Strong AI Lab (SAIL)
Situated within the NAOI, the Strong AI Lab (SAIL) is the central R&D engine for neuro-symbolic artificial intelligence. SAIL provides the core technical innovations and deep learning architectures that power the broader AAAIP national platform.
The mission of the Strong AI Lab (SAIL) is to transcend the limitations of today’s narrow, pattern-matching AI. We are pioneering neuro-symbolic architectures—combining the learning power of neural networks with the reasoning capability of symbolic logic—to build future AI systems capable of genuine understanding, robust generalisation, and transparent self-explanation.
Neuro-Symbolic Integration
Current deep learning models are powerful pattern matchers but function as brittle “black boxes” that cannot explain their decisions. SAIL’s primary mission is to overcome this limitation by developing hybrid neuro-symbolic architectures. By fusing the robust learning capabilities of neural networks with the explicit, interpretable logic of symbolic AI, we create systems that can both learn from data and reason about the world, resulting in AI that is more robust, data-efficient, and trustworthy.
Machine Reasoning
True intelligence requires more than just recognizing correlations; it requires the ability to think through complex problems step-by-step. SAIL researchers focus on endowing machines with the capacity for genuine reasoning—including deduction, induction, and abduction. This allows our agents to handle novel situations, plan multi-stage actions in dynamic environments, and solve problems that require understanding causality rather than just statistics.
Knowledge Graphs & Structured Memory
To reason and explain itself, an AI needs a structured understanding of the world. We utilize large-scale Knowledge Graphs to provide grounded, factual context to our models. Unlike unstructured text, these graphs map entities and their relationships explicitly. This allows our systems to trace their reasoning paths through verified facts, enabling traceable, verifiable explanations for their outputs that human users can understand and trust.