Events

Abstract: Models of vision have come far in the past 10 years. Deep neural networks can recognise objects with near-human accuracy, and predict brain activity in high-level visual regions. However, most networks require supervised training using ground-truth labels for millions of images, whereas brains must somehow learn from sensory experience alone. We have been using

Abstract: Choice architecture suggests that much of what we decide is influenced by that options are presented. This means that the choice environment can encode intelligence that will help (or can hurt) the decision maker. The talk will start by reviewing some results from choice architecture and describe how the environment can affect choice through

Abstract: Like most of our technologies today, AI dramatically increases the world's carbon footprint, thereby strengthening the severity of the coming downfall of life on the planet. In this talk, I propose that recent advances in large dimensional mathematics, and especially random matrices, could help AI engage in the future economic growth. This being said,

Abstract: Bees have a diverse instinctual repertoire that exceeds in complexity that of most vertebrates. This repertoire allows the social organisation of such feats as the construction of precisely hexagonal honeycombs, an exact climate control system inside their home, the provision of the hive with commodities that must be harvested over a large territory (nectar,

Abstract: Some of my past and current research looks at "decisions from  experience,” i.e., decisions based on the personally experienced outcomes of past choices, along the lines of reinforcement learning models and how such learning and updating is related to and differs from the way in which people and other intelligent agents use other sources of information,