The present disclosure is generally directed to the generation of domain-specific, voice-activated systems in interconnected networks. The system can receive input signals that are detected at a client device. The input signals can be voice-based input signals, text-based input signals, image-based input signals, or other type of input signals. Based on the input signals, the system can select domain-specific knowledge graphs and generate responses based on the selected knowledge graph.

A memory stores graph information representing a graph that includes nodes and inter-node edges. The nodes include a first plurality of nodes each associated with node information and a first node. Each of the inter-node edges has a weight. A processor extracts, in accordance with the node information, two or more nodes and transforms the two or more nodes into an aggregate node. The processor generates an aggregate inter-node edge between the aggregate node and the first node. The aggregate inter-node edge is associated with a weight based on two or more weights associated with two or more inter-node edges between the two or more nodes and the first node. The processor estimates first node information to be associated with the first node based on transformed graph information representing a transformed graph including the aggregate node and the aggregate inter-node edge.

The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

The present disclosure describes systems and methods that include calculating, via a reinforcement learning agent (RLA) controller, a plurality of state-action values based on sensor data representing an observed state, wherein the RLA controller utilizes a deep neural network (DNN) and generating, via a fuzzy controller, a plurality of linear models mapping the plurality of state-action values to the sensor data.

In a database environment including a plurality of logical object definitions having relationships defined according to a schema, and logical object instances following the logical object definitions include attribute names and respective attribute values indicating status of an enterprise in an enterprise resource planning system, the method can receive a starting exception definition specifying a first query against the logical object instances and derive a new exception definition based on the starting exception definition and one or more stored, acted-upon exception definition proposals. The first query can include one or more initial situational trigger conditions. The new exception definition can specify a second query against the logical object instances and the second query can include one or more modified situational trigger conditions.

Aspects of the disclosure relate to computing platforms that utilize machine learning to reduce false positive/negative collision output generation. A computing platform may apply machine learning algorithms on received data to generate a collision output. In response to generating the collision output indicating a collision, the computing platform may identify a data collection location. If the data collection location is within a predetermined radius of a false positive collection location, the computing platform may modify the collision output to indicate a non-collision. If the data collection location is not within the predetermined radius, the computing platform may compute a score using telematics data and compare the score to a predetermined threshold. If the score does not exceed the predetermined threshold, the computing platform may modify the collision output to indicate a non-collision. If the score exceeds the predetermined threshold, the computing platform may affirm the collision output indicating a collision.

Performing quantum file copying is disclosed herein. In one example, upon receiving a request to copy a source quantum file comprising a plurality of source qubits, a quantum file manager accesses a quantum file registry record identifying the plurality of source qubits and a location of each of the plurality of source qubits. The quantum file manager next allocates a plurality of target qubits equal in number to the plurality of source qubits, and copies data stored by each of the source qubits into a corresponding target qubit. The quantum file manager then generates a target quantum file registry record that identifies the plurality of target qubits and their locations. In some examples, a quantum file move operation may be performed by deleting the source quantum file after the copy operation, and updating the target quantum file registry record with the same quantum file identifier as the source quantum file.

A method and system identify characteristics of transaction description strings. The method and system extracts time data and location data from transaction description strings. The method and system generate estimated time data and location data for transaction strings that lack time data and location data by analyzing the time data and location data extracted from other transaction description strings. The method and system generate a user profile based on the estimated time data and estimated location data.

A platform receives an input document from a user device and automatically determines a semantic signature for the input document based on a probabilistic distribution of rare words within the input document. The platform automatically scrapes at least one Internet database for additional documents and webpages, determining semantic signatures for each document or webpage. Based on similarity of semantic signatures, the platform automatically constructs and displays a graphical network of documents, wherein each document is represented as a node and similarity of semantic signatures is used to determine the locations of edges between nodes. The graph automatically groups nodes by communities and selects nodes in different communities to promote serendipity of results.

A method in job processing server of processing database updates includes: storing, at a job processing server, a job queue including a plurality of job records, each job record having corresponding job parameters; detecting job initiation data at a data source; responsive to detecting the job initiation data, retrieving new job parameters from the data source based on the job initiation data; creating a new job record including the new job parameters in the job queue; and responsive to a predefined trigger, for each job in the job queue, processing the job based on corresponding job parameters, wherein processing the job includes sending instructions for execution by a second server, the instructions for performing an update at the second server.