Deep Learning Research in Africa with Yabebal Fantaye & Jessica Phalafala
Google Cloud Platform Podcast - Podcast autorstwa Google Cloud Platform
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Today, Melanie brings you another great interview from her time at Deep Learning Indaba in South Africa. She was joined by Yabebal Fantaye and Jessica Phalafala for an in-depth look at the deep learning research that’s going on in the continent. At the African Institute for Mathematical Sciences, the aim is to gather together minds from all over Africa and the world to not only learn but to use their distinct perspectives to contribute to research that furthers the sciences. Our guests are both part of this initiative, using their specialized skills to expand the abilities of the group and stretch the boundaries of machine learning, mathematics, and other sciences. Yabebal elaborates on the importance of AIMS and Deep Learning Indaba, noting that the more people can connect with each other, the more confidence they will gain. Jessica points out how this research in Africa can do more than just advance science. By focusing on African problems and solutions, machine learning research can help increase the GDP and economic standards of a continent thought to be “behind”. Jessica Phalafala Jessica Phalafala is a PhD Applied Mathematics student at Stellenbosch University and currently affiliated with the African Institute for Mathematical Sciences. In her mid-twenties, she finds herself with four qualifications all obtained with distinction, including a Master of Science in Pure Mathematics degree from the University of the Witwatersrand. Jessica is interested in using her functional analysis background together with a number of newly developed skills to contribute towards developing rigorous mathematical theory to support some existing deep learning methods and algorithms for her PhD research. Outside of research she takes great interest in fast-tracking the level of accessibility of higher education in South Africa as co-founder of the Sego Sa Lesedi Foundation, a platform created to inform underprivileged high school learners of career and funding opportunities in science as well as provide them with mentorship as they transition into undergraduate studies. Yabebal Fantaye Dr. Fantaye is an AIMS-ARETE Research Chair based in South Africa. His research is in applying artificial intelligence and advanced statistical methods to cosmological data sets in order to understand the nature of the universe and to satellite images of the Earth in order to find alternative ways to monitor African development progress. Dr. Fantaye is a fellow of the World Economic Forum Young Scientists community, and a fellow and a Chair of the Next Einstein Forum Community of Scientists. Cool things of the week A Kubernetes FAQ for the C-suite blog BigQuery and surrogate keys: a practical approach blog Adding custom intelligence to Gmail with serverless on GCP blog Announcing Cloud Tasks, a task queue service for App Engine flex and second generation runtimes blog Unity and DeepMind partner to advance AI research blog Interview African Institute for Mathematical Sciences site Provable approximation properties for deep neural networks research Next Einstein Initiative site Square Kilometer Array (SKA) site University of the Witwatersrand site Council of Scientific and Industrial Research (CSIR) site South African National Space Agency (SANSA) site National Astrophysics and Space Science Programme (NASSP)site IndabaX site Coursera site Andrej Karpathy research Andrej Karpathy Blog blog Question of the week If I’m using the Cluster Autoscaler for Kubernetes (or GKE), how can I prevent it from removing specific nodes from the cluster when scaling down? How can I prevent Cluster Autoscaler from scaling down a particular node? github What types of pods can prevent CA from removing a node? github Where can you find us next? Mark will definitely be at Kubecon in December and will probably be at Unite L.A. this month. Melanie is speaking at Monktoberfest Oct 4th in Portland, Maine and will be at CAMLIS the following week.