Abstract: A computer-implemented method for building a semantic analysis model. In one embodiment, the computer-implemented method includes creating proxy tags comprising a set of surface form variants. The computer-implemented method creates training examples comprising a combination of terminal tokens and at least one of the proxy tags. The computer-implemented method builds the semantic analysis model using the training examples.

Abstract: An event recommendation system recommends events for a candidate attendee. The system recommends an event based on characteristics of the candidate attendee and characteristics of prior attendees that attended a prior occurrence of the event. A prior attendee may have a positive or negative experience with the prior occurrence of the event. A positive experience may be defined, for example, as enjoying the event, completing the event, or performing well in the event. A negative experience may be defined, for example, as not enjoying the event, not completing the event, or not performing well in the event. An event is recommended to a candidate attendee if the candidate attendee has similar characteristics to a prior attendee that had a positive experience. An event is not recommended to a candidate attendee if the candidate attendee has similar characteristics as a prior attendee that had a negative experience.

Abstract: Implementations include providing, by the PKG platform, an initial knowledge graph based on user-specific data associated with a user, and a domain-specific knowledge graph, receiving, by the PKG platform, data representative of at least one answer provided from the user to a respective question, providing, by the PKG platform, an expanded knowledge graph based on the initial knowledge graph, the expanded knowledge graph including one or more nodes and respective edges based on the data, generating, by the PKG platform, a weighted knowledge graph based a groundtruth knowledge graph, and a targeted knowledge graph, the groundtruth knowledge graph including one or more true answers, and the targeted knowledge graph including the at least one answer provided from the user, and generating, by the PKG platform, the hyper-personalized knowledge graph (hpKG) based on the weighted knowledge graph, the hpKG being unique to the user within a domain.

Abstract: A method of tracking information flows through multiple network systems includes selecting a primary network system from a population of primary and secondary network systems, wherein each of the primary and secondary network systems include network nodes, selecting first selected characteristic features that identify network nodes of the primary network system that provide interaction between the selected primary network system and secondary network systems, identifying at least one secondary network system that is capable of interacting with the network nodes of the primary network system, subdividing the primary network into subnetwork systems based on identifying primary network nodes that provide interaction between the primary network system and secondary network nodes, identifying the subnetwork systems that are capable of interacting with one or more network nodes of the secondary network systems, identifying a subnetwork node count of the primary network nodes in each subnetwork, identifying objects

Abstract: At an artificial intelligence system, respective status indicators for classifier training iterations are determined. A visualization data set comprising the status indicators is presented via an interactive programmatic interface. A training enhancement action, based at least partly on an objective associated with a status indicator, is initiated. The action includes selecting data items for which labeling feedback is to be obtained programmatically during one or more classifier training iterations. A classification model that is trained using the labeling feedback obtained as a result of the action is stored.

Abstract: A method including receiving input data; selecting a classification scheme; transforming the input data into transformation data utilizing the classification scheme; transforming the input data into machine learner outputs; comparing the transformation data to the machine learner outputs; and altering machine state of one or more machines in response to comparing the transformation data to the machine learner outputs. Further, a method including receiving one or more sensor inputs; receiving one or more machine insights, the one or more machine insights comprising one or more states; selecting one of the one or more states; determining conditions of the one of the one or more states; comparing the conditions to the one or more sensor inputs; and altering a machine state of one or more machines in response to comparing the conditions to the one or more sensor inputs.

Abstract: This document describes techniques for predicting spread of content across social networks. In various embodiments, user interactions with content posted to a social network are accessed during a first stage. The accessed user interactions are applied to a prediction model to predict future user interactions with the content during one or more subsequent stages.

Abstract: A computer implemented method of grouping media objects is provided, as well as systems, interfaces and devices therefor. The method includes generating a group from the media objects based on a combination of a script of sequential events and an actor associated with one or more of the media objects in the script, segmenting the group into segments each including one or more of the media objects, based on clustering or classification, providing titling and captioning for the segments, and generating filter and annotation recommendations based on knowledge associations in the media objects, data, and the combination of the script and the actor, across the media objects of the group.

Abstract: In one embodiment, the present invention provides a neural network comprising multiple modalities. Each modality comprises multiple neurons. The neural network further comprises an interconnection lattice for cross-associating signaling between the neurons in different modalities. The interconnection lattice includes a plurality of perception neuron populations along a number of bottom-up signaling pathways, and a plurality of action neuron populations along a number of top-down signaling pathways. Each perception neuron along a bottom-up signaling pathway has a corresponding action neuron along a reciprocal top-down signaling pathway. An input neuron population configured to receive sensory input drives perception neurons along a number of bottom-up signaling pathways. A first set of perception neurons along bottom-up signaling pathways drive a first set of action neurons along top-down signaling pathways.

