feeder is located when it is out of its field of vision. Like us when a ball goes by we know where it went and without thinking about it can turn in the proper direction. This can be tested by checking a box that allows the mouse to be used to move the feeder around the screen so it gives chase. After some training time it will get very good at keeping up with it. This model is analogous to finger muscle control that through training becomes coordinated in a way that they have the keyboard layout stored as motions to reach each key. In both cases intelligence successfully learns to navigate a 3D space without requiring a physical map. Intelligence detection is in the form of a graphic display that allows confidence, contents of memory and success staying fed to be shown. No intelligence at all would produce a flat-line graph. But as input sensory information is added it's learning rate increases, as does its confidence level. Experimenting with how the simple main loop uses its sensory information (analogous to how neurons are connected) can produce thousands of various behaviors. Documentation Included.

This next generation Intelligence Generator (also on Planet Source Code) computer model is (as per Occam’s Razor) made to be as simple as possible to reduce all that is happening in a complex biological circuit of an intelligent living thing to what is most important to understand about the way self-learning intelligence works, in this case a compound eye insect. The program provides a precise and testable operational definition for “intelligence” where taking all sensors out of memory addressing demonstrates "protointelligence", while clicking out its Red Green Blue vision subsystems from both confidence and memory renders it completely “unintelligent” in which case it only expresses Brownian motion type random behavior. The computer model also provides a precise, testable and scientifically useful operational definition for "intelligent cause" where each of the three emergent levels can be individually modeled, with a model predicted to be possible that generates an intelligent causation event, now goal of further research and challenge for all. Applying this model to biology shows advantages of a two lobed brain over a single lobe that would have to be much larger to control the same amount of sensory input. This model also provides insight into the origin of life, intelligence, and mechanisms that produces new species including human which was found to be systematically the primary result of good-guess chromosome speciation from fusion of two ancestral chromosomes which created our second largest. The code is useful for game engines and other applications that require virtual intelligence, is relatively well commented, has on-screen tool-tip-text, and 30 pages of referenced documentation.

This computer model demonstrates self-organizing, self-learning intelligence. On startup this guess/memory intelligence is like a newborn. It does not know up from down or left from right or what it’s seeing, experiencing. But from trial and error quickly learns how to coordinate motors with sensory information to get where it wants to go. The model also has an angular ring memory that adds awareness of where the feeder is located when it is out of its field of vision. Like us when a ball goes by we know where it went and without thinking about it can turn in the proper direction. This can be tested by checking a box that allows the mouse to be used to move the feeder around the screen so it gives chase. After some training time it will get very good at keeping up with it. This model is analogous to finger muscle control that through training becomes coordinated in a way that they have the keyboard layout stored as motions to reach each key. In both cases intelligence successfully learns to navigate a 3D space without requiring a physical map. Intelligence detection is in the form of a graphic display that allows confidence, contents of memory and success staying fed to be shown. No intelligence at all would produce a flat-line graph. But as input sensory information is added it's learning rate increases, as does its confidence level. Experimenting with how the simple main loop uses its sensory information (analogous to how neurons are connected) can produce thousands of various behaviors. Documentation Included.

This next generation Intelligence Generator (also on Planet Source Code) computer model is (as per Occam’s Razor) made to be as simple as possible to reduce all that is happening in a complex biological circuit of an intelligent living thing to what is most important to understand about the way self-learning intelligence works, in this case a compound eye insect. The program provides a precise and testable operational definition for “intelligence” where taking all sensors out of memory addressing demonstrates "protointelligence", while clicking out its Red Green Blue vision subsystems from both confidence and memory renders it completely “unintelligent” in which case it only expresses Brownian motion type random behavior. The computer model also provides a precise, testable and scientifically useful operational definition for "intelligent cause" where each of the three emergent levels can be individually modeled, with a model predicted to be possible that generates an intelligent causation event, now goal of further research and challenge for all. Applying this model to biology shows advantages of a two lobed brain over a single lobe that would have to be much larger to control the same amount of sensory input. This model also provides insight into the origin of life, intelligence, and mechanisms that produces new species including human which was found to be systematically the primary result of good-guess chromosome speciation from fusion of two ancestral chromosomes which created our second largest. The code is useful for game engines and other applications that require virtual intelligence, is relatively well commented, has on-screen tool-tip-text, and 30 pages of referenced documentation.

This computer model demonstrates self-organizing, self-learning intelligence. On startup this guess/memory intelligence is like a newborn. It does not know up from down or left from right or what it’s seeing, experiencing. But from trial and error quickly learns how to coordinate motors with sensory information to get where it wants to go. The model also has an angular ring memory that adds awareness of where the feeder is located when it is out of its field of vision. Like us when a ball goes by we know where it went and without thinking about it can turn in the proper direction. This can be tested by checking a box that allows the mouse to be used to move the feeder around the screen so it gives chase. After some training time it will get very good at keeping up with it. This model is analogous to finger muscle control that through training becomes coordinated in a way that they have the keyboard layout stored as motions to reach each key. In both cases intelligence successfully learns to navigate a 3D space without requiring a physical map. Intelligence detection is in the form of a graphic display that allows confidence, contents of memory and success staying fed to be shown. No intelligence at all would produce a flat-line graph. But as input sensory information is added it's learning rate increases, as does its confidence level. Experimenting with how the simple main loop uses its sensory information (analogous to how neurons are connected) can produce thousands of various behaviors. Documentation Included.

