Methods and systems for feature extraction of LIDAR surface manifolds. LIDAR point data with respect to one or more LIDAR surface manifolds can be generated. An AHAH-based feature extraction operation can be automatically performed on the point data for compression and processing thereof. The results of the AHAH-based feature extraction operation can be output as a compressed binary label representative of the at least one surface manifold rather than the point data to afford a high-degree of compression for transmission or further processing thereof. Additionally, one or more voxels of a LIDAR point cloud composed of the point data can be scanned in order to recover the compressed binary label, which represents prototypical surface patches with respect to the LIDAR surface manifold(s).
Methods and systems for feature extraction of LIDAR surface manifolds. LIDAR point data with respect to one or more LIDAR surface manifolds can be generated. An AHAH-based feature extraction operation can be automatically performed on the point data for compression and processing thereof. The results of the AHAH-based feature extraction operation can be output as a compressed binary label representative of the at least one surface manifold rather than the point data to afford a high-degree of compression for transmission or further processing thereof. Additionally, one or more voxels of a LIDAR point cloud composed of the point data can be scanned in order to recover the compressed binary label, which represents prototypical surface patches with respect to the LIDAR surface manifold(s).
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
G06N 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe
G06N 5/02 - Représentation de la connaissanceReprésentation symbolique
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
G06N 5/04 - Modèles d’inférence ou de raisonnement
A thermodynamic RAM technology stack, two or more memristors or pairs of memristors comprising AHaH (Anti-Hebbian and Hebbian) computing components, and one or more AHaH nodes composed of such memristor pairs that form at least a portion of the thermodynamic RAM technology stack. The levels of the thermodynamic-RAM technology stack include the memristor, a Knowm synapse, an AHaH node, a kT-RAM, kT-RAM instruction set, a sparse spike encoding, a kT-RAM emulator, and a SENSE Server.
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06N 3/06 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone
G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
A thermodynamic RAM apparatus includes a physical substrate of addressable adaptive synapses that are temporarily partitioned to emulate adaptive neurons of arbitrary sizes, wherein the physical substrate mates electronically with a digital computing platform for high-throughput and low-power neuromorphic adaptive learning applications. The physical substrate addressable adaptive synapses can be configured as a part of a memristor-based physical neural processing unit.
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
5.
Anti-hebbian and hebbian computing with thermodynamic RAM
A thermodynamic RAM circuit composed of a group of AHaH (Anti-Hebbian and Hebbian) computing circuits that form one or more kT-RAM circuits. The AHaH computing circuits can be configured as an AHaH computing stack. The kTRAM circuit(s) can include one or core kT-Cores, each partitioned into AHaH nodes of any size via time multiplexing. The kT-Core couples readout electrodes together to form a larger combined kT-Core. AHaH Computing is the theoretical space encompassing the capabilities of AHaH nodes. At this level of development, solutions have been found for problems as diverse as classification, prediction, anomaly detection, clustering, feature learning, actuation, combinatorial optimization, and universal logic.
H04L 12/28 - Réseaux de données à commutation caractérisés par la configuration des liaisons, p. ex. réseaux locaux [LAN Local Area Networks] ou réseaux étendus [WAN Wide Area Networks]
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
G06N 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe
6.
Methods and systems for Anti-Hebbian and Hebbian (AHaH) feature extraction of surface manifolds using
Methods and systems for feature extraction of LIDAR surface manifolds. LIDAR point data with respect to one or more LIDAR surface manifolds can be generated. An AHAH-based feature extraction operation can be automatically performed on the point data for compression and processing thereof. The results of the AHAH-based feature extraction operation can be output as a compressed binary label representative of the at least one surface manifold rather than the point data to afford a high-degree of compression for transmission or further processing thereof. Additionally, one or more voxels of a LIDAR point cloud composed of the point data can be scanned in order to recover the compressed binary label, which represents prototypical surface patches with respect to the LIDAR surface manifold(s).
