Neuro-Symbolic AI - overview
The Neuro-Symbolic AI (NS) initiative aims to conceive a fundamental new methodology for AI, to address the gaps remaining between today's state-of-the-art and the full goals of AI, including AGI. In particular it is aimed at augmenting (and retaining) the strengths of statistical AI (machine learning) with the complementary capabilities of symbolic or classical AI (knowledge and reasoning). It is aimed at a construction of new paradigms rather than superficial synthesis of existing paradigms, and revolution rather than evolution.
The primary goals of NS are to demonstrate the capability to:
- Solve much harder problems
- Learn with dramatically less data, ultimately for a large number of tasks rather than one narrow task)
- Provide inherently understandable and controllable decisions and actions
NS research directly addresses long-standing obstacles including imperfect or incomplete knowledge, the difficulty of semantic parsing, and computational scaling. NS is oriented toward long-term science via a focused and sequentially constructive research program, with open and collaborative publishing, and periodic spinoff technologies, with only a small selection of motivating use cases over time.
The primary ones currently include the pursuit of true natural language understanding via the proxy of question answering; automatic data science, programming, and mathematics; and financial trading/risk optimization as ways to showcase the fundamental principles being developed. Research in NS is inherently multi-disciplinary and includes (among many other things) work in learning theory and foundations, optimization and algorithms, knowledge representation and acquisition, logic and theorem proving, reinforcement learning, planning, and control, and multi-task/meta/transfer learning.
