The overall goal of Artificial Intelligence is to give computers the capabilities that define human intelligence, without necessarily replicating human intelligence exactly. These capabilities can be seen as being provided, roughly, by two interacting systems:  1) fast, reactive, and largely unconscious perception and action, and 2) slow, deliberative and largely conscious integration, synthesis, planning, and evaluation. Another way to explain the divide is that system 1 is largely non-symbolic, and system 2 largely symbolic.  Both of these have been the subject of an enormous amount of research in AI, but recently advances in deep learning and computational infrastructure have let to great advances in less-symbolic AI, giving us systems that are very capable in vision, speech recognition and synthesis, and, more surprisingly, game playing, language translation, and simple question answering. But these advances have all perfected particular skills, not broad competences. The other branch of AI is about knowledge and reasoning: representing skills for reuse, composing skills to solve novel problems, and deliberating on those solutions, for example, to provide explanations; in short it is about reasoning.  Towards smarter AI, skill learning and reasoning might be unified to allow computers to solve complicated problems using skills and knowledge, and move us closer to more general AI.

The Strong AI Lab works on learning-based general artificial intelligence, with a focus on complex problem solving with Natural Language (NL); this is a very challenging area of AI research, with high potential commercial impact. We will combine the current Deep Learning revolution in AI with techniques derived from symbolic AI, including Knowledge Representation, Knowledge Capture, and Automated Reasoning, to give computers the powers of understanding and integration. This program is driven by the observation that knowledge can be seen exactly that which speeds up the learning of a new task, and that the main store of such existing knowledge is natural language.  While our long-term goal of human level understanding and reasoning is challenging, near-term advances in understanding text, diagrams and tables so they can be automatically repurposed and combined to answer questions are in reach and could be readily commercialised.