# Essential Features in a Theory of Context for Enabling Artificial General Intelligence
## 2 Background on Context: Definitions and Example Usage  
While AI research has now been maturing for almost 60 years since the famous Dartmouth conference [10], interestingly, task-specific development of AI (or what is now popularly referred to as Artificial Narrow Intelligence (ANI), in contrast with AGI) was not the most compelling research area at the time. Instead, the goal was to build machines that exhibited true intelligence, and had a degree of generalizability that could potentially make them indistinguishable from humans. There was an optimistic belief that such breakthroughs were on the horizon, but an analysis of AI history shows that the field has tended to go through ebbs and flows, with several AI winters having come and gone over the decades, many involving neural networks and their promise [11]. To make meaningful progress, and advance the research agenda, the last three decades have witnessed a particularly increased development in building algorithms and systems that could do well in specific tasks including face recognition [12], machine translation [13], sentiment analysis [14] and information extraction [15], to only name a few.  
However, now that excellent progress has been achieved on some of those tasks, researchers are again starting to come around to the development of more 'general' and less 'task-centric' AI, particularly in the NLP and computer vision communities. In Section 3, we describe research in specific sub-areas in AI to bolster the claim that context is an important part of this discussion. However, despite this interest, a standard definition of context has proven to be surprisingly complex. Oxford Languages defines it as the \"circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed.\"  
More academic treatments, such as in areas within the social sciences and humanities (e.g., anthropology and sociology), generally understand context to refer to objects or entities surrounding a \"focal event\" e.g., that it is a \"frame that surrounds the event and provides resources for its appropriate interpretation\" [16]. Table 1 provides a sample of definitions and studies from both common and academic sources. We are not aware of context ever having been defined in an AI paper, although it is in common use today in many applications and sub-communities (as subsequently shown).  
No matter the definition adopted, we could well argue that, at an intuitive level, these definitions share many common threads, including the relativistic nature of context. Beyond a definition, it is important to explicitly address the question: why is context important for AI? We believe that the most effective way to answer this question is to present some representative examples below where context is shown to be vital for human thinking, reasoning and navigation of everyday situations. For an AI to successfully interact with humans, or replicate (or even supersede) these abilities, it must be both aware of context, and be able to handle it appropriately, which may or may not mirror human cognition. We designate such an AI as a 'context-rich AI'. Subsequently, in Section 3, we study various sub-areas of current AI research to understand how context is drawn upon in such areas, directly or indirectly. Based on this study, Section 4 enumerates and makes a case for essential features that a theory of context must incorporate and address, as applicable to AI research.