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
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
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
Nonetheless, while the dark side is daunting, emerging research, development, and education across interdisciplinary topics addressing cybersecurity and privacy are yielding promising results. The shift from R&D on siloed add-on security, to new fundamental research that is interdisciplinary, and positions privacy, security, and trustworthiness as principal defining objectives, offer opportunities to achieve a shift in the asymmetric playing field.Read More
Much work in evolutionary and developmental psychology points in the same direction; the mind is not one thing, but many.
To build AIs able to comprehend open text or power general-purpose domestic robots, we need to go further. A good place to start is by looking at the human mind, which still far outstrips machines in comprehension and flexible thinking.