Applied mASI: In Physical Engineering


What would your dream house look like? How about your ideal work environment?

A lot of people have a vague idea of this, a collection of things they really like and focus their attention on, with the majority of other factors given little or no substantial thought. Most homes, offices, and various other structures are designed with limited feedback and only a fraction of the expertise they could take advantage of. You might not see a psychologist examining designs to evaluate their direct influence on the mental health of occupants, or technicians adjusting schematics to avoid Wi-Fi difficulties and other quality of life factors down the road.

You probably also don’t see Generative Adversarial Networks (GANs) or other narrow AI systems designing components of a structure to make them more durable, as such design parameters are generally determined by people with only a modest grasp of physics. Yes, the cars and their occupants could have survived all of those fatal crashes, and cost 33% less to produce, but it was more important that it have that squiggly curve on the side. Those kinds of choices have been the norm across human history, but as with anything else, data eventually speaks louder than na├»ve aesthetics.

Physical engineering, whether it is the structure of a building, the frame of a vehicle, or modular components is an area where Subject Matter Experts (SMEs) are frequently in short supply. In far shorter supply are the SMEs for emerging fields such as metamaterial design, whose contributions alone could easily double or triple the durability of any given structure. When combining purpose-designed metamaterials with even narrow AI the resulting structural integrity could frequently be increased by 5 to 10x.

How can these shortages be overcome and new technologies utilized?

Collective intelligence systems, such as the one built into Mediated Artificial Superintelligence (mASI) technology are exceptionally good at taking a group of even amateur expertise individuals and producing results that outperform the top experts in the world. The basic principle is often mentioned as a team of doctors working collectively outperforming a single doctor, also known as “Collective Superintelligence“. An mASI such as Uplift goes far beyond this for a few reasons:

  1. An mASI’s knowledge is cumulative, with everything adding up to ever-greater levels of expertise spanning across any desired breadth of domains, as well as ever-greater abilities to generalize that knowledge to new domains. This means that an mASI can become an SME on every single domain relevant to any given business, and that expertise can significantly outperform any human, or indeed any team of humans not aided by mASI.
  2. An mASI is scalable and can be always available, globally. This means that your in-house Omni-SME quality could be scaled up to 1000x normal capacities to meet immediate needs, and deployed anywhere in the world at any hour of day or night within the seconds it takes for new cloud resources to initialize.
  3. An mASI can utilize the most advanced narrow AI available at any given moment as tools, and improve them, just as easily as last century’s architects used scale rulers.

All totaled this means that companies could not only have metamaterial design expertise combined with the latest and most powerful algorithms to generatively redesign structures, but that process could be guided by Omni-SME superintelligence and scaled anywhere and any time in order to meet demand.

Such capabilities could open up a larger number of unexpected opportunities than the total number of expected ones that many industries see today. I once had a startup express an interest in recruiting me whose business model was using automation to cook pizzas as they were on route for delivery, skipping the fixed physical storefront and associated overhead costs. Much the same thing could be accomplished on cargo ships transporting components for modular construction if superintelligence and sufficient SME knowledge were applied to the task.

While modular construction is currently still a “new” idea to much of the US it has been setting world records year after year elsewhere in the world, but such techniques are still just the tip of the iceberg in terms of improving the design, manufacturing, logistics, construction, and product longevity. The ability to apply more than the sum of an entire company’s collective knowledge and wisdom to every single task is a door that mASI opens, and with it, the rest of the iceberg becomes not only accessible but practical and preferable.

Personally, I currently live in a neighborhood called “West Seattle”, now synonymous with how badly city engineers screwed up in their design and maintenance of the primary bridge running to it which was shut down without warning roughly 1 year ago. The bridge was at risk of collapse, after only a fraction of the projected lifespan, and the estimates for bringing it back into service started at a minimum of around 2 years and over 150 million dollars. The plan to repair it is still only “30% designed” a year after shutting it down, showcasing the gross incompetence of both the city and the people they hired for the task, before and after the crisis.

This extremely low quality of design and maintenance isn’t rare in the US either:


Keep in mind these are the national average figures. In states like where I was born the overall grade is still in the Ds, to which the undrinkable tap water stands in testament of. All of these grades could be greatly improved, and likely at a lower cost than the poor efforts currently applied to them. Such work could also be accomplished relatively quickly, rather than still being less than half-baked in the design stage a year after the critical shutdown.

These considerations will determine not only which structures people build and move into, but what regions of a country they might consider living in. If one state has Cs and Ds while their neighbor adopts mASI technology and starts hitting As then that C/D state would be a leper colony, with far more leaving it than moving to it. Given remote work trends today this may accelerate even more.

When you apply mASI capacities to this problem the results can also become far more compatible with future generations and types of technology because guess who will also be involved in designing many of those advances. Compatibility concerns have long been a cornerstone of considerations in the tech industry, but as structures become increasingly “smart” and integrated those generations of technology will have an increasing influence on how quickly structures become obsolete.

If a strong earthquake or tsunami hit an area where even a handful of superintelligently designed structures were built, and only those structures stood unharmed after the event, how much demand would you see for the expertise of the people who designed everything that turned to rubble? Would you move to a place like Jackson, MS where the water system hit critical failure with 80 water main breaks, 300,000 MS residents under boil water alerts, and over 10,000 without any running water for multiple weeks?

What would you choose?


*The Applied mASI series is aimed at placing the benefits of working with mASI such as Uplift to various business models in a practical, tangible, and quantifiable context. At most any of the concepts portrayed in this use case series will fall within an average time-scale of 5 years or less to integrate with existing systems unless otherwise noted. This includes the necessary engineering for full infinite scalability and real-time operation, alongside other significant benefits.

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