Abstract: Analogies among entities may be detected by obtaining associative counts among the entities and computing similarity measures among given entities and other entities, using the associative counts. First and second entities are then identified as being analogies if the first entity has a strongest similarity measure with respect to the second entity and the second entity also has a strongest similarity measure with respect to the first entity. The similarity measures may be calculated using a normalized entropy inverted among a given entity and other entities.

Abstract: Systems, methods and computer program products are provided for a distributed associative memory base. Such methods may include providing a distributed memory base that includes a network of networks of associative memory networks. The memory base may include a network of associative memory networks, a respective associative memory network comprising associations among a respective observer entity and a plurality of observed entities that are observed by the respective observer entity. Ones of the associative memory networks are physically and/or logically independent from other ones of the associative memory networks. Methods include imagining associations from the associative memory base using a plurality of streaming queues that correspond to ones of a plurality of rows of ones of the associative memory networks.

Abstract: Systems, methods and computer program products are provided for a distributed associative memory base. Such methods may include providing a distributed memory base that includes a network of networks of associative memory networks. The memory base may include a network of associative memory networks, a respective associative memory network comprising associations among a respective observer entity and a plurality of observed entities that are observed by the respective observer entity. Ones of the associative memory networks are physically and/or logically independent from other ones of the associative memory networks. Methods include imagining associations from the associative memory base using a plurality of streaming queues that correspond to ones of a plurality of rows of ones of the associative memory networks.

Abstract: A time of occurrence of an interest event of multiple events is anticipated based on time intervals between at least two previously occurring events and a previous occurrence of the interest event and based on a new occurrence of at least one of the events. For a respective event, multiple inter-event interval pairs based on occurrence times of pairs of respective remaining events relative to an occurrence time of the respective event are memorized. For a respective event, a time of future occurrence of the interest event from the respective event is predicted based on the inter-event interval pairs that have been memorized. The predicted time of future occurrence is also based on the new occurrence of the respective event and at least one of the events to obtain multiple interest event predictions of times of future occurrences of the interest event. The predictions are processed to generate an anticipated time when the interest event will occur in the future.

Abstract: Associative matrix compression methods, systems, computer program products and data structures compress an association matrix that contains counts that indicate associations among pairs of attributes. Selective bit plane representations of those selected segments of the association matrix that have at least one count is performed, to allow compression. More specifically, a set of segments is generated, a respective one of which defines a subset, greater than one, of the pairs of attributes. Selective identifications of those segments that have at least one count are stored. The at least one count that is associated with a respective identified segment is also stored as at least one bit plane representation. The at least one bit plane representation identifies a value of the at least one associated count for a bit position of the count that corresponds to the associated bit plane.

Abstract: Associative memory systems, methods and/or computer program products include a network of networks of associative memory networks. A network of entity associative memory networks is provided, a respective entity associative memory of which includes associations among a respective observer entity and observed entities that are observed by the respective observer entity, based on input documents. A network of feedback associative memory networks includes associations among observed entities for a respective positive and/or negative evaluation for a respective task of a respective user. A network of document associative memory networks includes associations among observed entities in a respective observed input source, such as a respective input document. A network of community associative memory networks includes associations among a respective observer entity, observed entities that are observed by the respective observer entity, and observed tasks of users in which the observer entity was queried.

Abstract: Associative memories include associative memory cells. A respective cell includes a sensor input, a prior association representation, a next association representation and an associative output. The cells are serially interconnected to form a linear array, such that the sensor inputs, the prior association representations and the next association representations of the serially connected cells are arranged in a sequence from distal to proximal cells based on affinities of associations among the series of sensor inputs. A respective cell also includes processing logic. The processing logic is responsive to the associated sensor input being active, to send a measure of the next association representation to an adjacent proximal cell and/or to send a measure of prior association representation to an adjacent distal cell.

Abstract: A location of a missing object is predicted based on past sightings of objects including the missing object, and a new sighting of the objects except for the missing object. For a respective given object in the objects, the past sightings are memorized based on respective distances of respective remaining objects from the respective given object. Distance-based memorization may take place using an agent or associative memory for a respective given object. Then, for a respective given object, except for the missing object, a distance of the missing object from the respective given object is predicted, based on the past sightings that have been memorized and the new sighting, to obtain candidate locations for the missing object. The candidate locations are then disambiguated, to predict the location of the missing object.

Abstract: Associative matrix compression methods, systems, computer program products and data structures compress an association matrix that contains counts that indicate associations among pairs of attributes. Selective bit plane representations of those selected segments of the association matrix that have at least one count is performed, to allow compression. More specifically, a set of segments is generated, a respective one of which defines a subset, greater than one, of the pairs of attributes. Selective identifications of those segments that have at least one count are stored. The at least one count that is associated with a respective identified segment is also stored as at least one bit plane representation. The at least one bit plane representation identifies a value of the at least one associated count for a bit position of the count that corresponds to the associated bit plane.

Abstract: Analogies among entities may be detected by obtaining associative counts among the entities and computing similarity measures among given entities and other entities, using the associative counts. First and second entities are then identified as being analogies if the first entity has a strongest similarity measure with respect to the second entity and the second entity also has a strongest similarity measure with respect to the first entity. The similarity measures may be calculated using a normalized entropy inverted among a given entity and other entities.

Abstract: Associative memory systems, methods and/or computer program products include a network of networks of associative memory networks. A network of entity associative memory networks is provided, a respective entity associative memory of which includes associations among a respective observer entity and observed entities that are observed by the respective observer entity, based on input documents. A network of feedback associative memory networks includes associations among observed entities for a respective positive and/or negative evaluation for a respective task of a respective user. A network of document associative memory networks includes associations among observed entities in a respective observed input source, such as a respective input document. A network of community associative memory networks includes associations among a respective observer entity, observed entities that are observed by the respective observer entity, and observed tasks of users in which the observer entity was queried.

Abstract: Sensors are configured to repeatedly monitor variables of a physical system during its operation. A novelty detection system is responsive to the sensors and is configured to repeatedly observe into an associative memory, states of associations among the variables that are repeatedly monitored, during a learning phase. The novelty detection system is further configured to thereafter observe at least one state of associations among the variables that are sensed relative to the states of associations that are in the associative memory, to identify a novel state of associations among the variables. The novelty detection system may determine whether the novel state is indicative of normal operation or of a potential abnormal operation. Multiple layers of learning for real-time diagnostics/prognostics also may be provided.

Abstract: An artificial neuron includes inputs and dendrites, a respective one of which is associated with a respective one of the inputs. A respective dendrite includes a respective power series of weights. The weights in a given power of the power series represent a maximal projection. A respective power also may include at least one switch, to identify holes in the projections. By providing maximal projections, linear scaling may be provided for the maximal projections, and quasi-linear scaling may be provided for the artificial neuron, while allowing a lossless compression of the associations. Accordingly, hetero-associative and/or auto-associative recall may be accommodated for large numbers of inputs, without requiring geometric scaling as a function of input.

Abstract: An artificial neuron includes inputs and dendrites, a respective one of which is associated with a respective one of the inputs. Each dendrite includes a power series of weights, and each weight in a power series includes an associated count for the associated power. The power series of weights preferably is a base-two power series of weights, each weight in the base-two power series including an associated count that represents a bit position. The counts for the associated power preferably are statistical counts. More particularly, the dendrites preferably are sequentially ordered, and the power series of weights preferably includes a pair of first and second power series of weights. Each weight in the first power series includes a first count that is a function of associations of prior dendrites, and each weight of the second power series includes a second count that is a function of associations of next dendrites.