It's important to approach the subject area of AI from the philosophy of design thinking. This is, so that there are a structure and an approach that is chartered out in the complex world of AI. Since there is an extensive range of solutions that one can design for AI applications, developers need to remain vigilant about the changing nature of Artificial Intelligence. Therefore, to ensure quality and proper guidance, it's best to think about AI from a design-thinking perspective. This captures much of the innovation that will potentially occur in the AI space so that the problem can be solved using another approach. Additionally, as AI is rapidly evolving, it makes the process of structuring a final proposal that much easier.

In the quarter century since Volvo rolled my ruby-red 240 wagon off an assembly line, its creator has gone from a modest Swedish independent automaker to part of a massive American conglomerate to an arm of a Chinese automaker that's pushing driverless cars and racing to go fully electric in the next two years. It's been a funky journey for a funky automaker, but it's no mere aberration or footnote. If you want to understand the car industry of the past 25 years--and maybe the next 25--study Volvo's journey. For a niche player best known for shuttling liberals and hippies, Volvo has long played an outsized role in the evolution of the auto industry. It invented and made standard the three-point seat belt.

How does this lead to innovation? What does this have to do with Deep Learning? Intuition like consciousness is something that we are all aware of its existence but likely have not investigated in enough detail to have a grounded understanding of its nature. In fact, I would say that there's more research on the nature of consciousness than research on intuition. I've written earlier about a few research groups that have explored consciousness with respect to an artificial general intelligence, however I don't think has been equivalently the same effort with the study of intuition.