The second identity can be proved in a similar manner.

Breast cancer is the most common cancer with the highest mortality rate. Swift detection and diagnosis diminish the impact of the disease. However, classifying breast cancer using histopathology images -; tissues and cells examined under a microscope -; is a challenging task because of bias in the data and the unavailability of annotated data in large quantities. Automatic detection of breast cancer using convolutional neural network (CNN), a machine learning technique, has shown promise -; but it is associated with a high risk of false positives and false negatives. Without any measure of confidence, such false predictions of CNN could lead to catastrophic outcomes.

A Michigan Tech-developed machine learning model uses probability to more accurately classify breast cancer shown in histopathology images and evaluate the uncertainty of its predictions. Breast cancer is the most common cancer with the highest mortality rate. Swift detection and diagnosis diminish the impact of the disease. However, classifying breast cancer using histopathology images -- tissues and cells examined under a microscope -- is a challenging task because of bias in the data and the unavailability of annotated data in large quantities. Automatic detection of breast cancer using convolutional neural network (CNN), a machine learning technique, has shown promise -- but it is associated with a high risk of false positives and false negatives.

New work by computer scientists at Lawrence Livermore National Laboratory (LLNL) and IBM Research on deep learning models to accurately diagnose diseases from X-ray images with less labeled data won the Best Paper award for Computer-Aided Diagnosis at the SPIE Medical Imaging Conference on Feb. 19. The technique, which includes novel regularization and "self-training" strategies, addresses some well-known challenges in the adoption of artificial intelligence (AI) for disease diagnosis, namely the difficulty in obtaining abundant labeled data due to cost, effort or privacy issues and the inherent sampling biases in the collected data, researchers said. AI algorithms also are not currently able to effectively diagnose conditions that are not sufficiently represented in the training data. LLNL computer scientist Jay Thiagarajan said the team's approach demonstrates that accurate models can be created with limited labeled data and perform as well or even better than neural networks trained on much larger labeled datasets. The paper, published by SPIE, included co-authors at IBM Research Almaden in San Jose.

Star Trek got it right: In the future, we'll use computers by talking to them. Artificial intelligence will open the door to ever-more devastating attacks - but the most effective ones may be the most subtle, Darktrace's Dave Palmer says.China Photos/Getty Images Computational sustainability is a new interdisciplinary research field with the overarching goal of developing computational models, methods, and tools to help manage the balance between environmental, economic, and societal needs for a sustainable future. I will provide examples of computational sustainability problems, ranging from wildlife conservation and biodiversity, to poverty mitigation, to materials discovery for renewable energy materials. Technology # In the AI wars, Google wants to change the world one'Pixel' at a time By ECONOMICTIMES.COM Updated: 8 Oct, 2016, 11:50 hrs IST VIEW IN APP Hardware devices launched by Google are beads of pearl strung together by their A.I. powered technology running on clouds. By Sreeraman Thiagarajan Since Steve Jobs' version of Macintosh the one that was'the computer for the rest of us', to today's iPhone7, hardware devices are the strategy and cloud is a tactic at App...