Abstract: Example apparatus and methods for program synthesis using genetic algorithms are disclosed herein. An example apparatus includes a program length predictor to predict a length of a first program by executing a first neural network model, a program generator to generate a candidate program having a length corresponding to the predicted length, a candidate program analyzer to generate a fitness score for the candidate program by executing a second neural network model and to identify the first candidate program for use in a breeding operation relative a second candidate program based on the fitness score, and a genetic program generator to perform the breeding operation with at least one of the first candidate program or the second candidate program to generate an evolved candidate program.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Abstract: Methods, apparatus, systems and articles of manufacture to optimize execution of a machine learning model are disclosed. An example apparatus includes a quantizer to quantize a layer of a model based on an execution constraint, the layer of the model represented by a matrix. A packer is to pack the quantized layer of the matrix to create a packed layer represented by a packed matrix, the packed matrix having non-zero values of the matrix grouped together along at least one of a row or a column of the matrix. A blocker is to block the packed layer into a blocked layer by dividing the non-zero values in the packed matrix into blocks. A fuser is to fuse the blocked layer into a pipeline. A packager is to package the pipeline into a binary.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for code review assistance for dynamically typed languages. An example apparatus to analyze a segment of code includes a function identifier to identify a first input of a first function call included in the segment of the code, a parameter type vector (PTV) estimatior model to estimate a first data structure based on the first input, the PTV estimatior model generated via a set of reviewed code, a PTV determiner to generate a second data structure based on a data parameter type of the first input, an error comparator to determine a first reconstruction error based on the first data structure, and the second data structure and a recommendation generator to, if the first reconstruction error does not satisfy a recommendation threshold, generate a first recommendation to review the first function call.

Abstract: Apparatus, systems, articles of manufacture, and methods for a context and complexity-aware recommendation system for efficient software development.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for object manipulation via action sequence optimization. An example method disclosed herein includes determining an initial state of a scene, generating a first action phase sequence to transform the initial state of the scene to a solution state of the scene by selecting a plurality of action phases based on action phase probabilities, determining whether a first simulated outcome of executing the first action phase sequence satisfies an acceptability criterion and, when the first simulated outcome does not satisfy the acceptability criterion, calculating a first cost function output based on a difference between the first simulated outcome and the solution state of the scene, the first cost function output utilized to generate updated action phase probabilities.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed improve runtime performance of software executing on a heterogeneous system. An example apparatus includes a feedback interface to collect a performance characteristic of the heterogeneous system associated with a compiled version of a block of code at a first runtime, the compiled version executed according to a function designating successful execution of the compiled version on the heterogeneous system, the heterogeneous system including a first processing element and a second processing element different than the first processing element; a performance analyzer to determine a performance delta based on the performance characteristic and the function; and a machine learning modeler to, prior to a second runtime, adjust a cost model of the first processing element based on the performance delta, the adjusted cost model to cause a reduction in the performance delta to improve runtime performance of the heterogeneous system.

Abstract: Methods, apparatus, systems and articles of manufacture to recommend instruction adaptations to improve compute performance are disclosed. An example apparatus includes a pattern detector to detect an execution pattern from an execution profile provided by a server, the execution profile associated with an instruction stored in an instruction repository. An adaptation identifier is to identify a possible instruction adaptation that may be applied to the instruction associated with the execution pattern. A model processor is to predict, using a machine learning model, an expected performance improvement of the adaptation. A result comparator is to determine whether the expected performance improvement meets an threshold. An instruction editor is to, in response to the result comparator determining that the expected performance improvement meets the threshold, apply the possible instruction adaptation to the instruction in the instruction repository.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed that analyze computer system attack mechanisms. An example apparatus includes a graph generator utilizing a natural language processing model to generate a graph based on a publication, an analyzer to: analyze two or more nodes in the graph by identifying respective attributes of the two or more nodes in the graph, and provide an indication of the two or more nodes that include similar respective attributes, a variation generator to generate an attack mechanism based on the indication, and a weight postulator to obtain the generated attack mechanism and, based on (A) the two or more nodes in the graph and (B) the generated attack mechanism, indicate a weight associated with a severity of the generated attack mechanism.

Abstract: Methods and apparatus to automatically generate code for graphical user interfaces are disclosed. An example apparatus includes a textual description analyzer to encode a user-provided textual description of a GUI design using a first neural network. The example apparatus further includes a DSL statement generator to generate a DSL statement with a second neural network. The DSL statement is to define a visual element of the GUI design. The DSL statement is generated based on at least one of the encoded textual description or a user-provided image representative of the GUI design. The example apparatus further includes a rendering tool to render a mockup of the GUI design based on the DSL statement.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to improve code characteristics. An example apparatus includes a weight manager to apply a first weight value to a first objective function, a state identifier to identify a first state corresponding to candidate code, and an action identifier to identify candidate actions corresponding to the identified first state.

