robot with phone 'like' 'like' 'like', illustration - Credit: Poynter

A Decade of Social Bot Detection

In this work, we briefly survey the first decade of research in social bot detection. Via a longitudinal analysis, we discuss the main trends of research in the fight against bots, the major results that were achieved, and the factors that make this never-ending battle so challenging. Capitalizing on lessons learned from our extensive analysis, we suggest possible innovations that could give us the upper hand against deception and manipulation. Studying a decade of endeavors in social bot detection can also inform strategies for detecting and mitigating the effects of other—more recent—forms of online deception, such as strategic information operations and political trolls.

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A closeup of a dog's nose - Photograph: Esther Kok/Getty Images

Now the Machines Are Learning How to Smell

There have been previous attempts to use machine learning to detect patterns that make one molecule smell like garlic and another like jasmine… Several other teams applied AI to that data and made successful predictions. But Wiltschko’s team took a different approach. They used something called a graph neural network, or GNN.

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Lego construction - Credit: Marcel Clemens

Toward ML-Centric Cloud Platforms

Cloud platforms, such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform, are tremendously complex. Cloud platforms are also extremely expensive to build and operate, so providers have a strong incentive to optimize their use. A nascent approach is to leverage machine learning (ML) in the platforms’ resource management using supervised learning techniques.

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Intelligent Systems for Geosciences: An Essential Research Agenda

Communications of the ACM, January 2019
By Yolanda Gil, Suzanne A. Pierce, et al.

“Many aspects of geosciences pose novel problems for intelligent systems research… A recently launched Research Coordination Network on Intelligent Systems for Geosciences followed a workshop at the National Science Foundation on this topic. This expanding network builds on the momentum of the NSF EarthCube initiative for geosciences, and is driven by practical problems in Earth, ocean, atmospheric, polar, and geospace sciences. Based on discussions and activities within this network, this article presents a research agenda for intelligent systems inspired by geosciences challenges.”

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