How brain-inspired AI and neuroscience advances machine learning

While building artificial systems does not necessarily require copying nature -- after all, airplanes fly without flapping their wings like birds -- the history of AI and machine learning convincingly demonstrates that drawing inspirations from neuroscience and psychology can lead to significant breakthroughs, with deep neural networks and reinforcement learning being perhaps the two most prominent examples. Taking inspiration from the brain, our IBM Research team recently used machine learning techniques to develop computational models of attention and memory. Our ultimate goal is to build lifelong learning AI systems, able to adapt to new environments while retaining what they have learned so far. This challenge can be broken down into short term adaptation, where there is little time to change a system and train it on what to pay attention to, and long term adaptation that is inspired by how the human brain forms memory and how neuroplasticity (e.g., adult neurogenesis) affects this process. Our team developed two important innovations that enable short-term and long-term adaptation which are a result of reward-driven attention techniques and enabling network "plasticity."