They are all technologies that have been compared to a genie which mankind has let escape from a bottle to do either good or bad, depending on our ability to direct and control their power. Now, a new one can be added to that list: Artificial Intelligence, which has sparked a similar debate--how do you benefit from it without losing control over it? The benefits of AI are clear and becoming more important, as Marko Erman, Chief Technology Officer of Thales, notes. "Artificial intelligence can save lives whether we are talking about split-second decisions which must be made to assure safety of people in transport of all kinds and in defence, as well as protecting critical infrastructures. AI also has a very constructive role to play across all fields of activities, ranging from developing new medicines to exploring solutions to climate change" However, some experts warn that we are in a race against time before AI is so intelligent on its own that we will lose the power to control it.

Artificial Intelligence provides critical information to decision-makers when lives are at stake. But how do we make sure that it doesn't become smarter …

With the explosive potential of Artificial Intelligence, we ask: How do society and culture shape A.I.? And how does A.I. impact our sense of humanity? Can A.I. help us understand better the human mind? One powerful aspect of human intelligence not well captured in current AI systems is visual mental imagery. DoSER is pleased to feature Dr. Maithilee Kunda, associate professor of computer science and engineering at Vanderbilt University, as the speaker of our annual Holiday Lecture, where she will highlight how algorithms for visual mental imagery can improve the performance and robustness of AI systems. In this presentation, Dr. Kunda will discuss the way mental imagery can advance the capacity of AI for physical reasoning (visualizing how objects move, slide, and fall), object recognition (picturing the hidden face of an object in view), and big data visualization (comparing a current data plot to composited versions of previously seen plots).