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: Methods, apparatus, systems, and articles of manufacture are disclosed herein that mitigate hard-braking events. An example apparatus at least one memory; instructions; and processor circuitry to execute the instructions to: determine a danger level associated with an object, the danger level indicative of a first measure of damage corresponding to a trajectory of the object compared to a trajectory of a vehicle; determine, based on the first danger level, a danger measure based on at least one of a position of the object, a velocity of the object, an acceleration of the object, a direction of travel of the object, a weight or mass of the object; and generate instructions to transmit to a steering system or a braking system of the vehicle based on the determination.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for intentional programming for heterogeneous systems. An example apparatus includes a code lifter to identify annotated code corresponding to an algorithm to be executed on the heterogeneous system based on an identifier being associated with the annotated code, and convert the annotated code in the first representation to intermediate code in a second representation by identifying the intermediate code as having a first algorithmic intent that corresponds to a second algorithmic intent of the annotated code, a domain specific language (DSL) generator to translate the intermediate code in the second representation to DSL code in a third representation when the first algorithmic intent matches the second algorithmic intent, the third representation corresponding to a DSL representation, and a code replacer to invoke a compiler to generate an executable including variant binaries based on the DSL code.

Abstract: Methods, apparatus, systems, and articles of manufacture to improve data quality for artificial intelligence are disclosed. An example apparatus includes an interface; instructions; and processor circuitry to execute the instruction to: determine an indirect quality of a repository that include datapoints of a dataset; determine a direct quality of the repository that include the datapoints of the dataset; determine a dataset quality based on the indirect quality of the repository and the direct quality of the repository; and when the quality does not satisfy a threshold, filter out a subset of the datapoints to prepare the dataset to support the training of the neural network.

Abstract: Methods, apparatus, systems and articles of manufacture for providing query selection systems are disclosed. An example query selection system includes a processor to: analyze a graph database; identify respective ones of objects associated with the graph database; obtain properties associated with the objects; identify common properties present in the respective ones of the objects; in response to determining the common properties present in the identified objects, output a list of the common properties corresponding to the respective ones of the objects; generate a table for the common properties and the respective ones of the objects; and populate the table with the common properties and the respective ones of the objects from the graph database to establish a relational database. The system further includes a machine learning model classifier to enable relational database query searching via the relational database.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to generate command lists to be offloaded to accelerator circuitry. An example apparatus includes kernel duration model circuitry to predict a duration of execution of a first kernel based on a first source location, a first name, a first property of a first argument, or an occupancy of the first kernel. The example apparatus includes subsequent kernel model circuitry to predict a tuple and a dependency of a second kernel based on a second source location, a second name, a second property of a second argument, or a time of submission of the previous kernel. The example apparatus includes reinforcement learning model circuitry to determine whether to bundle the first kernel into a command list based on the duration of execution of the first kernel, the tuple of the second kernel, or the dependency of the second kernel.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that implement an automatically evolving code recommendation engine. In one example, the apparatus collects a user code snippet. The apparatus then determines a structured representation of the user code snippet. Next, the apparatus generates a recommended code snippet using the structured representation of the user code snippet. Then the apparatus obtains user-determined code snippet feedback comparing the user code snippet to the recommended code snippet, the user-determined code snippet feedback indicating one of a match, no match, or uncertain. Finally, the apparatus stores a code snippet training pair in a training database, the code snippet training pair including the user code snippet and the recommended code snippet.

Abstract: Methods, apparatus, systems and articles of manufacture to identify a side-channel attack are disclosed. Example instructions cause one or more processors to generate an event vector based on one or more counts corresponding to tasks performed by a central processing unit; determine distances between the event vector and weight vectors of neurons in a self-organizing map; select a neuron of the neurons that results based on a determined distance; identify neurons that neighbor the selected neuron; and update at least one of a weight vector of the selected neuron or weight vectors of the neighboring neurons based on the determined distance of the selected neuron.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that perform machine learning-guided compiler optimizations for register-based hardware architectures. Examples disclosed herein include a non-transitory computer readable medium comprising instructions that, when executed, cause a machine to at least select a register-based compiler transformation to apply to source code at a current position in a search tree, determine whether the search tree is in need of pruning based on an output of a query to a machine learning (ML) model, in response to determining the search tree is in need of pruning, prune the search tree at the current position, in response to applying the selected register-based compiler transformation to the source code, generate a code variant, calculate a score associated with the source code at the current position in the search tree, and update parameters of the machine learning (ML) model to include the calculated score.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for intentional programming for heterogeneous systems. An example non-transitory computer readable storage medium includes instructions that, when executed, cause processor circuitry to at least identify a first code block having a first algorithmic purpose based on a second code block having a second algorithmic purpose, the second algorithmic purpose corresponding to the first algorithmic purpose, translate the first code block into executable domain specific language code, and output the executable domain specific language code.

