The technology disclosed relates to artificial intelligence based determination of analyte data for base calling. In particular, the technology disclosed uses input image data that is derived from a sequence of images. Each image in the sequence of images represents an imaged region and depicts intensity emissions indicative of one or more analytes and a surrounding background of the intensity emissions at a respective one of a plurality of sequencing cycles of a sequencing run. The input image data comprises image patches extracted from each image in the sequence of images. The input image data is processed through a neural network to generate an alternative representation of the input image data. The alternative representation is processed through an output layer to generate an output indicating properties of respective portions of the imaged region.

A learning image generation device includes an image acquisition unit that acquires a learning image, and a variation learning image generation unit that generates a variation learning image by adding a variation in which an output of a model deviates from a target value to a pixel value of at least one pixel that constitutes the learning image in a case in which the learning image acquired by the image acquisition unit is input to the model.

Identifying key frames of a video for use in training a machine learning model is provided. Object detection is performed to identify frames of a video including target classes of objects of interest. Feature extraction is performed on the identified frames to generate raw feature vectors. The feature vectors are compressed into lower dimension vectors. The compressed feature vectors are compressed into a plurality of clusters. The clustered compressed feature vectors are filtered to identify the key frames from each of the plurality of clusters. The key frames may be provided as a representative data set of the video.

Systems and methods for predicting user interaction with vehicles. A computing device receives an image and a video segment of a road scene, the first at least one of an image and a video segment being taken from a perspective of a participant in the road scene and then generates stimulus data based on the image and the video segment. Stimulus data is transmitted to a user interface and response data is received, which includes at least one of an action and a likelihood of the action corresponding to another participant in the road scene. The computing device aggregates a subset of the plurality of response data to form statistical data and a model is created based on the statistical data. The model is applied to another image or video segment and a prediction of user behavior in the another image or video segment is generated.

Methods and systems for a monitoring system for data collection in an industrial environment including a data collector communicatively coupled to a plurality of input channels connected to data collection points related to machine components, wherein at least one of the plurality of input channels is connected to a data collection point on a rotating machine component; a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each of the plurality of detection values corresponding to at least one of the plurality of input channels; and an expert system analysis circuit structured to analyze the collected data, wherein the expert system analysis circuit determines a failure state for the rotating machine component based on analysis of the plurality of detection values, wherein upon determining the failure state the expert system analysis circuit provides the failure state to a data storage.

Example methods and systems for quality-aware continuous learning for radiotherapy treatment planning are provided. One example method may comprise: obtaining an artificial intelligence (AI) engine that is trained to perform a radiotherapy treatment planning task. The method may also comprise: based on input data associated with a patient, performing the radiotherapy treatment planning task using the AI engine to generate output data associated with the patient; and obtaining modified output data that includes one or more modifications made by a treatment planner to the output data. The method may further comprise: performing quality evaluation based on (a) first quality indicator data associated with the modified output data, and/or (b) second quality indicator data associated with the treatment planner. In response to a decision to accept, a modified AI engine may be generated by re-training the AI engine based on the modified output data.

A computer implemented service for identifying and classifying damage. The algorithm may be implemented on a device, such as a computer or mobile device, or on a remote server. The remote server may be a website or cloud-based platform. A user may access the service by sending a request to the remote server including an image, video, or live feed containing an item to be inspected. The service may identify and classify any damage found on the item. The output of the service may include the location of the damaged item, a determination of the presence of damage, a certainty level of this determination, and a heatmap indicating the areas of the image that are most likely to contain damage. The output of the service may be stored on a remote server or may be integrated into existing damage reporting systems.

A system and method for generating a training data set for training a machine learning model to detect defects in specimens is described herein. A computing system cause presentation of an image on a device of a user. The image includes at least one defect on an example specimen. The computing system receives an annotated image from the user. The user annotated the image using an input via the device. The input includes a first indication of a location of the defect and a second indication of a class corresponding to the defect. The computing system adjusts the annotated image to standardize the input based on an error profile of the user and the class corresponding to the defect. The computing system uploads the annotated image for training the machine learning model.

System/method identifying a defined object (e.g., hazard): a sensor detecting and defining a digital representation of an object; a processor (connected to the sensor) which executes two techniques to identify a signature of the defined object; a memory (connected to the processor) storing reference data relating to two signatures derived, respectively, by the two techniques; responsive to the processor receiving the digital representation from the sensor, the processor executes the two techniques, each technique assessing the digital representation to identify any signature candidate defined by the object, derive feature data from each identified signature candidate, compare the feature data to the reference data, and derive a likelihood value of the signature candidate corresponding with the respective signature; combining likelihood values to derive a composite likelihood value and thus determine whether the object in the digital representation is the defined object.

Systems and techniques are described for image processing. An imaging system receives an identity image and an attribute image. The identity image depicts a first person having an identity. The attribute image depicts a second person having an attribute, such as a facial feature, an accessory worn by the second person, and/or an expression. The imaging system uses trained machine learning model(s) to generate a combined image based on the identity image and the attribute image. The combined image depicts a virtual person having both the identity of the first person and the attribute of the second person. The imaging system outputs the combined image, for instance by displaying the combined image or sending the combined image to a receiving device. In some examples, the imaging system updates the trained machine learning model(s) based on the combined image.