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
G06N 99/00 - Matière non prévue dans les autres groupes de la présente sous-classe
G06N 5/02 - Représentation de la connaissanceReprésentation symbolique
A thermodynamic random access memory includes one or more AHaH (Anti-Hebbian and Hebbian) node wherein read out of data is accomplished via a common summing electrode through memristive components and wherein multiple input cells are simultaneously active. A ktRAM architecture comprising a memory wherein each input synapse or “bit” of the memory interacts on or with a common electrode through a common “dendritic” electrode, and wherein each input can be individually driven. Each input constitutes a memory cell driving a common electrode.
G06F 17/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
G06N 5/00 - Agencements informatiques utilisant des modèles fondés sur la connaissance
G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
8.
Framework for the evolution of electronic neural assemblies toward directed goals
Methods and systems for the evolution of electronic neural assemblies toward directed goals. A compact computing architecture includes electronics that allows users of such an architecture to create autonomous agents, in real or virtual world and add intelligence to machines. An intelligent machine is composed of four basic modules: one or more sensors, one or more motors, a (Reward Input Output System) RIOS and a cortex. A number of genetically evolved detectors can project both to cortex and RIOS. At first the neurons within the cortex evolve to predict the structure of the sensory data followed by the structure of proprioceptive activations of its own motor system. Finally, once the cortex has learned its sensory and motor programs, it evolves to predict the reward signals, which comes in multiple channels but is dominated by the detection of the acquisition of free-energy.
G06E 1/00 - Dispositions pour traiter exclusivement des données numériques
G06E 3/00 - Dispositifs non prévus dans le groupe , p. ex. pour traiter des données analogiques hybrides
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
A universal machine learning building block, comprising in some embodiments a differential pair of output electrodes, wherein each electrode comprises a plurality of input lines coupled to it via collections of meta-stable switches. In other embodiments, a methodology can be implemented in the context of hardware and/or software for deriving linear neurons implementing an AHaH plasticity rule, and generating an AHaH node(s) that can include one or more such linear neurons, wherein the AHaH node{s) functions according to an AHaH rule. Some embodiments can also include an AHaH classifier and/or AHaH cluster that include one or more such AHaH nodes.
A thermodynamic bit apparatus, method and system. A thermodynamic bit is a device that returns a true or false state with a probability that depends on its internal state, which can be controlled via the application of positive feedback. A thermodynamic bit can include two or more memristors connected in series. A forward bias can be applied to the thermodynamic bit to read the state of the thermodynamic bit. A negative feedback can be applied to the thermodynamic bit during application of a forward bias to the thermodynamic bit. Also, a reverse bias can be applied to the thermodynamic bit to refresh or reinforce the state of the thermodynamic bit.
An extensible adaptive classification framework and method can include multiple feature detection modules, and a platform for integrating the multiple feature detection modules utilizing a plurality of AHaH nodes as adaptive classifiers over a feature space of multiple and extensible feature factory modules, thereby configuring the platform as an extensible and continuously adaptive pattern recognition platform.
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
A fractal flow fabric can include a substrate for growing at least one procedure, and a plurality of nodes formed on said substrate, wherein each node among said plurality of nodes connects to each other through at least one flow-stabilized link to grow said at least one procedure and automatically solve a problem via said at least one procedure.
Methods and systems for thermodynamic evolution. Adaptive control systems are constructed based on the property of volatile matter to self-organize to maximize the dissipation of energy. The logical state of sensory nodes in a node circuit are set and projected into a network. Then, the system evaluates logical state of processing nodes by summing input currents of processing nodes and project processing node's state into network. The strength of processing node is increased such that logical state of sensory node matches with logical states of processing node by utilizing plasticity rule. The system is configured to maximize energy dissipation by creating weight structures to stabilize nodes with logical state. The internal positive feedback of node circuit forces competition between nodes such that one node is driven to high logical state and other nodes to low logical state.
