Five AI failures you need to know before starting AI project

The description of the "failures" and especially the conclusions barely scrape the surface of what went wrong with these projects.

Some more examples of shallow weasel-worded "insights":

> "Bad engineering: It's tough to spot a particular issue while detecting the reasons for failure in the AI system. However, faulty engineering leads to wrong neural network settings, even when the data is accurate. But the above examples discussed are about highly responsible companies; they can afford the best engineers."

I am sure the engineers have published extensively on what particular their implementation was weak. But OP did not bother doing the research.

> "Complex Area of its Application: It might be a reason that the system under consideration is highly complex and need data that is difficult to obtain. Sometimes, the results obtained should be highly accurate to develop a precise algorithm. For instance, the usage of AI techniques for the medical industry, law, and other complex industries will be complicated. It requires active human minds, efficient workforce, and enough information to develop an accurate system."

Why did these ML-giants think they could tackle it in the first place? What do the engineers say on the assumed feasibility and how where they specifically wrong in their assumptions?

Structurally it's also somewhat shallow, but the author clearly intended otherwise by structuring it in parts with case studies and then some general analysis. Unfortunately I wouldn't call it insightful.

A giveaway of GPT-3 text is a lack of structure.