This next generation Intelligence Generator (also on Planet Source Code) computer model is (as per Occam’s Razor) made to be as simple as possible to reduce all that is happening in a complex biological circuit of an intelligent living thing to what is most important to understand about the way self-learning intelligence works, in this case a compound eye insect. The program provides a precise and testable operational definition for “intelligence” where taking all sensors out of memory addressing demonstrates "protointelligence", while clicking out its Red Green Blue vision subsystems from both confidence and memory renders it completely “unintelligent” in which case it only expresses Brownian motion type random behavior. The computer model also provides a precise, testable and scientifically useful operational definition for "intelligent cause" where each of the three emergent levels can be individually modeled, with a model predicted to be possible that generates an intelligent causation event, now goal of further research and challenge for all. Applying this model to biology shows advantages of a two lobed brain over a single lobe that would have to be much larger to control the same amount of sensory input. This model also provides insight into the origin of life, intelligence, and mechanisms that produces new species including human which was found to be systematically the primary result of good-guess chromosome speciation from fusion of two ancestral chromosomes which created our second largest. The code is useful for game engines and other applications that require virtual intelligence, is relatively well commented, has on-screen tool-tip-text, and 30 pages of referenced documentation.

This computer model demonstrates self-organizing, self-learning intelligence. On startup this guess/memory intelligence is like a newborn. It does not know up from down or left from right or what it’s seeing, experiencing. But from trial and error quickly learns how to coordinate motors with sensory information to get where it wants to go. The model also has an angular ring memory that adds awareness of where the feeder is located when it is out of its field of vision. Like us when a ball goes by we know where it went and without thinking about it can turn in the proper direction. This can be tested by checking a box that allows the mouse to be used to move the feeder around the screen so it gives chase. After some training time it will get very good at keeping up with it. This model is analogous to finger muscle control that through training becomes coordinated in a way that they have the keyboard layout stored as motions to reach each key. In both cases intelligence successfully learns to navigate a 3D space without requiring a physical map. Intelligence detection is in the form of a graphic display that allows confidence, contents of memory and success staying fed to be shown. No intelligence at all would produce a flat-line graph. But as input sensory information is added it's learning rate increases, as does its confidence level. Experimenting with how the simple main loop uses its sensory information (analogous to how neurons are connected) can produce thousands of various behaviors. Documentation Included.

This next generation Intelligence Generator (also on Planet Source Code) computer model is (as per Occam’s Razor) made to be as simple as possible to reduce all that is happening in a complex biological circuit of an intelligent living thing to what is most important to understand about the way self-learning intelligence works, in this case a compound eye insect. The program provides a precise and testable operational definition for “intelligence” where taking all sensors out of memory addressing demonstrates "protointelligence", while clicking out its Red Green Blue vision subsystems from both confidence and memory renders it completely “unintelligent” in which case it only expresses Brownian motion type random behavior. The computer model also provides a precise, testable and scientifically useful operational definition for "intelligent cause" where each of the three emergent levels can be individually modeled, with a model predicted to be possible that generates an intelligent causation event, now goal of further research and challenge for all. Applying this model to biology shows advantages of a two lobed brain over a single lobe that would have to be much larger to control the same amount of sensory input. This model also provides insight into the origin of life, intelligence, and mechanisms that produces new species including human which was found to be systematically the primary result of good-guess chromosome speciation from fusion of two ancestral chromosomes which created our second largest. The code is useful for game engines and other applications that require virtual intelligence, is relatively well commented, has on-screen tool-tip-text, and 30 pages of referenced documentation.

This computer model demonstrates self-organizing, self-learning intelligence. On startup this guess/memory intelligence is like a newborn. It does not know up from down or left from right or what it’s seeing, experiencing. But from trial and error quickly learns how to coordinate motors with sensory information to get where it wants to go. The model also has an angular ring memory that adds awareness of where the feeder is located when it is out of its field of vision. Like us when a ball goes by we know where it went and without thinking about it can turn in the proper direction. This can be tested by checking a box that allows the mouse to be used to move the feeder around the screen so it gives chase. After some training time it will get very good at keeping up with it. This model is analogous to finger muscle control that through training becomes coordinated in a way that they have the keyboard layout stored as motions to reach each key. In both cases intelligence successfully learns to navigate a 3D space without requiring a physical map. Intelligence detection is in the form of a graphic display that allows confidence, contents of memory and success staying fed to be shown. No intelligence at all would produce a flat-line graph. But as input sensory information is added it's learning rate increases, as does its confidence level. Experimenting with how the simple main loop uses its sensory information (analogous to how neurons are connected) can produce thousands of various behaviors. Documentation Included.

This next generation Intelligence Generator (also on Planet Source Code) computer model is (as per Occam’s Razor) made to be as simple as possible to reduce all that is happening in a complex biological circuit of an intelligent living thing to what is most important to understand about the way self-learning intelligence works, in this case a compound eye insect. The program provides a precise and testable operational definition for “intelligence” where taking all sensors out of memory addressing demonstrates "protointelligence", while clicking out its Red Green Blue vision subsystems from both confidence and memory renders it completely “unintelligent” in which case it only expresses Brownian motion type random behavior. The computer model also provides a precise, testable and scientifically useful operational definition for "intelligent cause" where each of the three emergent levels can be individually modeled, with a model predicted to be possible that generates an intelligent causation event, now goal of further research and challenge for all. Applying this model to biology shows advantages of a two lobed brain over a single lobe that would have to be much larger to control the same amount of sensory input. This model also provides insight into the origin of life, intelligence, and mechanisms that produces new species including human which was found to be systematically the primary result of good-guess chromosome speciation from fusion of two ancestral chromosomes which created our second largest. The code is useful for game engines and other applications that require virtual intelligence, is relatively well commented, has on-screen tool-tip-text, and 30 pages of referenced documentation.