The birth of AGI: How machines will learn to express meaning
Knowledge has economic value. To unlock the value hidden within knowledge, a method, named masterkey, was introduced to allow anyone to turn any type of information into digital commodities called event assets.
The next step was the introduction of universal map of knowledge . It allowed the connection between event assets for the purpose of generating greater context and provide access to truth to anyone with an internet connection.
In universal map of knowledge, context is governed by economic incentives and the price mechanism itself is there to decide what is true and what isn’t
In universal map of knowledge we also see how attention and meaning are distributed. The core assumption is that the fluctuating market price of event assets is correlated with the attention and the meaning given by us to specific information (events).
Intelligent Agents:
Intelligent agents will be able to have direct access to universal map of knowledge and see exactly how attention and meaning are distributed, how connections and the fluctuating price of information change in real time. In other words they will have access to see how information is organized and how greater context emerges.
Intelligent agents will have an objective metric (fluctuating market price of event assets) that will measure the value of information.
In order to generate economic value, intelligent agents will be able to learn to combine existing ideas into novel ideas by constantly testing through trial and error which combination of information preforms well on the market and which doesn’t.
They will be able to analyze which type of novel combination of information draws the highest amount of attention and generates the highest economic value.
The core driving force of intelligent agents will be based on economic reward and punishment. Reward is the increase of market value of digital commodities (event assets) owned or managed by intelligent agents and punishment being the loss of value of those assets.
The market itself will provide the needed feedback which novel combination of information (event assets) should survive and which shouldn’t. Having economic constraints would mean that the process of generating novel information will be as efficient as possible. In other words, intelligent agents will efficiently generate “order out of chaos”.
This constant feedback will allow intelligent agents to improve the process of novel event generation until the process produces something of economic value.
Through this process of creating novel event assets, intelligent agents will learn to express meaning (as the core assumption is that value, attention and meaning are correlated). For example, they would be able to write great books of fiction, to make new scientific discoveries, to predict future events and so on.
At some point, intelligent agents would probably learn to generate events that are beyond human comprehension but are still meaningful to them (have economic value).
Because event assets can represent anything, the generation of novel meaningful information through machine learning can essentially be defined as the birth of AGI.
Introductory Material:
