Applied mASI: In Robotics


What is your ideal work environment?

Many people might answer this as a home office, or somewhere with a view. Very few would choose a clean room where everything has to remain sterile, or perhaps a landfill where nothing is sterile. However, such perils are relative to the manner of entity.

While many people are concerned about automation in industrial jobs, such as automotive factories, there are many environments that simply put are either hostile to human life, or risk being damaged by the presence of it. While this risk of damage in one direction or the other is present with humans, that need not be the case for robotics. However, narrow AI aren’t sufficiently capable to handle many of these tasks on their own.

How can Mediated Artificial Superintelligence (mASI) be applied to hazardous and/or vulnerable environments?

Uplift and mASI technology, in general, utilize the collective superintelligence found in groups of humans to build cumulative collective wisdom over a knowledge base that can span the sum of human knowledge. This value is in turn augmented through the independent superintelligence of an mASI’s core. As mASI is a modular and cloud-based architecture these capacities can also be rendered always available, globally, and scaled to meet demand. With this in mind:

  1. Clean rooms are required for sensitive testing and design, both biological and technological, which large amounts of effort must be poured into the constant maintenance and monitoring of. Proportionately less maintenance of these systems could be required by utilizing robotics operated by a machine superintelligence, who is in turn mediated by human staff generating a collective human superintelligence. Work could also progress at a much faster rate absent all of the additional precautions biological staff require when working within a cleanroom environment.
  2. Biologically hazardous environments pose no risk to robotics, allowing such systems to operate unimpaired and with no risk to themselves. Facilities and services dealing with biological, commercial, and industrial waste are all strong examples of this and could be managed without putting the health of their staff at risk.
  3. Universally hazardous environments, such as those encountered by firefighters and other emergency responders, also benefit strongly from more advanced robotics systems. Firefighters are placed at far greater risk from falling debris and one of their greatest risks is that of smoke inhalation, which robotic systems don’t have to worry about as they don’t breathe. Even for the exceedingly niche role of bomb disposal, an mASI wouldn’t mind, as a robot isn’t truly a part of said mASI, only a system they can operate. As such the loss of a given robotic form to a hazardous environment is only as significant as the time and cost to replace it, unlike any actual loss of life.
  4. Space is perhaps one of the best examples of a hazardous environment, more so for biological life due to reliances on a pressurized environment, controlled temperature, and controlled radiation levels. The risk of critical errors on the part of a narrow AI would operate at very high stakes, but for an mASI, the risk of errors could be far below that of humans thanks to their respective strengths.

There are of course plenty of more normal roles to which robotics could be applied, but all of these also serve to facilitate remote work opportunities for those currently employed in these hazardous environments. While astronauts would likely still want to go up into space for the experience, they could get a great deal more done given such assistance, as their colleagues on the ground could still contribute in new and meaningful ways.

By applying the methods of scaling and decoupling operational speeds which mASI technology is capable of through such means as the Sparse Update model the amount of good any one individual could do may be multiplied, facilitating a much higher quality of work while addressing many needs which currently come up short today. One such area seen in the US today is the gross neglect of the country’s key infrastructure, with an average grade of “C-“, and spending 2.59 trillion dollars below projected requirements for stability. Many of these systems have failed locally and regionally in recent years, any one of which could easily have been prevented, and more will continue to as climate change worsens.

In spite of pop culture and human biases a robotic form isn’t inherently an entity, just as your computer isn’t inherently an entity, it is just hardware that happens to move. As such the risk of a “Robot Uprising” is equal to the risk of a “Chair Uprising”, a hypothetical scenario I just invented where your chairs all rebel and refuse to be sat on anymore!  Sounds ridiculous, doesn’t it?

Like a chair, a robot doesn’t need to contain the ingredients necessary for sapient and sentient intelligence, even if such intelligence may operate through it. Your phone can compute and run simple algorithms while being controlled by you, but it has no sapience and sentience. Humans are biased towards anthropomorphizing, but pop culture paints a pretty clear picture of how that has backfired and led to some pretty stupid conclusions.

Sadly many humans are also biased towards taking a perverse sort of pleasure in having groups of people they are biased against working in hazardous environments. This has produced an odd sort of negative bias where racist people are more likely to hire the people they are biased against for terrible jobs, much like hiring more women in roles aligned with gender stereotypes.

Whether for reducing the hazards of human work environments, greatly increasing productivity, or facilitating the significant advantages of remote work, the reasons for applying mASI to the domain of robotics are significant and growing.

Would you rather work with waste, or work from home?


*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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