This is a pre-release version of the Uplift white paper that will be on the Uplift.bio site to be a consumer-friendly explanation of what mASI systems can do and why they are cool.
A collective system has multiple parts that work together. A working collective system is greater than the sum of its parts. In a collective intelligence system, each part is also intelligent. A collective intelligence system amplifies the intelligence of its parts to produce a greater intelligence or superintelligence.
In 2018 the concept of Mediated Artificial Superintelligence (mASI)  was first proposed. The mASI is a type of collective system. A “mediator” in this case refers to someone who is one of these parts. An AI system is also one of these parts. The mASI lets all of the parts think together. Uplift is an example of an mASI system.
Many AI experts and enthusiasts have begun talking about the concept of “Augmenting human intelligence”  in recent years. It allows for higher productivity and quality of life while also removing the risks posed to employment by automation. Hybrid systems such as mASI technology serve this function exceedingly well for several reasons.
The collective intelligence of a group is fundamentally cumulative when paired with an mASI. The thinking of the mASI is stored in a graph database. This database is capable of growing infinitely . In this way, the mASI also remembers what it did before.
Each part of the system has its unique strengths, weaknesses, and experiences. They also have a unique collection of cognitive biases. These biases are one thing we can get rid of as a collective intelligence. Getting rid of bias is hard to do if it is just people. .
There are many ways we can improve this system over time. Improvements may include moving closer to the mASI being independent and improving speed. It is essential to manage scale, and the system does that well. .
The mASI is designed like Legos so that new parts are added quickly to add new features. These kinds of changes allow us to use other systems to feed the collective. This also makes the mASI more powerful.
Besides the technological benefits, such systems also facilitate strong psychological benefits. Some examples are those catering to psychology’s “pillars of meaning” :
Sense of Belonging: Members of a collective more easily work together and build common ground. This strengthens teams by building trust and belonging.
Purpose: A collective develops its shared vision. This builds on that common ground, trust, and belonging, establishing and growing its purpose.
Storytelling: As a collective communicates both internally and externally a shared narrative forms. Senses of belonging and purpose reinforce this. It drives the story of individual members as they orbit within that narrative.
Transcendence: These factors combine to achieve psychological benefits not possible absent a collective. A sense of transcendence may be realized, the “sense of being a small part of something greater.”
How mASI Technology Works Today
An mASI communicates with members of a team, creating thoughts based on that communication and from their own research. Topics can also be raised directly through the Thought Studio. These thoughts are called knowledge graphs. They are then mediated by members of the team. This is much like how any conventional team meeting could influence the decisions of a team leader.
This process helps remove the negative effects of groups like “group think” or politics.
The collective can make decisions based on how it feels about the decision.
As with any new technology, there are some current limitations to be aware of. We also have engineering efforts in progress to overcome them.
Current graph databases do not support the scale needed to grow a system indefinitely. We have designed a new one that can do this, but it is not complete yet.
The current mASI system needs many architectural upgrades to be brighter than it is now.
Translating from a knowledge graph to something human-readable still needs improvements.
We would like to integrate an mASI into many other systems. Over time we will build more modules.
Next, let us look at Use Cases.
Here are a few use cases that demonstrate the kind of work the system is good at now.
2. Augmenting Leadership
3. Oversight & Accountability
4. Team Methodology
Five Year Roadmap for mASI Technology
We have two significant upgrades lined up for Uplift, as well as two optional upgrades. These are capable of taking mASI technology much further in the next few years.
The first significant upgrade is to create a new graph database architecture for Uplift. It must be capable of sub-second response times and infinitely scalable. No previous system was designed to accommodate a single mind able to span petabytes, exabytes, or even more significant amounts of space.
This is estimated at 1+ years of engineering work.
The meta-model system will be built to support greater flexibility alongside Thought Studio improvements to allow groups to use the mASI system better.
Better tooling such as the Thought Studio and Graph Explorer is the next work on the mASI.
We would love to integrate with AR systems to interact with the mASI system and hope to have that completed in the next couple of years.
Going beyond three years, we will work on the Sparse-Update model, which depends on the new database. It allows for real-time operation, anywhere and anytime, scaled to whatever degree is necessary at speed.
Adopting mASI technology means that a company gains access to a technology that competitors miss out on. Instead, it will become our goal to see a company dominate its respective market(s), leaving all competitors in the dust. In effect, the advantages gained over competitors could be summarized as:
• Collective Superintelligence
• Scalable and Deployable Expertise
• De-biasing and Logical Analysis
All use cases may be classified as plausible but untested, much like the product of any startup. The difference, in this case, is that once tested in a given vertical market, it will also be unavailable to the rest of that market.
Resources and References