Abstract: A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.

Abstract: Techniques facilitating guiding machine learning models and related components are provided. In one example, a computer-implemented method comprises identifying, by a device operatively coupled to a processor, a set of models, wherein the set of models includes respective model components; determining, by the device, one or more model relations among the respective model components, wherein the one or more model relations respectively comprise a vector of component relations between respective pairwise ones of the model components; and suggesting, by the device, a subset of the set of models based on a mapping of the component relations.

Abstract: A collection of code fragments loaded in an interactive development platform for running on a first processor can be received. A candidate fragment in the collection of code fragments can be determined for migration to a second processor based on characterizing the collection of code fragments. Based on a location of the candidate fragment in the collection of code fragments, a spot can be identified in the collection of code fragments to inject a code for saving program context. The code for saving program context can be injected in the identified spot. Responsive to the code for saving program context having run on the first processor and based on a criterion, the program context can be migrated to the second processor.

Abstract: Devices that consume power may be individually and uniquely associated with a blockchain wallet into which funds may be transferred. The device can be provided with a client application that executes within the device to determine an energy provider and the cost rate of energy supply. The client application calculates a value of energy consumption by the device and creates a blockchain transaction to transfer a funds value for the energy consumption from the device's blockchain wallet to a wallet of the energy provider. By providing devices that can self-manage their electricity supply and the accounting thereof, billing of power can be decentralized from a meter that meters all supply to a premises to individual devices. The requirement for a central billing entity, billing address, etc. can also be removed.

Abstract: A machine-learning model task deduction method, system, and computer program product include extracting data schema of a machine-learning model and analyzing the data schema to determine an intended task of the machine-learning model.

Abstract: A conversational agent capability assessment method, system, and computer program product, includes assessing a performance, a personality and a cognitive trait of a conversational agent based on natural written language by a second conversational agent by combining an analysis of a plurality of metrics that each compare a metric from the conversational agent with a metric from the second conversational agent and producing a report detailing the performance, the personality, and the cognitive trait of the conversational agent.

Abstract: A method of deploying artificial intelligence (AI) model resources includes storing at least one AI model in a model store memory in a plurality of different versions, each different version having a different level of fidelity. When a request to exercise the AI model is received, a processor determines which version of the AI model to exercise for the received request. The determined AI model version is used to serve the received request by exercising input data accompanying the received request. The result of the exercised AI model version is used to respond to the received request.

Abstract: A method for answering a natural-language user query includes receiving a query from a user by a central agent, parsing the query, and determining if an answer is known to the central agent as being within a local database. If the answer is known, the query is answered with a known natural-language answer using the local database. If not, artificial conversational entity (ACE) invocation is performed. The ACE invocation includes determining a topical field for the query, determining an ACE, of a plurality of ACEs, to send the query to, based on the topical field, and sending the query to the ACE. If the ACE is capable of answering, a natural-language answer is received from the ACE and presented to the user. If not, a natural-language answer is created using social knowledge and presented to the user. Social knowledge includes data crawled from subscriber-provided content of social media services.

Abstract: Devices that consume power may be individually and uniquely associated with a blockchain wallet into which funds may be transferred. The device can be provided with a client application that executes within the device to determine an energy provider and the cost rate of energy supply. The client application calculates a value of energy consumption by the device and creates a blockchain transaction to transfer a funds value for the energy consumption from the device's blockchain wallet to a wallet of the energy provider. By providing devices that can self-manage their electricity supply and the accounting thereof, billing of power can be decentralized from a meter that meters all supply to a premises to individual devices. The requirement for a central billing entity, billing address, etc. can also be removed.

Abstract: Identifying life events within social network feeds. The method may include receiving social media data. The method may include identifying life event data within the social media data. The method may include determining a life event probability score associated with a life event class for the life event data using metadata. The method may include assigning a first life event class to each item of life event data based on the life event probability score. The method may include creating conversations by grouping the life event data. The method may include extracting metadata. The method may include determining a conversation probability score for each conversation based on the metadata and the life event probability score associated with each item of life event data. The method may include assigning a second life event class based on the conversation probability score. The method may include displaying the conversations based on user preferences.