Abstract: Apparatus, devices, systems, methods, and articles of manufacture for intent-based machine programming are disclosed. An example system categorize source code blocks includes a code repository accessor to access a code repository and select a source code block. The example system also includes a signature generator to generate a signature for the source code block, a collateral miner to extract collateral associated with the source code block, and a tokenizer to transform the source code block into tokens. In addition, the example system includes a function assessor to determine a function of the source code block based on the collateral and the tokens and an input/output determiner to determine an input and an output of the source code block based on the collateral and the signature. The example system further includes a tagger to categorize the source code block with the function, input, and output.

Abstract: An example includes a sequence generator to generate a plurality of sequence pairs, a first one of the sequence pairs including: (i) a first input sequence representing first accesses to first tensors in a first loop nest of a first computer program, and (ii) a first output sequence representing a first tuned loop nest corresponding to the first accesses to the first tensors in the first loop nest; a model trainer to train a recurrent neural network based on the sequence pairs as training data, the recurrent neural network to be trained to tune loop ordering of a second computer program based on a second input sequence representing second accesses to a second tensor in a second loop nest of the second computer program; and a memory interface to store, in memory, a trained model corresponding to the recurrent neural network.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed that optimize workflows. An example apparatus includes an intent determiner to determine an objective of a user input, the objective indicating a task to be executed in an infrastructure, a configuration composer to compose a plurality of workflows based on the determined objective, a model executor to execute a machine learning model to create a confidence score relating to the plurality of workflows, and a workflow selector to select at least one of the plurality of workflows for execution in the infrastructure, the selection of the at least one of the plurality of workflows based on the confidence score.

Abstract: Apparatus, systems, methods, and articles of manufacture for fingerprinting and classifying application behaviors using telemetry are disclosed. An example apparatus includes a trace processor to process events in a processor trace to capture application execution behavior; a fingerprint extractor to extract a first fingerprint from the captured application execution behavior and performance monitor information; a fingerprint clusterer to, in a training mode cluster the first fingerprint and the second fingerprint into a cluster of fingerprints to be stored in a fingerprint database with a classification; and a fingerprint classifier to, in a deployed mode, classify a third fingerprint, the fingerprint classifier to trigger a remedial action when the classification is malicious.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed improve utilization of a heterogeneous system executing software. The disclosed methods, apparatus, systems and articles of manufacture include an apparatus comprising a variant manager to determine whether an algorithm is a candidate for sub-algorithmic partitioning (SAP) based on at least one of a first size of input data to the algorithm and a second size of output data from the algorithm; a partitioner to partition the algorithm into at least a first tile and a second tile; and a compiler to compile a first variant based on the first tile and a second variant based on the second tile into an executable file, the first variant to be executed on a first processing element of the heterogeneous system, the second variant to be executed on a second processing element of the heterogeneous system.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine programmed creative support to a user are disclosed. An example apparatus include an artificial intelligence architecture to be trained based on previous inputs of the user; a processor to: implement a first machine learning model based on the trained artificial intelligence architecture; and predict a first action based on a current state of a computer program using the first machine learning model; implement a second machine learning model based on the trained artificial intelligence architecture; and predict a second action based on the current state of the computer program using the second machine learning model; and a controller to select a state based on the action that results in a state that is more divergent from the current state of the computer program.

Abstract: Apparatus, systems, articles of manufacture, and methods to generate acceptability criteria for autonomous systems plans are disclosed. An example apparatus includes a data compiler to compile data generated by the autonomous system into an autonomous system task dataset, a data encoder to encode the dataset for input into a rule distillation neural network architecture, a model trainer to train the rule distillation neural network architecture, an adaptor to adapt the trained rule distillation neural network architecture to a new input data domain using the autonomous system task dataset, a verifier to generate formally verified acceptability criteria, and an inferer to evaluate a control command, the evaluation resulting in an acceptance or rejection of the command.

Abstract: Apparatus, systems, methods, and articles of manufacture for automated quality assurance and software improvement are disclosed. An example apparatus includes a data processor to process data corresponding to events occurring with respect to a software application in i) a development and/or a testing environment and ii) a production environment. The example apparatus includes a model tool to: generate a first model of expected software usage based on the data corresponding to events occurring in the development and/or the testing environment; and generate a second model of actual software usage based on the data corresponding to events occurring in the production environment. The example apparatus includes a model comparator to compare the first model to the second model. The example apparatus includes a correction generator to generate an actionable recommendation to adjust the development and/or the testing environment to reduce a difference between the first model and the second model.

Abstract: Apparatus, systems, methods, and articles of manufacture for automatic robot perception programming by imitation learning are disclosed. An example apparatus includes a percept mapper to identify a first percept and a second percept from data gathered from a demonstration of a task and an entropy encoder to calculate a first saliency of the first percept and a second saliency of the second percept. The example apparatus also includes a trajectory mapper to map a trajectory based on the first percept and the second percept, the first percept skewed based on the first saliency, the second percept skewed based on the second saliency. In addition, the example apparatus includes a probabilistic encoder to determine a plurality of variations of the trajectory and create a collection of trajectories including the trajectory and the variations of the trajectory.