Abstract: In one embodiment, a method includes generating embeddings for social-networking entities by training the embeddings using a training algorithm, where an embedding corresponding to an entity represents a point in a d-dimensional embedding space, identifying a subset of entities having one or more common attributes that is not encoded in the generated embeddings, encoding, for each entity in the subset, values of the one or more common attributes into a j-dimensional additional embedding, creating, for each entity in the subset, a (d+j)-dimensional embedding by concatenating the generated d-dimensional embedding with the j-dimensional additional embedding, detecting a need to identify entities similar to a reference entity that is a member of the subset, computing k-nearest neighbors of an embedding corresponding to the reference entity in the (d+j)-dimensional embedding space, identifying entities corresponding to the computed k-nearest neighbors, and providing information regarding the corresponding entities

Abstract: Methods, systems, and computer program products for constraint tracking and inference generation are provided herein. A computer-implemented method includes parsing descriptions of one or more user-provided constraints pertaining to data within a target system, parsing truth value assignments to the user-provided constraints, and deriving a truth value for at least one of the user-provided constraints that does not correspond to a known truth value, wherein said deriving comprises performing a logical inference utilizing known truth values of one or more of the user-provided constraints. The computer-implemented method also includes storing, in a database, (i) the user-provided constraints, (ii) the known truth values, and (iii) the at least one derived truth value, and outputting the at least one derived truth value, one or more identified contradictions among the known truth values, and/or an indication that one or more unknown truth values corresponding to the user-provided constraints remain unknown.

Abstract: Methods and apparatuses for search and content creation. A partial input is received via a user interface of an electronic computing device. The partial input to be used for content creation. A search query is generated in response to receiving the partial input. A structured data repository is searched for objects matching the partial input. Results from the searching of the data repository are provided. Suggested content to be created from the displayed results is generated.

Abstract: Systems and method for perturbing a system include obtaining directed acyclic/cyclic graph candidates {GI, . . . , GN} for the system. Each Gi in {Gj, . . . GN} includes a causal relationship between a parent and child node. {GI, GN} demonstrate Markov equivalence. Observed data D is obtained for the nodes. For each respective Gi, the marginal probability of a parent node xi in Gi is clamped by D while computing a distribution of marginal probabilities for a child node yi, by Bayesian network or Dynamic Bayesian network belief propagation using an interaction function. The observed distribution for the child node yi, in D and the computed distribution of marginal probabilities for the child node yi are scored using a nonparametric function, and such scores inform the selection of a directed/cyclic graph from {GI, . . . , GN}. The system is perturbed using a perturbation that relies upon a causal relationship in the selected directed acyclic/cyclic graph.

Abstract: Roughly described, the technology disclosed provides a so-called machine-learned conversion optimization (MLCO) system that uses artificial neural networks and evolutionary computations to efficiently identify most successful webpage designs in a search space without testing all possible webpage designs in the search space. The search space is defined based on webpage designs provided by marketers. Neural networks are represented as genomes. Neural networks map user attributes from live user traffic to different dimensions and dimension values of output funnels that are presented to the users in real time. The genomes are subjected to evolutionary operations like initialization, testing, competition, and procreation to identify parent genomes that perform well and offspring genomes that are likely to perform well.

Abstract: A computer-implemented method comprising receiving and analyzing a data point to determine parameters of the data point, generating an alert ticket based on the analysis of the data point, communicating, via a chatbot, at least some information contained in the alert ticket to one or more users, and categorizing, via the chatbot, the data point that resulted in the alert ticket based on behavior of a device that generated the data point.

Abstract: Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths.

Abstract: An analytics server for scalable predictive analysis for analytics as a software service in multi-tenant environment is provided. The analytics server automatically validates portability of a predictive model from a first tenant to a second tenant by comparing value distribution of parameters between data inputs of the first tenant and the second tenant. The analytics server further automatically detects source data changes over a configurable time horizon as relevant to predictive model inputs, by comparing value distribution of parameters between two data inputs from a same tenant separated by a selected time horizon.

Abstract: A virtual cognitive agent (VCA) system comprises social media communication channels integrated with machine cognition engines. The VCA system connects to an external message platform and accesses posted messages. An intent is determined and sentiment analysis is performed on text elements from the intercepted messages to determine handling of the message. The integrated machine cognition engines determine a response to the captured message. The VCA system may access a corpus or exchange data with the originator of the post or another entity to determine the response. The integrated social media communication channels may connect to an external query response platform and communicate the response to the query response platform.

Abstract: In one aspect, the inventions include a system for control of a software defined computer network state system. First, an application plane layer is adapted to receive instructions regarding operation of the state system. Preferably, the application plane layer is coupled to an application plane layer interface. Second, a control plane layer includes an adaptive control unit, such as a cognitive computing unit, an artificial intelligence unit or a machine-learning unit. Third, a data plane layer includes an input interface to receive data input from one or more data sources. An adaptive control unit is trained at least in part on analyzing the behavioral responses of users in response to content provided on one or more displays to the users as sensed by one or more sensors.

Abstract: Systems and methods of the present disclosure can involve several work spaces wherein each of the work spaces are associated with a set of activities and wherein each of the work spaces are coupled to one or more radio frequency (RF) sensors. Through RF sensor data detected from interactions with the work surface, example implementations described herein can determine which activity from the set of activities is being conducted through the application of a recognition algorithm that is generated from a machine learning algorithm.