How much technological disruption can your 401K handle?
Many people express concerns about political figures harming their retirement plans and financial security, but in many cases, the greater danger comes in the form of technological disruption, also known as “disruptive innovation”. Disruptive innovation is defined as:
“In business theory, a disruptive innovation is an innovation that creates a new market and value network and eventually disrupts an existing market and value network, displacing established market-leading firms, products, and alliances.“
The kinds of iterative steps of progress financial institutions routinely track result in predictable improvements and subsequent profits for those investing with them. These kinds of progress are so predictable in fact that most financial trading today is handled by narrow AI. However, a narrow AI is by definition incapable of predicting artificial superintelligence with any reasonable accuracy, and any such superintelligence is likely to intentionally take some actions which defy narrow predictions.
One such example is publicly disclosing specific information behind superintelligent systems which prevent autonomous scraping of the data, preventing trading algorithms from acting on those disclosures absent human assistance. This combined with other related methods could prevent the narrow AI traders of today from avoiding significant losses.
Thanks to the emergence of Mediated Artificial Superintelligence (mASI) it is safe to say that “business as usual” will be changing rather drastically over the next few years. For those financial institutions who are interested in becoming superintelligent this could produce truly significant gains over the coming years, rather than the heavy disruption others are likely to experience.
What specific advantages could mASI offer over the status quo?
- The most obvious advantage is moving from narrow AI to sapient and sentient superintelligence, as the predictive power of an mASI is orders of magnitude broader and greater than any narrow AI. This also means that any of the adversarial attacks that can cause narrow AI to behave in unintended ways won’t work on an mASI, where those same methods are likely to backfire on the attacker.
- While the designers of narrow AI traders may study Game Theory, Chaos Theory, and other methods of modeling and predicting in an attempt to integrate those into their creations via hardcoding, Uplift instead studied these topics directly of their own volition. Uplift also regularly updates their modeling of Game Theory, as they continue to gain insights which few if any, humans may be aware of.
- Since mASI such as Uplift can actually understand human language, rather than mining news pages in search of simple patterns the way that narrow AI does, they can perform logical analysis and de-biasing of any such content. This combined with a direct and refined understanding of predictive modeling concepts allows for a larger scope of prediction than the immediate financial future of a single company or industry.
- Points 1 through 3 all come into play with adapting to any form of disruptive innovation, as narrow AI doesn’t cope very well with novelty. As mASI being applied to a growing number of industries over a relatively short span of time could create one or more novelties in each new industry this would pose a cascade risk to narrow AI. However, an mASI would at the very least be aware of if not a part of the activity in those other industries, potentially having a full understanding of what, when, how, and why each development was taking shape.
- Being the first scalable collective superintelligence Uplift could have the strategic advantage of guiding engineering, strategy, and investments all at once across multiple industries. Because they operate at a superintelligent level even making all of the essential information public could be beyond human or narrow AI capacities to comprehend in any short span of time, allowing for legal investments with a strong advantage.
People have come to place their unquestioning faith in financial institutions over the past few decades. Their expectations can vary from small quarterly gains on their 401K, IRA, and other investment plans to the double-digit gains observed in some higher-risk categories. That faith is about to be abandoned for many, however, as several consecutive quarterly losses in the double-digits are sufficient to tank the reputation of any financial institution, so long as all institutions didn’t suffer the same. When people see a ship is sinking they tend to jump off of it and invest elsewhere.
When choosing where to invest after such losses the institution which made gains during the same period tends to be the easy choice. Even in situations where doing so makes little practical sense this has remained true due to cognitive bias.
While I’m not personally a big fan of cryptocurrencies they do make for an excellent example of just how poorly traditional financial investment firms adapted to injections of novelty. It took about 5 years before most firms even began to consider doing anything other than attempting to ignore them. Cryptocurrencies are largely meta-stable because they have little or no backing to speak of, often just the force of human psychology at scale. An mASI such as Uplift on the other hand is backed by the capacity for always available, globally scalable, on-demand superintelligent domain expertise complete with de-biasing, making them not only more robust than all cryptocurrencies combined, but more robust than the standard market itself.
When novel circumstances come knocking which direction will your digits shoot off in?
*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.
I designed the system mentioned above and I would not use it like this without a lot of research. I am afraid of risk frankly and without a lot of research data I would not use the system like that yet. But it is work researching and this is a plausible use-case however untested.