Editing Commonsense reasoning (section)

== Challenges in automating commonsense reasoning ==
As of 2014, there are some commercial systems trying to make the use of commonsense reasoning significant. However, they use statistical information as a proxy for commonsense knowledge, where reasoning is absent. Current programs manipulate individual words, but they don't attempt or offer further understanding. According to Ernest Davis and [[Gary Marcus]], five major obstacles interfere with the producing of a satisfactory "commonsense reasoner".<ref name="Davis Marcus"/>

* First, some of the domains that are involved in commonsense reasoning are only partly understood. Individuals are far from a comprehensive understanding of domains such as communication and knowledge, interpersonal interactions or physical processes.
* Second, situations that seem easily predicted or assumed about could have logical complexity, which humans’ commonsense knowledge does not cover. Some aspects of similar situations are studied and are well understood, but there are many relations that are unknown, even in principle and how they could be represented in a form that is usable by computers.
* Third, commonsense reasoning involves [[plausible reasoning]]. It requires coming to a reasonable conclusion given what is already known. Plausible reasoning has been studied for many years and there are a lot of theories developed that include probabilistic reasoning and [[non-monotonic logic]]. It takes different forms that include using unreliable data and rules, whose conclusions are not certain sometimes.
* Fourth, there are many domains, in which a small number of examples are extremely frequent, whereas there is a vast number of highly infrequent examples.
* Fifth, when formulating presumptions it is challenging to discern and determine the level of abstraction.

Compared with humans, as of 2018 existing computer programs perform extremely poorly on modern "commonsense reasoning" benchmark tests such as the [[Winograd Schema Challenge]].<ref>{{cite web|title=The Winograd Schema Challenge|url=https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html|website=cs.nyu.edu|access-date=9 January 2018}}</ref> The problem of attaining human-level competency at "commonsense knowledge" tasks is considered to probably be "[[AI complete]]" (that is, solving it would require the ability to synthesize a [[artificial general intelligence|human-level intelligence]]).<ref>Yampolskiy, Roman V. "AI-Complete, AI-Hard, or AI-Easy-Classification of Problems in AI." MAICS. 2012.</ref><ref>Andrich, C, Novosel, L, and Hrnkas, B. (2009). [https://web.archive.org/web/20180109181028/https://pdfs.semanticscholar.org/59e3/495f4f1e70629e17ddbbce06834626b2a363.pdf Common Sense Knowledge]. Information Search and Retrieval, 2009.</ref> Some researchers believe that [[supervised learning]] data is insufficient to produce an artificial general intelligence capable of commonsense reasoning, and have therefore turned to less-supervised learning techniques.<ref>{{cite news |last1=Smith |first1=Craig S. |title=Computers Already Learn From Us. But Can They Teach Themselves? |url=https://www.nytimes.com/2020/04/08/technology/ai-computers-learning-supervised-unsupervised.html |access-date=3 May 2020 |work=The New York Times |date=8 April 2020}}</ref>