A robotic post system includes one or more composable robotic posts each having a processor and a memory. At least one composable post includes a set of modules including one or more pairable latches which is configured to couple and lock with another pairable latch from among another set of modules included on another of the composable posts, such that the composable robotic posts form a composable surface supported by the composable robotic posts.

An information processing apparatus includes a hardware processor which (i) performs learning by a learning data set associated with a correct answer label for a preset problem and creates a machine learning model for estimating a correct answer to the preset problem for input data, (ii) estimates the correct answer to the preset problem for the input data by using the machine learning model, (iii) in response to a user operation, determines a label indicating a result of the estimation as a correct answer label of the input data or corrects the label to determine the corrected label as a correct answer label of the input data, and (iv) additionally registers the determined correct answer label as learning data in association with the input data in the learning data set.

A method for providing a user interface (UI) for generating training data for an artificial intelligence (AI) model may include providing, for display via the UI, image information that depicts an object, a set of operations of the object, and a process associated with the set of operations. The method may include providing, for display via the UI, text information that describes the object, the set of operations of the object, and the process associated with the set of operations. The method may include receiving, via the UI, a user input that associates respective image information of the image information with corresponding text information of the text information. The method may include generating association information that associates the respective image information with the corresponding text information, based on the user input. The method may include generating discourse and semantic information from the text information associated to the image information.

A method of data preparation for artificial intelligence models includes receiving data characterizing a first plurality of images. The method further includes annotating a first subset of images of the first plurality of images based at least in part on a first user input to generate annotated first subset of images. The annotating includes labelling one or more features of the first subset of images. The method also includes generating, by a training code, an annotation code, the training code configured to receive the annotated first subset of images as input and output the annotation code. The training and the annotation code includes computer executable instructions. The method also includes annotating, by the annotation code, a second subset of images of the first plurality of images to generate annotated second subset of images, wherein the annotating includes labelling one or more features of the second subset of images.

A semi-automatic three-dimensional light detection and ranging (LIDAR) point cloud data annotation system and method for autonomous driving of a vehicle involve filtering 3D LIDAR point cloud and normalizing the filtered 3D LIDAR point cloud data relative to the vehicle to obtain normalized 3D LIDAR point cloud data, quantizing the normalized 3D LIDAR point cloud data by dividing it into a set of 3D voxels, projecting the set of 3D voxels to a 2D birdview, identifying a possible object by applying clustering to the 2D birdview projection, obtaining an annotated 2D birdview projection including annotations by a human annotator via the annotation system regarding whether the bounding box corresponds to a confirmed object and a type of the confirmed object, and converting the annotated 2D birdview projection to back into annotated 3D LIDAR point cloud data.

Disclosed herein include systems and methods for biological object tracking and lineage construction. Also disclosed herein include cloud-based systems and methods for allocating computational resources for deep learning-enabled image analysis of biological objects. Also disclosed herein include systems and methods for annotating and curating biological object tracking-specific training datasets.

Processes and systems for displaying information received in a query response. One system includes a communication device including a user interface and an electronic processor. The electronic processor transmits a query, and receives a response to the query including stored information in each of a plurality of fields of information about an object of interest. The electronic processor receives an image of the object from a camera, identifies visually descriptive text information describing the object, and categorizes at least a portion of the visually descriptive text information into the plurality of fields of information. The electronic processor determines a confidence level for each field of information based on a comparison of the stored information and the visually descriptive text information in each field, and displays the stored information in one or more fields for which the confidence level of the comparison is below a predetermined threshold.

A method and an electronic device are provided for editing video using objects of interest included in the video. An electronic device includes a camera, a memory, and a processor. The processor may obtain first video data through the camera, while obtaining the first video data, determine a first object of interest in a plurality of image frames included in the first video data, store, in the memory, second video data generated from the first video data based on the first object of interest, after generating the second video data, receive a user input for determining a second object of interest, different from the first object of interest, in the plurality of the image frames included in the first video data, and in response to receiving the user input, store, in the memory, third video data generated from the first video data based on at least the second object of interest.

In some implementations, a method includes: determining first usage patterns associated with a physical object within the physical environment; obtaining a first objective for an objective-effectuator (OE) instantiated in a computer-generated reality (CGR) environment, wherein the first objective is associated with a representation of the physical object; obtaining a first directive for the OE that limits actions for performance by the OE to achieve the first objective to the first usage patterns associated with the physical object; generating first actions, for performance by the OE, in order to achieve the first objective as limited by the first directive, wherein the first set of actions corresponds to a first subset of usage patterns from the first set of usage patterns associated with the physical object; and presenting the OE performing the first actions on the representation of the physical object overlaid on the physical environment.

An information processing device includes: an acquirer that acquires observation results obtained from a time-lapse image of a target object; and a controller that indicates, in a display image, analysis results visually representing the observation results and an increase/decrease time indicating an amount of time required to increase or decrease a predetermined value related to the target object. When a setting for a section in the analysis results has been received, the controller indicates, in the display image, the increase/decrease time corresponding to the section that has been set.