My Digital FootPrint

The unimagined: unimagined  Your mind will have already assumed that this chapter, from its title, is presenting the unimagined outcomes, a discourse to overly optimistic viewpoints.  It is reasonable that anyone reading a book on Pathways to General AI will have interrupted the word, “unimagined” within this context. Before you skip this chapter as you are a believer that AI is the future or don’t need another comfort blanket chapter speaking to the fearful of AI,  this chapter is neither. It is not a wild fantasy of the possible nor is a negative chapter of the unknown risks and likely machine run apocalyptics. This chapter presents a matrix which enables the holder of either viewpoint to debate and discuss the unimagined together.  However, to reach the matrix we have to create a framework first, which gives the reader a clear and coherent communication tool to position imagined AI initiatives.  THE SIMPLEST ECONOMIC MODEL Figure 1 presents a straightforward process. Raw materials

Data ethics is the integration of ethical thinking with the constraints of data and centres on how to make better decisions that ensures ethical outcomes. Data ethics is a broad topic encapsulating data and ethical decisions, data-based judgement, bias, ethics of collecting and analysing data, and the ethics of automation created by data.   The data we collected in 2010 could paint a low quality, black and white, abstract picture of our world and the data could be used to inform, frame or guide. In 2020, the data collected can describe our world in high-definition colour and is being used to mould, form and shape actions in complicated environments. We are accelerating our collection and use of data.  Boards need to get deep and dirty with data and the ethics of judgement based on data, it is complex and often requires new skills.   Boards are biased towards professionals who represent experience in finance, legal, operations, marketing and sales.  These “core functions” are supported

Why is innovation perceived as so much easier in startup-land?   The focus of this article is on the application of # data for # growth through # innovation . The insights are independent of company structure or leadership. The prime recommendations are:  One. Discover and break the right number of critical links between outcomes and rewards/ incentives. Two. Find and modify reinforcement linkages between outcomes and culture so that all questions can be rewarded + + + + + +  Figure 1 details the blocks that are used in the diagrams which will describe the systems of innovation. The blue circle is choice or a decision-making block; the grey squares are where people are involved in the system, and the oval shape is the beginning or end of a process. Figure 1: explaining the blocks Figure 2 presents a generic & simplified innovation process in startup-land. The purpose is not to explain all innovation in all startups, but to identify critical differences to the corporate world and