Many cancer patients undergo treatment with multiple drugs, each of which attacks cancer in a different way, so the combination fights cancer on many fronts. But more drugs mean higher risks of side effects. "Most cancer therapy is now a combination treatment," says Avinash (Avi) Sahu, Ph.D., assistant professor at The University of New Mexico Comprehensive Cancer Center. Sahu joined UNM from Harvard and Dana-Farber Cancer Institute. "We wanted to find drugs that could suppress two cancer-causing pathways at the same time."

A global virtual conference aimed at promoting the use of artificial intelligence (AI) and machine learning (ML) for the development of Odisha and advancement of Odia language in the digital era is being organized on 4th October Sunday, on the virtual meeting platform Zoom. The conference, called Odias in ML Conference, is being organized by a group of Odias, also called Odias in ML, with a shared interest in AI and ML. The conference also aims to showcase career and entrepreneurship opportunities in AI and ML for Odias across the world. This first-of-its-kind conference will see participation of a multitude of stakeholders including technologists, researchers, academicians, business executives, entrepreneurs, policymakers, linguists, language activists, media persons and community leaders, all with a commitment to AI and machine learning. The speakers, all Odias based across three continents, will come together to brainstorm how the opportunities created by these emerging technologies can be leveraged effectively to propel the next phase of growth for Odisha and Odias.

Unsupervised machine learning methods can be of great help in many traditional engineering disciplines, where huge amount of labeled data is not readily available or is extremely difficult or costly to generate. Two specific examples include the structure of granular materials and atomic structure of metallic glasses. While the former is critically important for several hundreds of billion dollars global industries, the latter is still a big puzzle in fundamental science. One thing is common in both the examples is that the particles are the elements of the ensembles that are embedded in Euclidean space and one can create a spatially embedded network to represent their key features. Some recent studies show that clustering, which generically refers to unsupervised learning, holds great promise in partitioning these networks. In many complex networks, the spatial information of nodes play very important role in determining the network properties. So understanding the structure of such networks is very crucial. We have compared the performance of our newly developed modularity function with some of the well-known modularity functions. We performed this comparison by finding the best partition in 2D and 3D granular assemblies. We show that for the class of networks considered in this article, our method produce much better results than the competing methods.

Social media has caused an explosion in the amount of data generated by people and businesses in the last five years. That has led to the rise of artificial intelligence and machine learning coupled with the input of humans to make sense of streams of data, according to experts on Argo Digital's panel Sunday at South by Southwest on "How Data and Machine Learning/AI Affect Risk Transfer in the 21st Century." Jason Abbruzzese, business reporter with Mashable, moderated the panel for Argo Digital, an emerging insure tech practice within property and casualty carrier Argo Group, based in San Antonio. By analyzing the data, all businesses are trying to make themselves smarter, said Andy Breen, senior vice president for Argo Digital and adjunct professor at NYU Stern School of Business and one of the panelists. Until recently, the tools did not exist to sift through all the data and extract insights that make a business operate better, he said.