Abstract: In an approach to composing a machine learning model for complex data sources, a computer receives data and associated metadata corresponding to a machine learning task from a user. A computer determines a task context and a problem domain. A computer identifies the machine learning task. A computer evaluates a match between the problem domain and one or more pre-compiled models. A computer selects at least two of the one or more pre-compiled models. A computer generates one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models. A computer executes the multimodal model combinations with the data and associated metadata. A computer displays the results of the executed one or more multimodal model combinations to the user. A computer determines whether a level of error associated with the results is acceptable to the user based on a response from the user.

Abstract: Chatbot logs can be received. The chatbot logs can be clustered or classified into clusters of potential intents. Trending topics can be detected by analyzing at least social media content. Semantic similarities between the potential intents and the trending topics can be computed. Based on the computed semantic similarities, at least one of the potential intents to at least one of the trending topics can be correlated. Training of the chatbot using the chatbot logs associated with the correlated potential intent can be triggered.

Abstract: A topic for building a new intent on which to train a chatbot can be received. A database of chatbot training data can be searched for a candidate intent having meta-knowledge similar to the received topic. Utterances associated with the candidate intent can be extracted. The received topic and the extracted utterances can be input to a trained machine learning model. The trained machine learning model generates example utterances for the new intent. The new intent with the generated example utterances can be used as training data for training the chatbot.

Abstract: Chatbot logs can be received. The chatbot logs can be clustered or classified into clusters of potential intents. Trending topics can be detected by analyzing at least social media content. Semantic similarities between the potential intents and the trending topics can be computed. Based on the computed semantic similarities, at least one of the potential intents to at least one of the trending topics can be correlated. Training of the chatbot using the chatbot logs associated with the correlated potential intent can be triggered.

Abstract: A method of automatically creating new intents and assigning examples to those new intents in a dialog system includes generating at least one predicted intent from an intent concept graph by combining two or more nodes of the intent concept graph. A graph embedding is generated of the at least one predicted intent. A sentence embedding is generated from examples from a call log. The graph embedding of the at least one predicted intent is then matched with the examples from the sentence embedding and the at least one predicted intent, with the examples, is connected into a dialog system.

Abstract: An approach to identifying architectures of machine learning models meeting a user defined constraint. The approach can receive input associated with evaluating machine learning models from a user. The approach can determine acceptable architectural templates to evaluate the machine learning models based on the input and determine a list of architectures and metrics based on a calculation of maximum neural network sizes of the acceptable architectural templates not exceeding the constraint. The approach can send the list of architectures and metrics to the user for selection.

Abstract: A method of automatically creating new intents and assigning examples to those new intents in a dialog system includes generating at least one predicted intent from an intent concept graph by combining two or more nodes of the intent concept graph. A graph embedding is generated of the at least one predicted intent. A sentence embedding is generated from examples from a call log. The graph embedding of the at least one predicted intent is then matched with the examples from the sentence embedding and the at least one predicted intent, with the examples, is connected into a dialog system.

Abstract: A computer system includes a processor configured to execute a conversational agent generator function. The conversational agent generator function is configured to receive a plurality of model input queries. Each model input query includes an intent and a corresponding answer to the intent. A database stores a corpus including sets of textual responses indicative of a common meaning. Each textual response in a given set of textual responses indicates a common meaning constructed in a different style indicative of a corresponding tone of communication. The processor is further configured to execute a rephrasing function configured determine the answer of one of the plurality of model input queries, and convert the answer of one of the plurality of model input queries into a plurality of different rephrased answers from the corpus stored in the database. The processor further outputs the plurality of different rephrased answers to the conversational agent generator function.

Abstract: A product quality analysis and control system is provided. The product quality analysis and control system includes a processing system that is configured to receive data of objective qualities of a product from multiple data gathering modules. Each of the multiple data gathering modules is respectively positioned at corresponding nodes of a supply chain and is configured to receive from sensors data of objective qualities of a product when the product is at each of the corresponding nodes. The processing system includes a model repository and a data evaluation module. The data evaluation module is configured to select, from the model repository, a model associated with a type of the product and to use the model to analyze the data to identify changes in the objective qualities along the supply chain.

