Systems and methods for managing predictions for vehicle repair estimates are provided. A method includes providing one or more images of a damaged vehicle as input to a machine learning model, wherein the machine learning model has been trained with images of other damaged vehicles and corresponding vehicle operations, wherein each of the vehicle operations represents the repair or replacement of a vehicle component; receiving output of the machine learning model responsive to the input, wherein the output comprises a plurality of values each corresponding to one of a plurality of the vehicle operations; determining a confidence metric based on the values; making a comparison between the confidence metric and a confidence threshold value; and selecting the one of the plurality of the vehicle operations corresponding to the highest value as a predicted operation based on the comparison.

Systems, methods and apparatus for data monitoring are disclosed. A system may include a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit, a data storage circuit structured to store specifications and anticipated state information for a plurality of vehicle types, an analysis circuit structured to analyze the plurality of detection values relative to specifications and anticipated state information to determine a vehicle performance parameter, and a response circuit structured to initiate an action in response to the vehicle performance parameter.

Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines. Computer-readable apparatus and computing systems are also disclosed.

A method for acquiring annotated data with the aid of surgical microscopy systems comprises obtaining desired criteria which are intended to be satisfied by desired data to be annotated, and storing the set of desired criteria in a plurality of surgical microscopy systems. In each surgical microscopy system, images are then recorded and current criteria which correspond to the recorded images are determined. The current criteria are compared with the desired criteria. If the desired criteria sufficiently correspond to the current criteria, a confirmation is requested from a user as to whether said user would like to annotate data. If the user provides the confirmation, annotations for images are received from the user and stored together with the images.

A system, method and apparatus for data collection related to a chemical production process are described. A system may include a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from a detection package, the detection package including at least one of a plurality of input sensors coupled to at least one of a plurality of components of the process, a data analysis circuit structured to analyze a subset of the plurality of detection values to determine at least one of a sensor state, a process state, and a component state, wherein the data analysis circuit includes a pattern recognition circuit, and an analysis response circuit structured to perform an action in response to the at least one of the sensor state, the process state, and the component state.

According to one implementation, a quality control (QC) system for annotated content includes a computing platform having a hardware processor and a system memory storing an annotation culling software code. The hardware processor executes the annotation culling software code to receive multiple content sets annotated by an automated content classification engine, and obtain evaluations of the annotations applied by the automated content classification engine to the content sets. The hardware processor further executes the annotation culling software code to identify a sample size of the content sets for automated QC analysis of the annotations applied by the automated content classification engine, and cull the annotations applied by the automated content classification engine based on the evaluations when the number of annotated content sets equals the identified sample size.

This document relates to training of machine learning models. One example method involves providing a machine learning model having one or more mapping layers. The one or more mapping layers can include at least a first mapping layer configured to map components of pretraining examples into first representations in a space. The example method also includes performing a pretraining stage on the one or more mapping layers using the pretraining examples. The pretraining stage can include adding noise to the first representations of the components of the pretraining examples to obtain noise-adjusted first representations. The pretraining stage can also include performing a self-supervised learning process to pretrain the one or more mapping layers using at least the first representations of the training data items and the noise-adjusted first representations of the training data items.

A Sports Detection System including Sports Detection Device having an artificial intelligence (AI) recognition embedded therein and configured to run an Action Detection Model (ADM) that identifies and stores one or more individual sports actions on the Sports Detection Device for later offloading onto a secondary computing device. Methods for training and improving the ADM include tagging time-aligned portions of sensed and video data to be confirmed by profilers where the feedback can be run through a supervised learning algorithm to generate or update an ADM. The process of identifying and tagging identified portions of time-aligned data can be aided by integrating data mining and pattern recognition techniques.

A system providing a photo classification and selection application for receiving a set of images in a high-resolution format to identify which, if any, of the images possesses a set of composition and quality characteristics to match a desired brand, theme, or similar use. The images are assigned an initial classification type based upon automated processing of the set of images. The initial classifications for each image are validated once automatically determined, via a user interface or Lookbook. Validated images then may be automatically used to define a Lookbook of images for use by stakeholders with a high degree of confidence that the images included in the Lookbook possess the desired characteristics.

Disclosed are methods, devices, and computer-readable media for operating paired or grouped drone devices. In one embodiment, a method is disclosed comprising capturing a first image by a camera installed on a first drone device; processing the first image using an artificial intelligence (AI) engine embedded in the first drone device, the processing comprising generating a first inference output; transmitting the first inference output to a second drone device; receiving a second inference output from the second drone device, the second inference output associated with a second image captured by the second drone device; and transmitting the first image to a processor based on the first inference output and second interference output.