FOR IMMEDIATE RELEASE
Advancing AI and Neuroscience: Introducing the AI Institute for Artificial and Natural Intelligence (ARNI)
Research.University, June 3, 2023 – Today marks a significant milestone in the fields of artificial intelligence (AI) and neuroscience as Columbia University proudly announces the establishment of the AI Institute for Artificial and Natural Intelligence (ARNI). This groundbreaking interdisciplinary center brings together leading researchers from across the country with the shared mission of connecting the remarkable advancements in AI systems to the revolution in our understanding of the brain.
Harnessing the collective expertise of esteemed academic and industry partners, ARNI sets out to develop cutting-edge AI-based technologies that deepen our understanding of the brain and pave the way for remarkable improvements. Under the leadership of Principal Investigators Richard Zemel, Kathleen McKeown, Christos Papadimitriou, Liam Paninski, and Xaq Pitkow, ARNI will focus on four key areas:
- Learning with limited data: ARNI will pioneer new AI-based methods to tackle the challenge of learning from limited data, both in AI and neuroscience. These methods will enable the development of brain models that shed light on how the brain learns from limited experiences.
- Reasoning about causality and uncertainty: ARNI aims to develop innovative AI-based techniques for reasoning about causality and uncertainty, two fundamental aspects of human cognition. By constructing brain models that capture these processes, we can unravel the mechanisms behind decision-making in uncertain situations.
- Lifelong learning: ARNI’s researchers will explore novel AI-based methods for lifelong learning, empowering the brain to continuously acquire new knowledge and skills throughout an individual’s lifetime. These advancements will deepen our understanding of how the brain learns and adapts over time.
- Bridging the gap between artificial and biological networks: ARNI seeks to bridge the current gap between artificial and biological networks by developing AI-based methodologies. These methodologies will facilitate the creation of brain models that elucidate the inner workings of the brain and drive the development of AI technologies for brain enhancement.
