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: 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: 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: 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.

Abstract: Artificial neural networks include a plurality of artificial neurons and a plurality of Boolean-complete compartments, a respective one of which couples a respective pair of artificial neurons. By providing Boolean-complete compartments, spurious complement memories can be avoided. A Boolean-complete compartment includes a collection of at least four Boolean functions that represent input vectors to the respective pair of artificial neurons. The collection of at least four Boolean functions are selected from sixteen possible Boolean functions that can represent input vectors to the respective pair of artificial neurons. A count for each of the at least four Boolean functions is also provided. The count represents a number of occurrences of each of the at least four Boolean functions in input vectors to the respective pair of artificial neurons.

Abstract: A graphical user interface for a computer system that includes one or more intelligent assistants. The interface includes composite icons comprising a graphical representation of a human figure, a representation of a desk, and a mini-icon that associates an assistant with the object it supports. The assistant's desk can be opened to show its contents and the human figure can move from its position next to the desk to a position on the computer display screen next to a suggestions dialog box that displays suggested actions to the user.

Abstract: Based on the recognition that transfer functions of Boolean completeness can be expressed as a domain within a periodic function, an architecture of a artificial neuron which is fully generalized in application and capable of rapid learning with minimal memory requirements while maintaining content addressability of memory, is provided. Full functionality of this architecture is demonstrated for an input vector containing two values. Extension to three variables shows the potential for generality of this architecture to N-valued input vectors.

Abstract: An interface for a neural network includes a generalized data translator and a certainty filter in the data path including the neural network for rendering a decision on raw data, possibly from a data processing application. The data translator is controlled with user-definable parameters and procedures contained in a property list in order to manipulate translation, truncation, mapping (including weighting) and other transformations of the raw data. The neuron to which the output of the data translator is applied is controlled by a code index contained in an action list. An external certainty threshold is also provided, preferably by the action list to filter the output of the neural network. The core program used with the ConExNS neurons for system maintenance also includes further core operations and size maintenance operations responsive to commands from the user of an application to cause operations to be performed with in the neural network as well as to create and update the property and action lists.

Abstract: Based on the recognition that transfer functions of Boolean completeness can be expressed as a domain within a periodic function, an architecture of a artificial neuron which is fully generalized in application and capable of rapid learning with minimal memory requirements while maintaining content addressability of memory, is provided. Full functionality of this architecture is demonstrated for an input vector containing two values. Extension to three variables shows the potential for generality of this architecture to N-valued input vectors.