International Business Machines Corporation

Daniel Salles Chevitarese

Marcio Ferreira Moreno

Rafael Rossi De Mello Brandao

Renato Fontoura De Gusmao Cerqueira

Helon Vicente Hultmann Ayala

Lucas Correia Villa Real

Paul Borrel

Paulo Rodrigo Cavalin

Bianca Zadrozny

Alvaro Bueno Buoro

Ana Fucs

Vinicius Costa Villas Boas Segura

Ruberth A A Barros

Breno William Santos Rezende De Carvalho

Juliana Jansen Ferreira

Daniela De Mattos Szwarcman

An ore content prediction system is provided. The system receives structured geological data that is derived based on spatial geological information that is associated with an input region. The received structured geological data includes a plurality of multidimensional tensors that are derived from spatial geological information of a plurality of sub-regions of the input region. The spatial geological information includes one or more types of data. The system trains a prediction model to produce a prediction output based on an average grade of an ore of a target mineral type at a target region by using the received structured geological data. The system identifies a relationship of the structured geological data to the prediction output and determines a revised input region based on the identified relationship.

Estimate ore content based on spatial geological data through 3D convolutional neural networks

A modular node includes a semiconductor chip, including modular sensors to detect events or changes in environment, modular actuators for moving or controlling an object, a non-transitory computer readable medium storing a program, a processor executing the program configured to control module networking and setup, and a neuromorphic chip configured to receive information from the modular sensors and modular actuators, autonomously process the information received from the modular sensors and actuators, determine a validity of the information received from the modular sensors and actuators, and autonomously communicate with neighboring modular nodes for network auto-arrangement.

Smart sensing: a system and a method for distributed and fault tolerant hierarchical autonomous cognitive instrumentation

Systems and methods are described for mapping input devices in real time. The method may include controlling a camera to capture a plurality of images, performing object recognition on the images to identify a plurality of objects, determining properties for each identified object that enable the corresponding object to provide spatial data and interaction data of a preferred input device, ranking the objects according to the determined properties, presenting the identified objects in an order corresponding to the rank that enables a user to select one of the presented objects, tracking the selected object and a human being, generating spatial data and interaction data from a result of the tracking, and providing the generated spatial data and interaction data to a computer to emulate the events of the preferred input device.

Method and system to automatically map physical objects into input devices in real time

Automatically identifying speakers in real-time through media processing with dialog understanding. A plurality of audio streams may be received, an audio stream representing a speech of a participant speaking during an online meeting. A voice characteristic of a voice corresponding to the speech of the participant in the audio stream may be determined. The plurality of audio streams may be converted into text and a natural language processing may be performed to determine content context of the dialog. The natural language processing infers a name to associate with the voice in the audio stream based on the determined content context. A data structure linking the name with the voice may be created and stored in a knowledge base. A user interface associated with the online meeting application is triggered to present the name or identity of the speaker.

Automatically identifying speakers in real-time through media processing with dialog understanding supported by AI techniques

An automatic method of determining an image composition procedure that generates a new image visualization based on aggregations and variations of input images. A set of input images is received. Visual features are extracted from the input images. Context associated with input images is received. Based on the extracted visual features and the context associated with the input images, a composition procedure comprising a set of image operations to apply on the set of input images is learned. One or more image operations in the composition procedure are determined to present to a user. A difference visualization image associated with the input images may be generated by executing the one or more image operations.

Machine learning to predict cognitive image composition

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Rio de Janeiro, Brazil

Daniel Salles Chevitarese