We show that temporal logic and combinations of temporal logics and modal logics of knowledge can be effectively represented in ar(cid:173) tificial neural networks. We present a Translation Algorithm from temporal rules to neural networks, and show that the networks compute a fixed-point semantics of the rules. We also apply the translation to the muddy children puzzle, which has been used as a testbed for distributed multi-agent systems. We provide a complete solution to the puzzle with the use of simple neural networks, capa(cid:173) ble of reasoning about time and of knowledge acquisition through inductive learning.

Digital technologies are transforming every aspect of our professional and personal lives. While certainly disruptive, and often the brunt of doomsday futurists, innovations like artificial intelligence (AI) present enormous possibilities to make things work better. They also add some exciting sparkle to traditionally dour (but very important) professions such as financial management. Shiny new image aside, AI is set to transform the financial management sector into a body of strategic, informed, and responsive consultants. There is no doubt that the COVID-19 pandemic has accelerated digital transformation, encouraging many finance professionals to harness the benefits of AI to meet their clients' changing expectations. The well-timed Sage CFO 3.0 research surveyed South African CFOs and other senior financial decision-makers for their views on the role of digital technologies, such as AI, in navigating uncertainty and preparing for the new working world.