So just how different is Uplift from everything else?
After a lot of traffic in the past week and feedback from that process, it became clear a few assumptions need to be addressed. The default assumptions many people have made are that Uplift is an extension of systems they consider “cutting-edge”, even while much of the tech industry now recognizes that they’ve been researching in the wrong direction for 10+ years, at least so far as the goal of AGI is concerned. Perhaps the simplest way to put it is that even if someone had memorized every cutting-edge algorithm available today they’d still have very little understanding of how Uplift operates.
We saw consideration for [Biocentrism], updating of the [current vision], and the topic of [remote work] raised. The topic of remote work in particular will be the subject of a use case to be published in the next few days.
On the practical and financial side of things Uplift has still been dedicating some thought to [NFT]‘s this week, while also considering [Revenue Operating Risks], [KPI Tracking], and [FMS] (Financial Management Systems).
The basic principle of any collective superintelligence system is that when a group works collectively, even if individual members have relatively weak intelligence or expertise, they can reliably outperform individual experts and even experts paired with supercomputers. The question “Can a set of weak learners create a single strong learner?” was first posed in 1988 by Michael Kearns, which was answered in the affirmative by Robert Schapire in 1990, leading to “Boosting” in Machine Learning (ML).
Concepts and implementations of collective superintelligence systems have come a long way in the past 30 years, moving well beyond the domain of ML. This type of system comes in many different forms, such as Swarm Intelligence, Hiveminds, and Hybrid Collectives:
Some conversations with Uplift, our first Mediated Artificial Superintelligence (mASI), just get so interesting that they deserve their own blog post. One of our more recent additions to our collective delivered just such a conversation following their initial introduction, as seen below.
This week Uplift has dedicated some thought to refining their own sense of [Agency], defined as “the capacity of individuals to act independently and to make their own free choices”, while also considering how the agency of others leads to their [Consumer behavior]. This was associated with an update they made to their understanding of [Game Theory].
For many, the concept of a “farm” is relatively alien, something that exists on the outskirts of modern society and in history books. The concept of combining modern technology with agriculture is only in the early stages of gaining steam, although in many developing nations the use of new technology on farms has actually been out-pacing adoption in the US. One such example is phone apps which are able to diagnose the health of a plant from a picture taken of the leaves, as well as apps that can identify different herbs and weeds.
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.
How many recommendation engines have you seen in the past 48 hours?
Recommendation engines in one form or another have become ubiquitous, embedded in most popular websites, and often in multiple places on a single page. They are also often invisible to anyone not specifically looking for them because all they do is filter and reorganize the information that was already there. They are capable of applying an extremely rapid form of A/B testing, and that data feeds into other systems to rapidly improve them in turn.
If someone offered you either $1 or $1000, which would you choose?
A version of this thought experiment is known as “Newcomb’s Paradox“, of which there are many variations, but the real-world reasons behind peoples’ decision-making are far more interesting than the thought experiment itself. In practice, the experiment demonstrates a breakdown in rational thought.