Machine Learning

Researchers Put Machine Learning on Path to Quantum Advantage

There are high hopes that quantum computing’s tremendous processing power will someday unleash exponential advances in artificial intelligence.

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Using Machine Learning to Develop Blood Test For Key Alzheimer’s Biomarker

Alzheimer’s disease, a terminal neurodegenerative disease, has historically been diagnosed based on observing significant memory loss.

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Machine Learning in Action for the Humanitarian Sector

IBM and the Danish Refugee Council developed a machine learning system to help understand migration via strategic forecasts and scenario analysis.

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Understanding Parkinson’s Disease with Machine Learning and The Michael J. Fox Foundation

IBM’s collaboration with The Michael J. Fox Foundation aims to better understand Parkinson's disease (PD), a chronic, degenerative neurological disorder.

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Probabilistic Programming with Pyro in WML

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 […]

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Using AI to Create New Fragrances

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 […]

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Restoring Balance in Machine Learning Datasets

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 […]

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Using AI to Design Deep Learning Architectures

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 […]

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An End-to-End Approach for Scaling Up Spectral Clustering

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 […]

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Addressing South Africa’s Cancer Reporting Delay with Machine Learning

Could you make a critical health policy decision using four-year-old data? Cancer registries hold vital data sets, kept tightly encrypted, containing demographic information, medical history, diagnostics and therapy. Oncologists and health officials access the data to understand the diagnosed cancer cases and incidence rates nationally. The ultimate goal is to use this data to inform […]

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Teaching AI to Learn from Non-Experts

Today my IBM team and my colleagues at the UCSF Gartner lab reported in Nature Methods an innovative approach to generating datasets from non-experts and using them for training in machine learning. Our approach is designed to enable AI systems to learn just as well from non-experts as they do from expert-generated training data. We […]

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IoT and Machine Learning to Reduce Energy Use in Cooling Systems

A new approach to operating a building’s cooling system using machine learning techniques and Internet of Things (IoT) data can help to drive down energy consumption and costs, as the global demand for energy increases. The buildings sector is one of the largest energy-consuming entities, accounting for a staggering 40 percent of global energy consumption […]

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