Topic: machine vision

We have entered a golden age of artificial intelligence research, driven mainly by the advances in the artificial neural networks over the last several decades. Applications of these techniques—to machine vision, speech recognition, autonomous vehicles, natural language, and many other domains—are coming so quickly that many observers predict that the long-elusive goal of “Artificial General Intelligence” (AGI) is within our grasp. However, we still cannot build a machine capable of building a nest, stalking prey, or loading a dishwasher. I will describe how evolution may have shaped the algor

State-of-the-art machine vision models can predict human recognition memory for complex scenes with astonishing accuracy. In this talk I present work that investigated how memorable scenes are actually remembered and experienced by human observers. We found that memorable scenes were recognized largely based on recollection of specific episodic details but also based on familiarity for an entire scene. I thus highlight current limitations in machine vision models emulating human recognition memory, with promising opportunities for future research. Moreover, we were interested in what observers