Basing on the "Artificial Intelligence for the Real World" article by Thomas H. Davenport, Rajeev Ronanki and published by the Harvard Business Review. Answer the following questions:

1. Discuss the creative ways that prediction machines are driving the three types of AI discussed in the article. How is prediction critical to the success of each type of AI?
2. Position the three types of AI discussed in the article within the 2x2 of knowns/unknowns from your book and our lecture together. Discuss the potential pitfalls of each type of AI from this context, along with the typical types of mistakes that each AI could be prone to make based on their positioning within the 2x2.
3. The case concludes by discussing the future cognitive company, and highlights marketing, health care, financial services, education, and professional services as industries primed for an infusion of AI. I'd like your group to consider our new economic perspective on prediction, and come up with what you believe to be an unexpected (or non-obvious) industry outcome from a sharp drop in the price of quality predictions. This could be unexpected due to your identified impact coming from an unanticipated industry, or it could be an unanticipated impact from within one of the industries highlighted in the article. Don't overthink the mechanics of this - we are early in our discussion of AI and are working with a limited but powerful toolset. At the industry level, what unexpected outcome from a sharp drop in the price of prediction can your group anticipate as you consider the impact of the three types of AIthat the article describes?