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: Methods, apparatus, systems, and articles of manufacture are disclosed to detect attacks in V2X networks. An example apparatus includes a challenge handler to (a) transmit a first challenge packet to a first vehicle to request a transmission of a first response, (b) instruct a second challenge packet to be transmitted to a second vehicle to request a transmission of a second response, (c) increment a first counter when the first response is not obtained, (d) increment a second counter when the second response is not obtained, and (e) after repeating (a)-(d), determine that the first and second vehicles are phantom vehicles associated with an attacker with a half-duplex radio when at least one of the first or second counters satisfy a threshold, and a network interface to instruct a third vehicle associated with the V2X network to ignore future messages from the phantom vehicles based on the determination.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to validate data communicated by a vehicle. An example apparatus an anomaly detector to, in response to data communicated by a vehicle, at least one of compare an estimated speed with a reported speed or compare a location of the vehicle with a reported location. The apparatus including the anomaly detector further to generate an indication of the vehicle in response to the comparison. The apparatus further includes a notifier to discard data sent by the vehicle and notify surrounding vehicles of the data communicated by the vehicle.

Abstract: Methods and apparatus to generate vehicle warnings are disclosed. An example apparatus includes a sensor to detect a vehicle, where the sensor is associated with an observer of the vehicle, an object tracker to determine a motion of the vehicle, an accident estimator to calculate a likelihood of a collision of the vehicle based on the determined motion, and a transceiver to transmit a message to the vehicle upon the likelihood of the collision exceeding a threshold, where the message includes information pertaining to the collision.

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: Methods, apparatus, systems, and articles of manufacture are disclosed to determine refined context for bug detection. At least one non-transitory machine-readable medium includes instructions that, when executed, cause at least one processor to at least classify a node on a graph, the graph to represent a computer program, the node to contain partial bug context corresponding to the computer program; identify a location of a software bug in the computer program, the location based on the node; determine a static bug context of the software bug using the location of the software bug; determine a dynamic bug context of the software bug using the location of the software bug; and determine a refined bug context based on a merge of the static bug context and the dynamic bug context.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to validate data communicated by a vehicle. An example apparatus an anomaly detector to, in response to data communicated by a vehicle, at least one of compare an estimated speed with a reported speed or compare a location of the vehicle with a reported location. The apparatus including the anomaly detector further to generate an indication of the vehicle in response to the comparison. The apparatus further includes a notifier to discard data sent by the vehicle and notify surrounding vehicles of the data communicated by the vehicle.

Abstract: An example apparatus comprises a transformation generator to generate a population of code variants corresponding to an input code, the population of code variants to include transformation sequences of the input code, a dependence analyzer to analyze the population of code variants for dependence vectors, a profile controller to profile the population of code variants to determine performance metrics of hardware during an execution of respective ones of the transformation sequences, and a hash code generator to generate hash codes for storing in a database, the hash codes (a) corresponding to a combination of the dependence vectors and respective performance metrics and (b) mapped to respective transformation sequences.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed herein that mitigate hard-braking events. An example apparatus at least one memory; instructions; and processor circuitry to execute the instructions to: determine a danger level associated with an object, the danger level indicative of a first measure of damage corresponding to a trajectory of the object compared to a trajectory of a vehicle; determine, based on the first danger level, a danger measure based on at least one of a position of the object, a velocity of the object, an acceleration of the object, a direction of travel of the object, a weight or mass of the object; and generate instructions to transmit to a steering system or a braking system of the vehicle based on the determination.

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.