Abstract: An anomalous effect detector responsive to 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. Preferably, several artificial sensory neurons are grouped in a small volume. 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 consciousness 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 in ACD, and an output device.

Abstract: Mind-enabled question answering (MEQA) systems (300, 340) and methods (400, 500) produce answers (313) that are not inferable from information available from private databases, online searching or other traditional sources. MEQA systems utilize information provided by using devices (200) and methods that are responsive to an influence of mind. Preferred embodiments of MEQA technology use a Bayesian Network to calculate the probability of an answer's correctness. MEQA systems and methods utilize high speed non-deterministic random number generators (NDRNGs). Preferred NDRNGs (202) achieve high statistical quality without randomness correction, which allows improved response of a mind-enabled device (200, 302).

Abstract: Mental influence detectors and corresponding methods are useful for detecting an influence of mind and hidden or classically non-inferable information. An anomalous effect detector includes a source of non-deterministic random numbers, a converter to convert a property of numbers, a processor to accept converter output and to produce an output signal representative of an influence of mind. The processor output signal contains fewer numbers than the input. A quantum computer includes a physical source of entropy to generate output numbers; a source of test numbers; a measurement processor to accept output numbers and to measure a relationship between process numbers and at least one test number to produce an output representative of an influence of mind.

Abstract: V An anomalous effect detector (100, 130, 300, 800, 830, 840, 900, 930) responsive to an influence of mind comprises a source of non-deterministic random numbers, SNDRN, (104, 134, 310), a phase-sensitive filter (108, 140, 170, 320), and a results interface (110, 160, 340). In some embodiments, the phase-sensitive filter comprises a complex filter (170). An artificial sensory neuron (802, 810, 820, 906) comprises a SNDRN. Preferably, several artificial sensory neurons (802, 906) are grouped in a small volume. An analog artificial sensory detector (800) comprises a plurality of analog artificial sensory neurons (802), an abstracting processor (804) and a control or feedback unit (806). Some embodiments include an artificial neural network (850). An artificial consciousness network (900) contains a plurality of artificial neural networks (902, 914). One of the artificial neural networks (914) comprises an activation pattern meta-analyzer.

Abstract: Mental influence detectors and corresponding methods are useful for detecting an influence of mind and hidden or classically non-inferable information. An anomalous effect detector includes a source of non-deterministic random numbers, a converter to convert a property of numbers, a processor to accept converter output and to produce an output signal representative of an influence of mind. The processor output signal contains fewer numbers than the input. A quantum computer includes a physical source of entropy to generate output numbers; a source of test numbers; a measurement processor to accept output numbers and to measure a relationship between process numbers and at least one test number to produce an output representative of an influence of mind.

Abstract: In the field of direct mind-machine interactions, prior art devices and methods do not provide sufficiently fast and reliable results. Mental influence detectors (100, 140, 400, 430) and corresponding methods provide fast and reliable results useful for detecting an influence of mind and hidden or classically non-inferable information. An anomalous effect detector (100) includes a source (104) of non-deterministic random numbers (110), a converter (114) to convert a property of numbers, a processor to accept converter output (118) and to produce an output signal (124) representative of an influence of mind. The processor output signal (124) contains fewer numbers than the input (110).

Abstract: A true random number generator (TRNG) in an integrated circuit comprises a plurality of independent ring oscillators with multiple output taps combined into enhanced outputs, a plurality of delay lines, a combiner-sampler and a source of a clock signal. Some embodiments provide a TRNG that is resettable, allowing one or more independent random numbers to be generated in response to a trigger signal.

Abstract: V An anomalous effect detector (100, 130, 300, 800, 830, 840, 900, 930) responsive to an influence of mind comprises a source of non-deterministic random numbers, SNDRN, (104, 134, 310), a phase-sensitive filter (108, 140, 170, 320), and a results interface (110, 160, 340). In some embodiments, the phase-sensitive filter comprises a complex filter (170). An artificial sensory neuron (802, 810, 820, 906) comprises a SNDRN. Preferably, several artificial sensory neurons (802, 906) are grouped in a small volume. An analog artificial sensory detector (800) comprises a plurality of analog artificial sensory neurons (802), an abstracting processor (804) and a control or feedback unit (806). Some embodiments include an artificial neural network (850). An artificial consciousness network (900) contains a plurality of artificial neural networks (902, 914). One of the artificial neural networks (914) comprises an activation pattern meta-analyzer.

Abstract: In the field of direct mind-machine interactions, prior art devices and methods do not provide sufficiently fast and reliable results. Mental influence detectors (100, 140, 400, 430) and corresponding methods provide fast and reliable results useful for detecting an influence of mind and hidden or classically non-inferable information. An anomalous effect detector (100) includes a source (104) of non-deterministic random numbers (110), a converter (114) to convert a property of numbers, a processor to accept converter output (118) and to produce an output signal (124) representative of an influence of mind. The processor output signal (124) contains fewer numbers than the input (110).

Abstract: In the field of direct mind-machine interactions, prior art devices and methods do not provide sufficiently fast and reliable results. Mental influence detectors (100, 140, 400, 430) and corresponding methods provide fast and reliable results useful for detecting an influence of mind and hidden or classically non-inferable information. An anomalous effect detector (100) includes a source (104) of non-deterministic random numbers (110), a converter (114) to convert a property of numbers, a processor to accept converter output (118) and to produce an output signal (124) representative of an influence of mind. The processor output signal (124) contains fewer numbers than the input (110).