International Business Machines Corporation

Dario Augusto Borges Oliveira

Priscilla Barreira Avegliano

Andrea Britto Mattos Lima

Carlos Henrique Cardonha

Campbell D Watson

Marisa Affonso Vasconcelos

Bianca Zadrozny

Jorge Luis Guevara Diaz

A processor may receive weather event data. The processor may determine, utilizing an artificial intelligence model mapping weather events having weather impacts with a higher likelihood of occurrence proximate to each other in the latent space, a weather impact associated with a weather event. In some embodiments, the artificial intelligence model may be trained using historical weather event data and historical weather impact data associated with the historical weather event data. The processor may output the weather impact associated with the weather event to a user.

Automatic weather event impact estimation

A computer completes a data image analysis task. The computer receives a machine learning (ML) model trained for use with image data content characterized by first context. The computer receives an evaluation image dataset having evaluation image data content characterized by a second context. The computer receives a request to complete an image data analysis task for the evaluation image dataset using the ML model. The computer compares the contexts to determine whether the contexts are similar and whether the evaluation image dataset is compatible with the ML model. If the evaluation dataset is incompatible with the ML model, the computer uses the generative model to generate a ML model compatible synthetic image dataset based on the evaluation dataset. The computer applies the ML model to the synthetic image dataset to provide an answer for the data image analysis task; the computer delivers the answer to a user interface.

Artificial intelligence generated synthetic image data for use with machine language models

Data associated with a region, acquired by a robot may be passed to a previously trained classifier. The classifier outputs a classification label L, and a confidence score C. Responsive to determining that the confidence score C is below a threshold T, the acquired data can be added to a training data set associated with the classifier, and the classifier retrained using the training data set which include at least information from the acquired data. Responsive to determining that the confidence score C is below the threshold T, at least one candidate region having characteristic similarity to the region can be identified. Responsive to determining that the confidence score C is not below the threshold T, at least one candidate region having a different characteristic from the region can be identified. The robot may be caused to acquire data associated with the candidate region.

Online utility-driven spatially-referenced data collector for classification

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Sao Paulo, Brazil

Dario Augusto Borges Oliveira