Deep neural network architectures for dual process computation

Dual process theories have been used to explain the different modes of behavior of the human brain when processing information. These theories became popular with the work of Kaheman, Thinking Fast and Slow, but they are based on decades of experimental evidence that the human brain works in two different modes. System 1 processes large amounts of visual and sensory information, efficiently and unconsciously. For instance, face and object recognition, speech processing and many other automatic functions are performed effortlessly by the human brain, using system 1. Other tasks require conscious effort, like answering complex riddles, executing non-trivial arithmetic operations of planning unfamiliar tasks. These tasks are performed by system 2. Existing systems, like convolutional neural networks, for vision, or transformers, for natural language processing, behave very much like system 1 in the human brain: they perform fast, high-throughput, processing of high-dimensional information, in an unconscious way. This dissertation will be focused on the design of deep neural network architectures that can be used to emulate the dual process computation that characterizes the human brain and also on the relation of dual process architectures and consciousness. Requisites: The student should have significant programming experience, and practical knowledge of machine learning languages and environments, such as PyTorch or TensorFlow. He/she should also have interest in developing the understanding of neuroscience and human psychology. Notes: The selected student will have access to the facilities of INESC-ID and the MLKD group ( https://mlkd.idss.inesc-id.pt/ ), including computing facilities that include four DELL PowerEdge C41402 servers, eight NVIDIA 32GB Tesla V100, four NVIDIA 48GB A40 and four NVIDIA 64GB Tesla A100, among other computing servers ( https://mlkd.idss.inesc-id.pt/cluster )