Abstract: A method of providing examples to a computerized conversation agent includes associating one or more triplets of two concepts and a relationship therebetween with an intent related to a query. The triplet is embedded in a knowledge graph and the concepts in the knowledge graph are mapped to the intent. A call log is scanned for examples of the intent based on the concepts in the knowledge graph and the examples are mapped to the intent.

Abstract: A method of providing examples to a computerized conversation agent includes associating one or more triplets of two concepts and a relationship therebetween with an intent related to a query. The triplet is embedded in a knowledge graph and the concepts in the knowledge graph are mapped to the intent. A call log is scanned for examples of the intent based on the concepts in the knowledge graph and the examples are mapped to the intent.

Abstract: A product quality analysis and control system is provided. The product quality analysis and control system includes a processing system that is configured to receive data of objective qualities of a product from multiple data gathering modules. Each of the multiple data gathering modules is respectively positioned at corresponding nodes of a supply chain and is configured to receive from sensors data of objective qualities of a product when the product is at each of the corresponding nodes. The processing system includes a model repository and a data evaluation module. The data evaluation module is configured to select, from the model repository, a model associated with a type of the product and to use the model to analyze the data to identify changes in the objective qualities along the supply chain.

Abstract: A computer system includes a processor configured to execute a conversational agent generator function. The conversational agent generator function is configured to receive a plurality of model input queries. Each model input query includes an intent and a corresponding answer to the intent. A database stores a corpus including sets of textual responses indicative of a common meaning. Each textual response in a given set of textual responses indicates a common meaning constructed in a different style indicative of a corresponding tone of communication. The processor is further configured to execute a rephrasing function configured determine the answer of one of the plurality of model input queries, and convert the answer of one of the plurality of model input queries into a plurality of different rephrased answers from the corpus stored in the database. The processor further outputs the plurality of different rephrased answers to the conversational agent generator function.

Abstract: A computer-implemented method includes extracting a plurality of topics from a plurality of unlabeled social media posts, mapping the plurality of topics to a plurality of frequencies, each frequency in the plurality of frequencies indicating how often a corresponding topic in the plurality of topics occurs in the plurality of unlabeled social media posts, and predicting, based in part on the plurality of frequencies, a future social media posting behavior of a specific social media user, wherein the future social media posting behavior includes a specific topic about which the specific social media user is likely to post at a time in the future and a frequency with which the specific topic is likely to occur in posts of the specific social media user that are created at the time in the future.

Abstract: A fracture pattern characterization method, system, and computer program product, include classifying fracture patterns of reservoir layers using a correlation of reservoir information associated with target images and the target images extracted from a database and determining a fracture pattern characterization of a new reservoir layer input based on the classified fracture patterns.

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: An ore content prediction system is provided. The system receives structured geological data that is derived based on spatial geological information that is associated with an input region. The received structured geological data includes a plurality of multidimensional tensors that are derived from spatial geological information of a plurality of sub-regions of the input region. The spatial geological information includes one or more types of data. The system trains a prediction model to produce a prediction output based on an average grade of an ore of a target mineral type at a target region by using the received structured geological data.

Abstract: An embodiment includes a method for use in importing geomechanical data from one or more references into a knowledge base. The method includes selecting at least one chart within at least a given one of the one or more references; extracting at least a subset of the geomechanical data in the selected chart; preparing one or more learnable models at least in part from the geomechanical data; and loading the learnable models into the knowledge base for use at least in part by at least one machine learning classifier.

Abstract: An embodiment includes a method for use in importing geomechanical data from one or more references into a knowledge base. The method includes selecting at least one chart within at least a given one of the one or more references; extracting at least a subset of the geomechanical data in the selected chart; preparing one or more learnable models at least in part from the geomechanical data; and loading the learnable models into the knowledge base for use at least in part by at least one machine learning classifier.

Abstract: A computer-implemented method includes extracting a plurality of topics from a plurality of unlabeled social media posts, mapping the plurality of topics to a plurality of frequencies, each frequency in the plurality of frequencies indicating how often a corresponding topic in the plurality of topics occurs in the plurality of unlabeled social media posts, and predicting, based in part on the plurality of frequencies, a future social media posting behavior of a specific social media user, wherein the future social media posting behavior includes a specific topic about which the specific social media user is likely to post at a time in the future and a frequency with which the specific topic is likely to occur in posts of the specific social media user that are created at the time in the future.