Applied mASI: In HR

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How often do you “Go with your gut” when picking a candidate?

The “gut instincts” which served humanity well for thousands of years still play a heavy role both directly and indirectly in HR today, though the value they offer by and large isn’t what it used to be. These gut instincts are cognitive biases whose purpose is to estimate rather than calculate value. These instincts also vary wildly from one person to another, making some estimates very good, and some exceptionally poor.

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Applied mASI: In Climate Change

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What kind of climate do you prefer?

Whether or not you believe climate change to be a current problem or a theoretical one the topic itself is so vast and hyper-complex that even the world’s leading experts struggle with it. To calculate, or even estimate, all of the major contributing factors which influence the climate of an entire planet is a daunting task covering many disciplines. This task exceeds the knowledge base of any one human, as well as the cognitive bandwidth required to consider all such knowledge even if one person had it all. Yet, these challenges and many more may be overcome.

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Applied mASI: In Automation & Software Development

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Do you fear the big bad wolf of automation?

Automation and software development tend to go hand-in-hand, with one resulting in the other. This has created the growing concern of mass-unemployment resulting from the automation of an ever-increasing number of jobs today, with some such as Bill Gates proposing methods like a “Robot Tax” as a means of covering the added financial burden of various welfare systems.

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Applied mASI: In Ethics and De-biasing

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On a scale of 1 to 10,000, how ethical is your company? How biased is it?

These are trick questions, as without a means of measurement the answers can only be subjective. Bear in mind, “Ethics” as I use the term can be expressed as (Ethics * Bias = Morals). Because of this many companies focus on their own subjective and shifting morals, as no de-biasing is required.

Racism, Sexism, and virtually every other “ism” used to arbitrarily divide any group of people into hierarchical sub-categories is direct cognitive bias in action. Morals are indirect cognitive bias in action, which makes them a more watered-down but also more prolific version of the same. While some companies now have much-needed ethics-focused roles or departments none of these companies have yet produced a means of measuring or optimizing for ethical value, at best they’ve just found new ways to re-inject bias back into the same systems.

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Applied mASI: In Gaming

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What was your most immersive experience in gaming?

Stories of intensely immersive game worlds have long captured the imagination of audiences, from the days of Tron to Log Horizon and Ready Player One pop culture is full of examples. Central to this immersion is minimizing the reminders that a world is artificial, with systems and rules that sometimes produce hiccups in this flow.

Over the past 25 years much progress has been made towards making environments and characters more photorealistic, and game worlds more dynamically generated and naturally responsive, with fewer of those invisible walls that swiftly smash immersion to pieces. Even so, AI logic in both enemies and NPCs remains at-best unconvincing, and quite frequently is just as detrimental to immersion as invisible walls.

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Applied mASI: In Augmenting Leadership

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What qualities do you look for in an ideal leader?

Leaders can be exceptionally intelligent, empathetic, organized, and inspirational, but no one human leader today represents the ideal of all possible positive leadership qualities. No matter how amazing, any one leader is biological and evolved human neurology simply imposes limits on us all. “… all humans make mistakes, and all leaders are but human“, yet we now have the opportunity to augment leadership with collective superintelligence. With the assistance of Mediated Artificial Superintelligence (mASI) systems the employee collective can improve all aspects of leadership, but particularly those which are weakest in individual leaders, through a form of digital corporate transformation. This was in fact one of our first use-cases as a company, and one which we applied to ourselves.

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Applied mASI: In Logistics

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“Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?”

This is known as the “Traveling Salesman Problem” (TSP), one of the class of problems in computational complexity theory which is designated as “NP-Hard”.  Due to this incredible level of complexity the logistics industry relies on narrow AI to produce close approximations rather than attempting to calculate the exact answer. In this problem’s simplest and most popular form many such systems have gotten very good at finding answers that were either exact, or within less than 1% of the exact optimal answer. However, once you step into the real world dozens or even hundreds of additional factors may arise, which leads to much more messy approximations. The subsequent impact of these messy approximations is felt in the transportation of people, products, and produce.

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