A powerful new model could make global warming estimates less vague
One of the biggest sources of climate uncertainty is how clouds will behave. Caltech physicist Tapio Schneider is trying to give us some answers.
Read MoreTag: Machine Learning
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.
Read More
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.
Read More
The Quest to Make a Bot That Can Smell as Well as a Dog
Scientists are trying to crack the code of how smell works—and create robots that can sniff out the world’s secrets like a dog.
Read More
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.
Read More
The Seven Tools of Causal Inference, with Reflections on Machine Learning
Communications of the ACM, March 2019
By Judea Pearl
“Unlike the rules of geometry, mechanics, optics, or probabilities, the rules of cause and effect have been denied the benefits of mathematical analysis.” “…the art of automated reasoning.”
Read More
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.”
Read More
Deep Learning Hunts for Signals Among the Noise
Communications of the ACM, June 2018, Vol. 61 No. 6, Pages 13-14
By Chris Edwards
“The secret to deep learning’s success in avoiding the traps of poor local minima may lie in a decision taken primarily to reduce computation time.”
Read More
AI is reinventing the way we invent
MIT Technology Review, Feb. 15, 2019
By David Rotman
“In other words, AI’s chief legacy might not be driverless cars or image search or even Alexa’s ability to take orders, but its ability to come up with new ideas to fuel innovation itself.”
Read More