The What, Why and How of Machine Learning Algorithms How do you choose the correct ML algorithm for a given application? Machine learning is a field of learning patterns through data without any need for explicitly programming or hand-written rules. Before machine learning became mainstream, programmers wrote rules derived from a function of their domain knowledge, observation of some hand-picked instances, and the business requirement to perform a particular task. But this legacy way of delivering business results suffered some evident constraints. How to Choose the Right Algorithm Once you understand what type of business problems machine learning algorithms are best suited for and what are the broad categories in terms of statistical formulation of the given use case.

First, financial regulators need to ensure that regulatory oversight delivers on the inclusion and intermediation-enhancing benefits of digital finance without compromising traditional regulatory goals such as financial stability, adequate competition, consumer protection and market integrity. Second, there is a pressing need for a system of data governance that allows consumers and business to exercise control over their data through the granting and withholding of consent to the use and transfer of their data. Developing a user-friendly granular consent-based data governance system with low transaction costs is a challenge that, when successfully addressed, will promote the development of virtual banking worldwide. Hong Kong SAR offers one example of an integrated regulatory framework for virtual banks. The licensing and regulatory regime – also applicable to incumbent banks – aims to manage the full spectrum of risks arising from any source, including the ownership structure, without compromising development objectives that often rest on technological innovation.

Framing the Future of Web 3.0: #METAVERSE EDITION Goldman Sachs v/ Norbert Gehrke "In our view, the global Internet is in the middle to late innings of the innovation curve of Web 2.0 (the shift from desktop to mobile computing & from local to cloud storage) and the "leaders" of this wave of the Internet are now firmly established. In framing the next wave of computing (Web 3.0),we see the potential for dramatic shifts in industry structure (decentralized, more local/niche/targeted) that could impact current investor perceptions of platform moat/strength, industry input costs, possible headwinds to monetization driven by personalization and potential for shifting media & commerce trends. One element of Web 3.0 that has recently captured media & investor attention is the "Metaverse" – a term that has taken on many meanings but with common themes around virtual and immersive experiences, online communities and the creator economy. In the report, we examine how the gaming/media landscape has already shown some key elements as to how the Metaverse might evolve and how themes such as decentralized web activity & virtual experiences could become hallmarks of many of the next wave of computing in Web 3.0."

If we want the decision-makers to understand the ethical issues of facial recognition, we need to turn the table:) 'The Flemish Scrollers' is software automatically tagging distracted Belgian politicians when they use their phone on the daily live streams. Every meeting of the Flemish government in Belgium is live-streamed on a YouTube channel. When a Livestream starts the software is searching for phones and tries to identify a distracted politician. This is done with the help of AI and face recognition. For ML/ AI/ Data Science learning materials, please check my previous posts.