Abstract: A corpus of source code from a code database is accessed and a language prediction model is trained based on the corpus of source code. A given program is accessed and a completion of a given line of the given program is predicted by performing inferencing using the language prediction model and at least a portion of the given program. The given line is completed based upon the prediction.

Abstract: A method, computer system, and a computer program product are provided. Training data in tabular form having at least some columns is received. One or more sketches for contents of the respective columns are created. The sketches are combined with metadata embeddings of the training data to form respective combined input vectors. A transformer architecture machine learning model is trained by computing loss based on an objective function, by inputting the combined input vectors into the transformer architecture machine learning model, and, in response, the transformer architecture machine learning model producing an output.

Abstract: Techniques for computer software code analysis are disclosed. One or more data flows are generated, based on analyzing software code using static analysis. A data object is identified in the software code using the one or more data flows, the data object relating to a structured dataset. A correspondence between a code expression in the software code and a characteristic of the structured dataset is identified, based on analyzing one or more reads from and one or more writes to the data object using the one or more data flows. The code expression for the structured dataset is analyzed, based on the correspondence, including at least one of: (i) generating a software code recommendation engine based on the code expression and the structured dataset, or (ii) generating one or more lambda expressions for application to the structured dataset, based on the code expression.

Abstract: Automated improved computer mechanisms are provided for improving the way in which a lifted successor generation (LSG) solution to an artificial intelligence (AI) planning problem is processed. An artificial intelligence (AI) planning problem is received that includes definitions for a plurality of operators. An initial label set, which defines an initial version of an action space, is created, with each label corresponding to an operator. A label reduction is performed on the label set to obtain a reduced label set (seed set) that defines a reduced action space. The AI planning problem is represented as a LSG problem comprising a set of tables and a join query. A LSG module is executed on the LSG problem using the seed set to process the join query and generate applicable action(s) as a solution to the AI planning problem which are then output for further AI operations.

Abstract: A computer-implemented method, system and computer program product for refactoring code using a machine learning model. Parallel corpora is generated using a single directional code transform. A single directional code transform refers to a transformation performed by a refactoring tool which refactors computer code (“code”) to restructure the code to include styles, which are often undesirable, such as “for” loops. Parallel corpora refers to a collection of code of a first style of code (e.g., dictionary comprehensions) in the code prior to refactoring (non-refactored code) and a second style of code (e.g., “for” loops) in the refactored code. A machine learning model is then trained to perform code refactoring in a reverse direction of the single directional code transform using the parallel corpora. New computer code is then refactored using the trained machine learning model, where the refactored code now includes a desired style of code (e.g., dictionary comprehensions).

Abstract: A method of data augmentation includes receiving, by a processor, a set of data including a plurality of variables, mapping each variable to one or more target concepts associated with a name of each variable, and acquiring a set of semantic transforms, each semantic transform including a function applied to one or more concepts mapped to a respective variable. The method also includes comparing the one or more target concepts to the one or more concepts of each semantic transform, selecting at least one semantic transform based on the comparing, generating an expression for each selected semantic transform, each expression configured to apply a function of a selected semantic transform to at least one of the plurality of variables, and augmenting the set of data for use in an application by adding each expression to the set of data.

Abstract: A corpus of source code from a code database is accessed and a language prediction model is trained based on the corpus of source code. A given program is accessed and a completion of a given line of the given program is predicted by performing inferencing using the language prediction model and at least a portion of the given program. The given line is completed based upon the prediction.

Abstract: A processor may receive structured data. The structured data may include one or more columns and associated column names. The processor may analyze the structured data. Analyzing the structured data may include gathering a requisite set of keywords from the associated column names across all columns and/or a sample of column cells. The processor may access a corpus of documents. Each of the documents in the corpus may be associated with a respective keyword. The processor may search the corpus of documents based on the requisite set of keywords. The processor may summarize one or more documents associated with the requisite set of keywords.

Abstract: A computer-implemented method is provided that includes accessing candidate text and a candidate pair including first and second phrases, substituting the first and second phrases into cause-effect patterns to generate variant sentences. An artificial intelligence model is leveraged to determine respective probabilities that the variant sentences are inferred from the candidate text, calculate a statistical measure of the respective probabilities, and assess the calculated statistical measure to ascertain whether the first and second phrases possess a causal relationship or non-causal relationship to one another. A knowledge base including one or more pairs of cause-effect phrase pairs is populated with the first and second phrases possessing the causal relationship. A computer system and a computer program product are also provided.

Abstract: A processor may receive structured data. The structured data may include one or more columns and associated column names. The processor may analyze the structured data. Analyzing the structured data may include gathering a requisite set of keywords from the associated column names across all columns and/or a sample of column cells. The processor may access a corpus of documents. Each of the documents in the corpus may be associated with a respective keyword. The processor may search the corpus of documents based on the requisite set of keywords. The processor may summarize one or more documents associated with the requisite set of keywords.

