What is commonsense in DATA SCIENCE?

The boundaries of commonsense are quite challenging to define, but we will go with this working definition:

Commonsense is the basic level of practical knowledge and reasoning concerning everyday situations and events that are commonly shared among most people.

For example, it’s commonsense that it’s OK to keep the closet door open, but not the fridge door, as the food inside might go bad.

Types of commonsense:

Commonsense knowledge can be categorized according to types, including but not limited to:

  • Social commonsense: people are capable of making inferences about other people’s mental states, e.g. what motivates them, what they are likely to do next, etc. This kind of inferences is captured by the ATOMIC knowledge base, discussed later. In addition, we each have a set of social norms of accepted behavior, e.g. knowing that “it’s impolite to comment on someone’s weight”. While these are often implicit in our actions and decisions, machines need to be taught them explicitly.

  • Temporal commonsense: natural language rarely communicates explicit temporal information. Instead it’s vague and relies on the commonsense knowledge of the listener. For example, when told that " Dr. Porter is taking a vacation " we can predict that Dr. Porter will not be able to see us soon, as opposed to when " Dr. Porter is taking a walk ". This requires knowing the typical duration of “taking a walk” (minutes) and that of “taking a vacation” (days). Other temporal knowledge is typical times, order, frequency, etc. of events which are addressed by the MC-TACO dataset and the TACO-LM time-aware contextual language model.

  • Physical commonsense: a glass will likely shatter if it falls to the floor, is a fact most people (and arguably cats) know. Physical commonsense includes knowledge about the physical properties and affordances of everyday objects, as tested in the PIQA dataset.

Commonsense is essential for humans to navigate everyday situations seamlessly and interact with each other in a reasonable and safe way, and for AI to understand human needs and actions better. Yet, endowing machines with such human-like commonsense reasoning capabilities has remained an elusive goal of AI research for decades. Past attempts, in the 1960s and 1970s, resulted in an AI winter, i.e. reduced interest and funding for AI research due to failed over-hyped research directions. In recent years, new interest in machine commonsense has emerged, with the availability of stronger computing power and huge amounts of data. With that said, the path to machine commonsense is unlikely to be brute force training larger neural networks with deeper layers.