Verified AI is the goal of achieving strong, ideally provable assurances of correctness and trustworthiness of AI systems with respect to mathematically specified requirements. Five challenge areas for verified AI: Environment modelling, Formal specification, Modeling learning systems, Scalable formal engines, and Correct-by-construction design.
Toward Verified Artificial Intelligence Read More
She was a star engineer who warned that messy AI can spread racism. Google brought her in. Then it forced her out. Can Big Tech take criticism from within?
What Really Happened When Google Ousted Timnit Gebru Read More
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.
A Decade of Social Bot Detection 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.
Now the Machines Are Learning How to Smell 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.
The Quest to Make a Bot That Can Smell as Well as 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.
Cybersecurity Research for the Future 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.