Abstract: Technology for: (i) receiving a domain-dependent artificial intelligence planning problem including definitions for a plurality of operators; (ii) creating an initial version of a label set, which defines an initial version of an action space, with the label set including a plurality of labels, and with each label of the plurality of labels respectively corresponding to the operators of the plurality of operators; (iii) performing, automatically and by machine logic, a label reduction on the initial version of the label set to obtain a reduced version of the label set that defines a reduced action space; and (iv) recasting the artificial planning problem as a first Markov decision process using the reduced version of label set.

Abstract: To improve the technological process of programming a computer using a dynamic programming language, generate a first portion of training data which maps types in the dynamic programming language to corresponding functions and methods by performing information retrieval on documentation libraries in the dynamic programming language and/or generate a second portion of training data which maps program variables to the corresponding functions and methods by performing data flow analysis on a plurality of pre-existing programs written in the dynamic programming language. Train a neural network on the first and/or second portions of training data to infer unknown types in the dynamic programming language. Carry out inference with the trained neural network to infer the unknown types. Facilitate programming in the dynamic programming language based on the inferred unknown types. Optionally, execute a resulting program.

Abstract: Generate, from a logical formula, a directed acyclic graph having a plurality of nodes and a plurality of edges. Assign an initial embedding to each mode and edge, to one of a plurality of layers. Compute a plurality of initial node states by using feed-forward networks, and construct cross-dependent embeddings between conjecture node embeddings and premise node embeddings. Topologically sort the DAG with the initial embeddings and node states. Beginning from a lowest rank, compute layer-by-layer embedding updates for each of the plurality of layers until a root is reached. Assign the embedding update for the root node as a final embedding for the DAG. Provide the final embedding for the DAG as input to a machine learning system, and carry out the automatic theorem proving with same.

Abstract: Embodiments are disclosed for a method. The method includes identifying a prefix updated by a searcher of a machine learning model. The machine learning model is configured to generate source code in a programming language. The method also includes determining whether the prefix violates a semantic correctness property of the programming language. Additionally, the method includes instructing the searcher, in response to the determination, to prune the prefix from a set of prefixes under consideration by the searcher.

Abstract: A computer-implemented method according to one embodiment includes identifying a data set and meta information; and augmenting the data set with additional features in response to an automatic analysis of the data set in view of the meta information.

Abstract: A method, system, and computer program product are disclosed. The method includes extracting at least one identifier from a formula in a document and extracting text passages in the document that contain the identifier(s). The method also includes selecting an identifier and extracted text passages containing the identifier, as well as generating identifier-passage pairs for the selected text passages and the identifier. Further, the method includes submitting the identifier-passage pairs to a question answering (QA) model, which generates candidate answers from the selected text passages. A definition of the identifier is then selected from the candidate answers.

Abstract: A method, system, and computer program product are disclosed. The method includes extracting at least one identifier from a formula in a document and extracting text passages in the document that contain the identifier(s). The method also includes selecting an identifier and extracted text passages containing the identifier, as well as generating identifier-passage pairs for the selected text passages and the identifier. Further, the method includes submitting the identifier-passage pairs to a question answering (QA) model, which generates candidate answers from the selected text passages. A definition of the identifier is then selected from the candidate answers.

Abstract: Embodiments relate to a system, program product, and method for employing feature engineering to improve classifier performance. A first machine learning (ML) model with a first learning program is selected. The first selected ML model is operatively associated with a first structured dataset. First features in the first dataset directed at performance of the selected ML model are identified. A second structured dataset is assessed with respect to the identified features in the first dataset, and new features in the second dataset are identified, where the new features are semantically related to the identified features in the first dataset. The first dataset is dynamically augmented with the identified new features in the second dataset. The dynamically augmented first dataset is applied to the selected ML model to subject an embedded learning algorithm of the selected ML model to training using the augmented first dataset.

Abstract: A schema for a dataset is identified by identifying a dataset comprising data and relationships between data pairs. An original schema is identified for the dataset. This original schema comprises an organizational structure. An initial fit between the dataset and the original schema is determined. The initial fit quantifying a conformity of the data in the dataset to the organizational structure of the original schema. A plurality of additional schemas are identified. Each additional schema is a distinct organizational schema. The dataset is partitioned into a plurality of subsets. Each subset comprises a modified fit quantifying a modified conformity of subset data in each subset to one of the original schema and the additional schemas. The modified fit is greater than the original fit.

Abstract: Techniques for code analysis are provided. User code is received, and an import statement is identified in the user code. A first empty object is generated based on the import statement, and the first empty object is named based on the name of an import reference included in the import statement. A knowledge graph is generated based at least in part on the first empty object.