The example embodiments are directed to a system and method for deploying a machine learning model using a parse-free memory allocation. In one example, the method may include one or more of receiving a request to deploy a machine learning model, in response to receiving the request, creating a memory map comprising a mapping of a data structure for storing an unpacked flat representation of the machine learning model, allocating a contiguous block of memory of the data structure that is mapped by the memory map, loading data blocks of the unpacked flat representation of the machine learning model into the allocated contiguous blocks of memory of the data structure, and storing an offset associated with the contiguous block of memory in storage.

A system and corresponding method are provided for generating responses for a dialogue between a user and a computer. The system includes a memory storing information for a dialogue history and a knowledge base. An encoder may receive a new utterance from the user and generate a global memory pointer used for filtering the knowledge base information in the memory. A decoder may generate at least one local memory pointer and a sketch response for the new utterance. The sketch response includes at least one sketch tag to be replaced by knowledge base information from the memory. The system generates the dialogue computer response using the local memory pointer to select a word from the filtered knowledge base information to replace the at least one sketch tag in the sketch response.

A system and corresponding method are provided for generating responses for a dialogue between a user and a computer. The system includes a memory storing information for a dialogue history and a knowledge base. An encoder may receive a new utterance from the user and generate a global memory pointer used for filtering the knowledge base information in the memory. A decoder may generate at least one local memory pointer and a sketch response for the new utterance. The sketch response includes at least one sketch tag to be replaced by knowledge base information from the memory. The system generates the dialogue computer response using the local memory pointer to select a word from the filtered knowledge base information to replace the at least one sketch tag in the sketch response.

A method and system for verifying state monitor reliability in hyper-converged infrastructure (HCI) appliances. Specifically, the method and system disclosed herein entail using a supervised machine learning model—i.e., a classification decision tree—to accurately distinguish whether conflicting event notifications, logged across multiple state monitors tracking state on an HCI appliance, are directed to a real event or a non-real event. The classification decision tree, generated based at least on information gains calculated for the multiple state monitors, may reflect which state monitor(s) is/are more reliable in accurately classifying the conflicting event notifications.

Devices and techniques are generally described for ranking of search results based on multiple objectives. A first ranking for a plurality of search results is determined using a first machine learning model optimized for a first objective for ranking search results. A second objective for ranking search results is determined. A constraint is determined for the at least one second objective. The first machine learning model is iteratively updated to generate an updated machine learning model by minimizing a cost of the first objective subject to the constraint, wherein violations of the constraint are penalized using a penalty term. A second ranking for the plurality of search results is determined using the updated machine learning model. The search results of the second ranking are reordered relative to the search results of the first ranking.

An inferential knowledge construction support apparatus 1 has a literal generation unit 2 that generates literal information based on the extracted elements, a rule generation unit 3 that estimates a causal or implicational relation between literals, using plural pieces of literal information, and generates rule information by allocating literal information estimated to be in the causal/implicational relation to an antecedent and a consequent, a display information generation unit 4 that generates display information to be used for causing a display device to output a rule editing user interface for displaying, in a juxtaposed manner, a literal display area for displaying the literal information and descriptive information corresponding to the literal information and a rule display area for displaying the rule information and descriptive information corresponding to the rule information, and an editing unit 5 that enables an operator to edit the rule information, using the rule editing user interface.

Systems and methods for optimizing clearances within an engine include an adjustable coupling configured to couple a thrust link to the aircraft engine, an actuator coupled to the adjustable coupling, where motion produced by the actuator adjusts a hinge point of the adjustable coupling, sensors configured to capture real time flight data, and an electronic control unit. The electronic control unit receives flight data from the sensors, implements a machine learning model trained to predict clearance values within the engine based on the received flight data, predicts, with the machine learning model, the clearance values within the engine based on the received flight data, determines an actuator position based on the clearance values, and causes the actuator to adjust to the determined actuator position.

Apparatuses, systems, and techniques to perform a language-guided distributional tree search based at least in part on a natural language task. In at least one embodiment, a tree search is performed using one or more neural networks to determine an action to be performed by an autonomous agent.

The invention concerns a query response device comprising: an input adapted to receive user queries; a memory (106) adapted to store one or more routing rules; one or more live agent engines (116) configured to support interactions with one or more live agents; one or more virtual assistant engines (120) configured to support interactions with one or more virtual assistants instantiated by an artificial intelligence module (103); and a routing module (104) coupled to said live agent engines and to said virtual assistant engines, the routing module comprising a processing device configured: to select, based on content of at least a first user message from a first user relating to a first user query and on said one or more routing rules, a first of said live agent engines or a first of said virtual assistant engines; and to route one or more further user messages relating to the first user query to the selected engine.

The invention concerns a query response device comprising: an input adapted to receive user queries; a memory (106) adapted to store one or more routing rules; one or more live agent engines (116) configured to support interactions with one or more live agents; one or more virtual assistant engines (120) configured to support interactions with one or more virtual assistants instantiated by an artificial intelligence module (103); and a routing module (104) coupled to said live agent engines and to said virtual assistant engines, the routing module comprising a processing device configured: to select, based on content of at least a first user message from a first user relating to a first user query and on said one or more routing rules, a first of said live agent engines or a first of said virtual assistant engines; and to route one or more further user messages relating to the first user query to the selected engine.