Methods and systems for constructing biological-scale hierarchically structured cortical statistical memory systems utilizing fabrication technology and meta-stable switching devices. Learning content-addressable memory and statistical random access memory circuits are detailed. Additionally, local and global signal modulation of bottom-up and top-down processing for the initiation and direction of behavior is disclosed.
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
15.
Framework for the evolution of electronic neural assemblies toward directed goals
Methods and systems for the evolution of electronic neural assemblies toward directed goals. A compact computing architecture includes electronics that allows users of such an architecture to create autonomous agents, in a real or a virtual world and add intelligence to machines. An intelligent machine is composed of four basic modules: one or more sensors, one or more motors, a (Reward Input Output System) RIOS, and a cortex. A number of genetically evolved detectors can project both to cortex and RIOS. At first the neurons within the cortex evolve to predict the structure of the sensory data followed by the structure of proprioceptive activations of its own motor system. Finally, once the cortex has learned its sensory and motor programs, it evolves to predict the reward signals, which comes in multiple channels but is dominated by the detection of the acquisition of free-energy.
G06E 1/00 - Dispositions pour traiter exclusivement des données numériques
G06E 3/00 - Dispositifs non prévus dans le groupe , p. ex. pour traiter des données analogiques hybrides
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
An emotional memory control system and method for generating behavior. A sensory encoder provides a condensed encoding of a current circumstance received from an external environment. A memory associated with a regulator recognizes the encoding and activates one or more emotional springs according to a predefined set of instructions. The activated emotional springs can then transmit signals to at least one moment on a fractal moment sheet incorporated with a timeline for each channel in order to form one or more watersheds. An activation magnitude can be calculated for each moment and transmitted to a reaction relay. A synaptic link can then form between the moment and a motor encoder, thereby linking a specific moment with a specific action state.
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
G06E 1/00 - Dispositions pour traiter exclusivement des données numériques
G06E 3/00 - Dispositifs non prévus dans le groupe , p. ex. pour traiter des données analogiques hybrides
G06G 7/00 - Dispositifs dans lesquels l'opération de calcul est effectuée en faisant varier des grandeurs électriques ou magnétiques
Methods and systems for modifying at least one synapse of a physicallelectromechanical neural network. A physical/electromechanical neural network implemented as an adaptive neural network can be provided, which includes one or more neurons and one or more synapses thereof, wherein the neurons and synapses are formed from a plurality of nanoparticles disposed within a dielectric solution in association with one or more pre-synaptic electrodes and one or more post-synaptic electrodes and an applied electric field. At least one pulse can be generated from one or more of the neurons to one or more of the pre-synaptic electrodes of a succeeding neuron and one or more post-synaptic electrodes of one or more of the neurons of the physical/electromechanical neural network, thereby strengthening at least one nanoparticle of a plurality of nanoparticles disposed within the dielectric solution and at least one synapse thereof.
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
A universal logic gate apparatus is disclosed, which include a plurality of self-assembling chains of nanoparticles having a plurality of resistive connections, wherein the plurality of self-assembling chains of nanoparticles comprise resistive connects utilized to create A plasticity mechanism is also provided, which is based on a plasticity rule for creating stable connections from the plurality of self-assembling chains of nanoparticles for use with the universal, reconfigurable logic gate. The plasticity mechanism can be based, for example, on a 2-dimensional binary input data stream, depending upon design considerations. A circuit is also associated with the plurality of self-assembling chains of nanoparticles, wherein the circuit provides a logic bypass that implements a flip-cycle for second-level logic. Additionally, an extractor logic gate is associated with the plurality of self-assembling chains of nanoparticles, wherein the extractor logic gate provides logic functionalities.
