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Models for Thinking: An Example of Why Data Sciences Increasingly Need the Humanities

Parsing such large-scale data sets – classifying genomic sequences, mapping forms of advertisement, observing online discussions, etc. – is a matter of organization: How do you make sense of, and classify, these clusters of information? The answer, often, is to configure them into abstract but coherent topics.

AI for Legalese

Have you ever signed a lengthy legal contract you didn’t fully read? Or have you every read a contract you didn’t fully understand? Contract review is a time-consuming and labor-intensive process for everyone concerned — including contract attorneys. Help is on the way. IBM researchers are exploring ways for AI to make tedious tasks like contract review easier, faster, and more accurate.

Imperial College London and Coursera Announce New Online Master’s Degrees in Machine Learning

Imperial College London, a globally ranked top 10 university, today announced an online MSc in Machine Learning on Coursera, a leading online learning platform. This is a new online master’s degrees in machine learning and will help students pursue the most advanced engineering roles in artificial intelligence (AI), data science, machine learning, bioinformatics, and more.

Interview: Rafael Irizarry, Professor of Biostatistics at Harvard University

I recently caught up with Rafael (Rafa) Irizarry, Professor of Biostatistics with the T.H. Chan School of Public Health at Harvard University to hear his unique perspective as an edX instructor regarding the rising demand for data scientists across most industries.

Parallel Storage Fuels Groundbreaking Neuroscience and Behavioral Research at Harvard

To alleviate bottlenecks and achieve the ideal balance of parallel performance and optimized availability, Harvard University’s Faculty of Arts and Sciences Research Computing (FASRC) deployed the DataDirect Networks (DDN®) GRIDScaler® GS7KX® parallel file system appliance with 1PB of storage. The installation has sped the collection of images detailing synaptic connectivity in the brain’s cerebral cortex.

The Future of AI and Education

In this contributed article, freelance human Avery Phillips discusses the practical uses of AI in the education industry as well as an assessment of AI’s role in current and future educational activities. Education professionals are taking on the task to implement AI into operations, and finding it to be quite beneficial.

Big Data Meets HPC – Exploiting HPC Technologies for Accelerating Big Data Processing

DK Panda from Ohio State University gave this talk at the Stanford HPC Conference. “This talk will provide an overview of challenges in accelerating Hadoop, Spark and Memcached on modern HPC clusters. An overview of RDMA-based designs for Hadoop (HDFS, MapReduce, RPC and HBase), Spark, Memcached, Swift, and Kafka using native RDMA support for InfiniBand and RoCE will be presented.”

Impacts of Artificial Intelligence and Higher Education’s Response

Northeastern University and Gallup just released a fascinating new survey that gauges public perceptions about artificial intelligence (AI) titled, “Optimism and Anxiety: Views on the Impacts of Artificial Intelligence and Higher Education’s Response.” Taken together, the results appear to be a wake-up call for higher education. Colleges and universities will have to adapt by designing a 21st century curriculum that empowers humans to become “robot-proof.”

Best of arXiv.org for AI, Machine Learning, and Deep Learning – November 2017

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

A Wave of Abundance from Big Ocean Data

In this contributed article, Matthew Mulrennan, Director of the Ocean Initiative at XPRIZE, and Dr. Jyotika Virmani, Senior Director for Planet & Environment at XPRIZE and prize lead for the Shell Ocean Discovery XPRIZE, explain how advancing big data collection in ocean science can improve the business of conservation and protection of our underwater resources and provide early warnings for water quality risks to human health and in lead to new underwater discoveries.