Meaning Versus Information, Prediction Versus Memory, and Question Versus Answer

Brain science and artificial intelligence have made great progress toward the understanding and engineering of the human mind. The progress has accelerated significantly since the turn of the century thanks to new methods for probing the brain (both structure and function), and rapid development in deep learning research. However, despite these new developments, there are still many open questions, such as how to understand the brain at the system level, and various robustness issues and limitations of deep learning. In this informal essay, I will talk about some of the concepts that are central to brain science and artificial intelligence, such as information and memory, and discuss how a different view on these concepts can help us move forward, beyond current limits of our understanding in these fields. 1 Introduction Brain and neuroscience, psychology, artificial intelligence, all strive to understand and replicate the functioning of the human mind. Advanced methods for imaging, monitoring, and altering the activity of the brain at the whole-brain scale are now available, allowing us to probe the brain in unprecedented detail. These methods include high-resolution 3D imaging (using both physical sectioning and optical sectioning), monitoring ongoing neural activity (calcium imaging), and altering the activation of genetically specific neurons (optogenetics). On the other hand, in artificial intelligence, deep learning based on decades-old neural networks research made exponential progress, and it is now routinely beating human performance in many areas including object recognition and game playing. However, despite such progress in both fields, there are still many open questions. In brain science, one of the main question is how to put together the many detailed experimental results into a system-level understanding of brain function.