In computer science, you are taught to comment your code. When you learn a new language, you learn the syntax for a comment in that language. Although the compiler or interpreter ignores all comments in a program, comments are valuable. However, there is a recent viewpoint that commenting code is bad, and that you should avoid all comments in your programs. In the 2013 article No Comment: Why Commenting Code Is Still a Bad Idea, Peter Vogel continued this discussion.

Imagine you want to build an application that helps to identify wine and cheese pairings. Applications solely based on machine learning, those ones which are based on experts' knowledge only, or a combination of both? Most of the machine learning algorithms were developed to solve a well-known problem in AI, which is called the'Knowledge Acquisition Bottleneck'. It deals with the question how subject matter experts (SMEs) can be enabled to work together with data scientists on knowledge models in an efficient and sustainable way (See also: Taxonomies and Ontologies – The Yin and Yang of Knowledge Modelling). Machine learning algorithms learn from data, and by that, successful implementations are obviously strongly related to data quality and the approaches taken to encode the semantics (meaning) of data.

The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. The recent history of expert systems, for example highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill- structured problems. But decades of work on such systems have convinced us that each of these approaches has difficulty "scaling up" for want a substantial base of real world knowledge.