# Essential Features in a Theory of Context for Enabling Artificial General Intelligence
## 4 Understanding Context in Practical AI Research  
Based on the discussion in the previous section, we synthesize seven features that encompass a wide range of context-rich AI applications. These features are neither mutually exhaustive nor completely non-overlapping, but each has distinctive elements. At minimum, a theory of context must accommodate these seven features, although there may be domain-specific exceptions. 1. Locality: Locality is usually an important aspect of context, especially in \"embedding\" or representation learning algorithms. For example, in the word2 vec architecture [45], and others inspired by it [48; 49], only words within a certain window of the target word are 'activated' and considered as the context of that word. Similar notions apply in graph and network embeddings [50; 52]. However, more recently, context has become less local due to the use of powerful features such as biased random walks when training graph embeddings [51; 79; 80]. 2. Selective Activation of Salient Elements: Whether local (such as in the applications above) or non-local, context involves selective activation of salient elements. In the case of using random walks for embeddings, the nodes and edges in the walk may be considered to be the salient elements, even though they may not be considered local to the target node. In cognitive and agent-based architectures, certain kinds of long and short-term memory retrieval may be used to selectively activate salient elements [81]. 3. Relational Dependencies: In the definitions in Table 1, we noted indications of relational dependencies between elements or objects when context is invoked. Specifically, depending on the application, context may be defined as (emphasis ours) \"a probability _distribution over the concepts_ in an environment [20], a set of _relationships_ between objects [21], logical statements that represent _cause and effect_[22], or a function to select relevant features for object recognition [23].\" Another kind of relational dependency may arise due to the application. On social media, for example, the context for training a tweet embedding may include metadata (such as the user posting the tweet) rather than just the content in the tweet [82; 83]. This metadata expresses the relation between a user profile and between the actual tweet content, both of which can exist independently, but which need to be related to each other to learn good embeddings for either. 4. Implicitness: Especially in common sense reasoning and explainable AI research, certain pieces of information are considered implicit. [PERSON]'s conversational maxims are good examples of statements that explain some of the implicitness in conversation [24]; however, other similar maxims, or a generalization of the original maxims, may be necessary to categorize and explain implicitness in domains such as computer vision that are non-linguistic. Open-World Environments: As AI systems are implemented in organizations and customer-facing applications, they must increasingly adapt to the \"open world\". An open world environment is one where either the structure or parameterization (or both) is unknown or unknowable [84]. An example of the latter is a chaotic system with a high degree of uncertainty around the initial conditions [85]. One can probabilistically reason about the outcomes given the laws governing the dynamics of the system, but the outcome at a given point of time may be largely unknown with any reasonable confidence. The real world, and complex systems in the real world, are good examples of open worlds that are (at best) partially unknown, at least in practice, and (at worst) may not be completely knowable, even in theory. Philosophers have long argued about whether complete and provable knowledge is even possible in such systems, due to uncertainty [86], vagueness [87], and the circularity of induction [88]. An important aspect of open worlds is that, due to their (partially) unknown parameterization and structure, unexpected situations and \"novelties\" might occur [89].