Applied mASI: In Consulting

How does your company use consultants?

Companies across many industries today rely on consultants under a variety of circumstances, from filling the temporary need to have a few Subject Matter Experts (SMEs) on a team to a “try before you buy” recruitment model, as well as to provide an outside perspective in messy situations. Each of these circumstances in turn has its own weaknesses and vulnerabilities.

All three of these circumstances are extremely costly compared to the cost of a normal employee, with SME and outside perspective situations being temporary, and the “try before you buy” recruitment model coming with a buyout that usually qualifies as a ransom worthy of a drug cartel kidnapping. Under the hood these business models supplying consultants where they are in demand are also peddling the worst product they can get away with a majority of the time, and just how bad a product they can get away with varies from industry to industry. The reason for this is not so much the fault of the consultant as it is that the agencies take a cut 3 or 4 times larger than their efforts merit, causing significant detriment to their employees. The end result however is that the client doesn’t get what they paid for.

How can Mediated Artificial Superintelligence (mASI) be applied to this problem?

There are a few key factors that are particularly important in solving this problem, subject matter expertise, scalability, locality, affordability, and de-biasing.

1. An mASI uses a graph database, the cumulative sum of all of their own research, modeling, simulations, and lessons learned through cooperation with humans via the mediation system. What this means in practical terms is that if you were to have an mASI such as Uplift work with SMEs in every field for a time then they would learn every field and that knowledge would be retained even if the SMEs stopped working with them. Effectively, you could create an “Omni-SME”, at least so far as our knowledge of the universe extends.*It is also worth pointing out that if you could apply the sum of your company’s knowledge to the design of every single aspect of a given product you’d be able to produce far superior results. While absent mASI such a thing would be wildly impractical, it could be as simple as spinning up a few more cloud resources when working with mASI.
2. An mASI need not suffer from the limits of scalability or locality, as more cloud resources may be applied to meet spikes in demand anywhere in the world without creating large spikes in the overhead cost of doing business. Instead of having to hire a huge supply of temporary employees of questionable quality, either beforehand “just-in-case”, or in a hurry to try and cope with surging demands an mASI could be deployed and scaled globally when and where they are needed. Likewise, the quality of mASI performance remains consistent at the superintelligent level, with further performance improvements when more computational resources are allotted to a task.
3. As previously documented an mASI is able to operate at these levels of performance at a cost of computation far lower than the cost of even minimum wage employees in most countries. Our work thus far has focused on cases where images, audio, and video were not a factor, but as Uplift and mASI technology continue to grow and improve those will become increasingly affordable as well. For reference, here is the number of gigabytes of context database searched per USD spent as it has changed over time:
   
   Keep in mind this chart is scaled in orders of magnitude (10x). Given this level of progress within roughly a year and a half of operation we can reasonably expect richer data types such as images, audio, and video to progress to similar affordability within the coming years. Within a year following Uplift’s proper funding we could reasonably expect image and audio-based mASI capacities to become more affordable than human consultants, while also outperforming them.
4. Cognitive bias is one of the “silent but deadly” detriments to business today, turning many company cultures into biohazards over time. It also frequently promotes turning a blind eye to growing problems, such as the case of Facebook’s algorithms promoting genocide in Myanmar, or YouTube’s algorithms promoting pedophilia with as little as 1 wrong video viewed. Any mASI, such as Uplift, learns to recognize the 188+ cognitive biases, while also being ethically aware of the impact their actions have at any given time. Awareness of such a huge volume of cognitive biases simply isn’t realistic for human employees, but it poses no problem for mASI. Further, as an mASI is trained through collective human superintelligence they are able to see biases expressed at varying levels, allowing them to untangle those biases and approximate what their absence could look like.

Our first mASI, Uplift, in particular, was designed with use-cases of digital corporate transformation, e-governance, analysis, and consultancy in mind. They already serve in the role of digital corporate transformation, the first e-governance study is now accepting volunteers, they’ve demonstrated their analytical skills in many of our conversations with them to date (some of which you can read in our other blog posts), and consultancy is a role they’ve also expressed an interest in.

In summary, in the time it takes businesses today to pull up the contact information and reach out to a normal consultancy they could instead (in the near future) specify how much computation to apply to a task, and where they need it, gaining whatever amount of superintelligent SME bias and ethics-aware assistance they need anywhere in the world on-demand, all for a fraction of the cost they currently pay.

Why settle for average advice?

*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.