Abstract: Techniques facilitating guiding machine learning models and related components are provided. In one example, a computer-implemented method comprises identifying, by a device operatively coupled to a processor, a set of models, wherein the set of models includes respective model components; determining, by the device, one or more model relations among the respective model components, wherein the one or more model relations respectively comprise a vector of component relations between respective pairwise ones of the model components; and suggesting, by the device, a subset of the set of models based on a mapping of the component relations.

Abstract: Identifying life events within social network feeds. The method may include receiving social media data. The method may include identifying life event data within the social media data. The method may include determining a life event probability score associated with a life event class for the life event data using metadata. The method may include assigning a first life event class to each item of life event data based on the life event probability score. The method may include creating conversations by grouping the life event data. The method may include extracting metadata. The method may include determining a conversation probability score for each conversation based on the metadata and the life event probability score associated with each item of life event data. The method may include assigning a second life event class based on the conversation probability score. The method may include displaying the conversations based on user preferences.

Abstract: A conversational agent capability assessment method, system, and computer program product, includes obtaining data to create at least one scenario for testing a conversational agent, performing a set of tests using a scenario of the at least one scenario created to assess a capability of the conversational agent, and comparing a result of the capability from the set of tests with an expected result of the scenario.

Abstract: A conversational agent capability assessment method, system, and computer program product, includes assessing a performance, a personality and a cognitive trait of a conversational agent based on natural written language by a second conversational agent by combining an analysis of a plurality of metrics that each compare a metric from the conversational agent with a metric from the second conversational agent and producing a report detailing the performance, the personality, and the cognitive trait of the conversational agent

Abstract: A system, method and program product for arranging a set of nodes having disparate cognitive learning capabilities in a space. A method is provided that includes: receiving node related inputs that include structured data and unstructured data; identifying existing relationships and interactions among the nodes by analyzing external resource data; generating a cognitive profile for each node in the set of nodes based on the node related inputs; determining a compatibility for each pair of nodes in the set of nodes based on generated cognitive profiles and existing relationships and interactions; and calculating an arrangement based on the compatibility determined for each pair of nodes.

Abstract: Backup and restore of digital data may utilize online social network parameters of a user to determine a number of parts into which to divide digital data and distribute the parts. A list of friends from the social network friends may be selected, e.g., based on a relationship to the user in the social network, to store the parts. The parts may be stored in a friend's device memory, each of the parts stored with a different friend. The digital data may be reconstructed by obtaining a threshold number of parts from the friends.

Abstract: Backup and restore of digital data may utilize online social network parameters of a user to determine a number of parts into which to divide digital data and distribute the parts. A list of friends from the social network friends may be selected, e.g., based on a relationship to the user in the social network, to store the parts. The parts may be stored in a friend's device memory, each of the parts stored with a different friend. The digital data may be reconstructed by obtaining a threshold number of parts from the friends.

Abstract: A conversational agent capability assessment method, system, and computer program product, includes obtaining data to create at least one scenario for testing a conversational agent, performing a set of tests using a scenario of the at least one scenario created to assess a capability of the conversational agent, and comparing a result of the capability from the set of tests with an expected result of the scenario.

Abstract: A method for answering a natural-language user query includes receiving a query from a user by a central agent, parsing the query, and determining if an answer is known to the central agent as being within a local database. If the answer is known, the query is answered with a known natural-language answer using the local database. If not, artificial conversational entity (ACE) invocation is performed. The ACE invocation includes determining a topical field for the query, determining an ACE, of a plurality of ACEs, to send the query to, based on the topical field, and sending the query to the ACE. If the ACE is capable of answering, a natural-language answer is received from the ACE and presented to the user. If not, a natural-language answer is created using social knowledge and presented to the user. Social knowledge includes data crawled from subscriber-provided content of social media services.

Abstract: A system for coordinating multiple chatbots in a group conversation using natural dialog systems, including a creation unit that enables a user to create a group chat with chatbots; a response unit that allows the user to reply to any utterance extracted from a message received from a member of the group chat; a transmission unit that sends messages to every member of the group chat; a development unit that develops chatbots that understand natural language and interact in a group chat using natural dialogue; a network connection; a first database that stores a knowledge base extracted from all the utterances exchanged by members of the group chat; a second database that stores all interactions between the user and the group chat, and a third database that stores all interaction protocols used by the members of the group chat.