Artificial intelligence device and method responsive to influences of mind

Abstract

A device for detecting an influence of mind comprises a source of non-deterministic random numbers, SNDRN, a phase-sensitive filter, and a results interface. In some embodiments, the phase-sensitive filter comprises a complex filter. An artificial sensory neuron comprises a SNDRN. An analog artificial sensory detector comprises a plurality of analog artificial sensory neurons, an abstracting processor and a control or feedback unit. Some embodiments include an artificial neural network. An artificial consciousness network contains a plurality of artificial neural networks. One of the artificial neural networks comprises an activation pattern meta-analyzer. An artificial intelligence device comprises a cluster of artificial consciousness networks, a sensory input device to provide sensory input signals to the input of one or more ANNs, and an output device.

1 . An artificial intelligence device (AID) responsive to an influence of mind, comprising:
 a sensory input device, said sensory input device being operable to produce sensory signals;   a cluster of a plurality of artificial consciousness networks (ACNs); and   an output device;   wherein each ACN comprises:   a sensory artificial neural network (sensory ANN), said sensory ANN being operable to accept ASN signals from an artificial sensory neuron (ASN) and to produce sensory-ANN output numbers indicative of an influence of mind; and   a meta-analyzer artificial neural network comprising an activation pattern meta-analyzer, said meta-analyzer ANN being operable to accept sensory-ANN output numbers from a sensory ANN and to produce an output responsive to a number of states in said ACN.   
     
     
         2 . An artificial intelligence device as in  claim 1  wherein:
 a source of non-deterministic random numbers in said ASN is operable to generate digital source numbers; and 
 said ASN comprises a low-pass filter operable to accept said digital source numbers and to produce quasi-continuous digital ASN signals. 
 
     
     
         3 . An artificial intelligence device as in  claim 1 , further comprising:
 a signal processor.   
     
     
         4 . An artificial intelligence device as in  claim 1  further comprising:
 an information database and memory. 
 
     
     
         5 . An artificial intelligence device as in  claim 1  comprising:
 a plurality of sensory input devices; 
 a plurality of sensory ANNs in said cluster of ACNs, each sensory ANN being operable to process ASN signals from an ASN to produce abstracted numbers indicative of an influence of mind, and 
 a plurality of meta-analyzer ANNs comprising an activation pattern meta-analyzer, a plurality of said meta-analyzer ANNs being operable to accept abstracted numbers from a plurality of sensory ANNs and to respond to a number of states associated with consciousness operating on or in said AID. 
 
     
     
         6 . An artificial consciousness network as in  claim 1  wherein:
 a local cluster of ASNs is contained in spherical volume having a diameter in a range of from 0.1 mm to 1.0 mm. 
 
     
     
         7 . An artificial intelligence device as in  claim 1  wherein:
 a sensory ANN in an ACN is operable to accept abstracted numbers from another sensory ANN in said ACN. 
 
     
     
         8 . An artificial intelligence device as in  claim 1  wherein:
 at least one of said ACNs comprises an ASN that comprises a low-pass filter. 
 
     
     
         9 . A method of detecting an influence of mind, comprising:
 providing an artificial sensory neuron (ASN) containing an analog source of non-deterministic random numbers (SNDRN);   generating a stream of analog source numbers using said ASN; and   abstracting said stream of analog source numbers using an analog artificial neuron network (ANN) to produce abstracted output numbers.   
     
     
         10 . A method as in  claim 9 , further comprising:
 feeding said abstracted output numbers to a control unit.   
     
     
         11 . A method as in  claim 9 , further comprising:
 feeding said abstracted output numbers to a feedback unit.   
     
     
         12 . A method as in  claim 9 , further comprising:
 providing a plurality of ASN containing an analog SNDRN.   
     
     
         13 . A method as in  claim 12  comprising:
 providing a local cluster of ASNs contained in a spherical volume having a diameter in a range of from 0.1 mm to 1.0 mm. 
 
     
     
         14 . A method of detecting an influence of mind using artificial consciousness, comprising:
 providing an artificial sensory neuron (ASN) containing a source of non-deterministic random numbers (SNDRN);   generating a stream of source numbers using said ASN;   producing abstracted numbers indicative of an influence of mind by using a sensory artificial neural network (sensory ANN) to process said source numbers from said ASN; and   producing an output indicative of a number of specific influences of mind operating on said ACN by feeding said abstracted numbers from said sensory ANN to a meta-analyzer artificial neural network (meta-analyzer ANN) comprising an activation pattern meta-analyzer.   
     
     
         15 . A method as in  claim 14 , further comprising:
 before using said sensory ANN, filtering said source numbers to change digital source numbers to quasi-continuous digital source numbers using a low-pass filter.   
     
     
         16 . A method as in  claim 14 , comprising:
 providing a plurality of artificial sensory neurons (ASNs), each ASN containing a source of non-deterministic random numbers (SNDRN);   generating a stream of source numbers from a plurality of said ASNs;   producing abstracted numbers indicative of an influence of mind by feeding a stream of source numbers from each of a plurality of said ASNs to one of a plurality of sensory ANNs; and   producing an output indicative of a number of specific influences of mind operating on said ACN by feeding said abstracted numbers from said sensory ANN to a meta-analyzer artificial neural network (meta-analyzer ANN) comprising an activation pattern meta-analyzer.   
     
     
         17 . A method as in  claim 14  wherein providing a plurality of ASNs comprises:
 providing a local cluster of ASNs contained in a spherical volume having a diameter in a range of from 0.1 mm to 1.0 mm. 
 
     
     
         18 . A method of detecting an influence of mind using artificial intelligence, comprising:
 producing sensory signals using a sensory input device;   feeding said sensory signals to an artificial neural network (ANN) in an artificial consciousness network (ACN) of a cluster of a plurality of artificial consciousness networks (cluster of ACNs);   abstracting said ASN signals using said cluster of ACNs to produce a cluster output indicative of an influence of mind; and   sending said cluster output to an output device; wherein   an ACN in said cluster of ACNs comprises:   a sensory artificial neural network (sensory ANN), said sensory ANN being operable to process ASN signals to produce abstracted numbers indicative of an influence of mind; and   a meta-analyzer artificial neural network (meta-analyzer ANN) comprising an activation pattern meta-analyzer, said meta-analyzer ANN being operable to accept abstracted numbers from a sensory ANN and to respond to a number of states in said ACN.   
     
     
         19 . A method as in  claim 18 , further comprising:
 exchanging numbers between said ACN cluster and a signal processor.   
     
     
         20 . A method as in  claim 18 , further comprising:
 exchanging numbers between said ACN cluster and an information database and memory.   
     
     
         21 . A method as in  claim 18 , comprising:
 producing a plurality of streams of sensory signals using a plurality of sensory input devices; and   feeding said stream of sensory signals to a plurality of ANNs of at least one ACN in said cluster of ACNs.