A comprehensive analysis of tumor ecosystems that represents an important step in research that may lead to precision medicine approaches to treat breast cancer.
IBM Research and New York University are using AI to analyze retina imaging data and help to assess the presence of glaucoma.
There are high hopes that quantum computing’s tremendous processing power will someday unleash exponential advances in artificial intelligence.
Alzheimer’s disease, a terminal neurodegenerative disease, has historically been diagnosed based on observing significant memory loss.
IBM and the Danish Refugee Council developed a machine learning system to help understand migration via strategic forecasts and scenario analysis.
IBM’s collaboration with The Michael J. Fox Foundation aims to better understand Parkinson's disease (PD), a chronic, degenerative neurological disorder.
In a previous post we explained how to write a probabilistic model using Edward and run it on the IBM Watson Machine Learning (WML) platform. In this post, we discuss the same example written in Pyro, a deep probabilistic programming language built on top of PyTorch. Deep probabilistic programming languages (DPPLs) such as Edward and […]
Bringing Art and Science Together Skilled perfumers bring art and science together to design new fragrances, a talent that takes ten or more years to develop. Crafting a fragrance that leaves an impression is one of the most important components a consumer considers when forming a positive or negative opinion about everyday products like laundry […]
If you want to teach a child what an elephant looks like, you have an infinite number of options. Take a photo from National Geographic, a stuffed animal of Dumbo, or an elephant keychain; show it to the child; and the next time he sees an object which looks like an elephant he will likely […]
Selecting the best architecture for deep learning architectures is typically a time-consuming process that requires expert input, but using AI can streamline this process. I am developing an evolutionary algorithm for architecture selection that is up to 50,000 times faster than other methods, with only a small increase in error rate. Deep learning models are […]
Clustering is one of the most fundamental problems in machine learning and data mining tasks, such as image segmentation, power load clustering, and community detection for social networks. But well-known clustering techniques like K-Means, which is based on Euclidean proximity, may not be capable of clustering data that lies on a high-dimensional manifold, as illustrated […]