Machine learning for seeing and hearing more

Modern machine learning has had some of its greatest successes in perceptual problems like image and sound understanding. Extracting relevant information from such high dimensional input is frequently the challenge in scientific data understanding as well. I’ll survey some exciting results from the Google Accelerated Science team in the areas of cellular imaging for biomedical research, extracting surprising results from human clinical imaging, and disease staging from auditory signals. Collectively these show the promise of further machine assistance in making sense of scientific data, with a great deal of exciting work still to come.