H03K 19/20 - Circuits logiques, c.-à-d. ayant au moins deux entrées agissant sur une sortieCircuits d'inversion caractérisés par la fonction logique, p. ex. circuits ET, OU, NI, NON
H03K 19/00 - Circuits logiques, c.-à-d. ayant au moins deux entrées agissant sur une sortieCircuits d'inversion
A universal logic gate apparatus is disclosed, which include a plurality of self-assembling chains of nanoparticles having a plurality of resistive connections, wherein the plurality of self-assembling chains of nanoparticles comprise resistive connects utilized to create a universal, reconfigurable logic gate A plasticity mechanism is also provided, which is based on a plasticity rule for creating stable connections from the plurality of self-assembling chains of nanoparticles for use with the universal, reconfigurable logic gate. The plasticity mechanism can be based, for example, on a 2-dimensional binary input data stream, depending upon design considerations. A circuit is also associated with the plurality of self-assembling chains of nanoparticles, wherein the circuit provides a logic bypass that implements a flip-cycle for second-level logic. Additionally, an extractor logic gate is associated with the plurality of self-assembling chains of nanoparticles, wherein the extractor logic gate provides logic functionalities.
H03K 19/20 - Circuits logiques, c.-à-d. ayant au moins deux entrées agissant sur une sortieCircuits d'inversion caractérisés par la fonction logique, p. ex. circuits ET, OU, NI, NON
20.
Methodology for the configuration and repair of unreliable switching elements
A universal logic gate apparatus is disclosed, which include a plurality of self-assembling chains of nanoparticles having a plurality of resistive connections, wherein the plurality of self-assembling chains of nanoparticles comprise resistive elements. A plasticity mechanism is also provided, which is based on a plasticity rule for creating stable connections from the plurality of self-assembling chains of nanoparticles for use with the universal, reconfigurable logic gate. In addition, the universal logic gate can be configured with a cross-bar architecture, where nanoconnections are formed from a columbic-educed mechanical stress contact.
A system for independent component analysis includes a feedback mechanism based on a plasticity rule, and an electro-kinetic induced particle chain, wherein the feedback mechanism and the electro-kinetic induced particle chain is utilized to extract independent components from a data set or data stream. The electro-kinetic induced particle chain is generally composed of a plurality of interconnected nanoconnections (e.g., nanoparticles) disposed between at least two electrodes in a solution, including for example one or more pre-synaptic electrodes and one or more post-synaptic electrodes. The feedback mechanism generally provides feedback to one or more particles within the electro-kinetic induced particle chain, while the plasticity rule can be non-linear in nature. The feedback mechanism also provides for one or more evaluate phases and one or more feedback phases.
G06E 1/00 - Dispositions pour traiter exclusivement des données numériques
G06E 3/00 - Dispositifs non prévus dans le groupe , p. ex. pour traiter des données analogiques hybrides
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
G06G 7/00 - Dispositifs dans lesquels l'opération de calcul est effectuée en faisant varier des grandeurs électriques ou magnétiques
22.
Fractal memory and computational methods and systems based on nanotechnology
Fractal memory systems and methods include a fractal tree that includes one or more fractal trunks. One or more object circuits are associated with the fractal tree. The object circuit(s) is configured from a plurality of nanotechnology-based components to provide a scalable distributed computing architecture for fractal computing. Additionally, a plurality of router circuits is associated with the fractal tree, wherein one or more fractal addresses output from a recognition circuit can be provided at a fractal trunk by the router circuits.
G06E 1/00 - Dispositions pour traiter exclusivement des données numériques
G06E 3/00 - Dispositifs non prévus dans le groupe , p. ex. pour traiter des données analogiques hybrides
G06F 15/18 - dans lesquels un programme est modifié en fonction de l'expérience acquise par le calculateur lui-même au cours d'un cycle complet; Machines capables de s'instruire (systèmes de commande adaptatifs G05B 13/00;intelligence artificielle G06N)
G06G 7/00 - Dispositifs dans lesquels l'opération de calcul est effectuée en faisant varier des grandeurs électriques ou magnétiques
brevets
