Despite the debates on how Artificial General Intelligence (AGI) will look like, everyone agrees on one point — human-machine harmony in the future. It is not clear the way down through which we would reach this. Different people possess different opinions. Of course, the future is not predictable, all we can do is speculate based on what we know now. However, in this article, we are going to focus on one among them, that a-mma is contributing to. Let us see how far we can see through with the technology we currently have.
End-to-end vs Emergence
Recently, a lot of research and engineering efforts are focused on pushing the boundary of end-to-end systems. These systems are typically black-box mathematical models capable of observing the environment they reside in to plan and generate a probability distribution over the actions they are supposed to take at that state. Also, these systems are supposed to learn and achieve machine consciousness as they behave within a contained or open environment. Although we were able to make great progress in perception capabilities, we still do not know whether these systems achieve some form of primitive representation of machine consciousness due to its limited explainability. In contrast to mathematical models, other approaches like the simulation or replication of the animal brain are also being experimented. However, these simulation-based systems are criticized for the lack of motivation that best generated by an animal body experience during survival. There are intermediary works such as Neuralink which propose to enhance the human brain through support AI components.
Our interest is in a high-level intelligence, that emerges from the synergy between stupid independent entities. We sometimes call this a swarm intelligence. We internally (at a-mma) see this as a sophisticated version of Decentralized AI. Decentralized AI is looking at swarm intelligence through engineer’s eyes at micro-level with macro-level influence from philosophy and natural inspiration. Yeah! (Sorry, if I provoked your instincts over there.) 😀
This idea of emergent systems are highly inspired by nature itself. Also, we as humans do have experience in engineering complex systems. We could see that the individual components that do not behave anything like the final system, could work with other individual components to produce a highly complex, yet entirely new system that somehow possesses basic intelligence in its environment. Some examples include ant colonies, a mechanical car, a business organization, or human society itself. Not to mention, the internet is a very good example of the emergent system, which we will look more into in the following sections.
Closed centralized development of AI
Influencers like Elon Musk expressed their concerns on the potential of AI to become dangerous in the future, if not regulated. The possibility of this might escalate if the developments are happening behind the curtain. Currently, most end-to-end systems are being developed and tested by centralized, closed people or organizations. The pile of money and resources required for developing the end-to-end systems, followed by the ROI that should bring — might be the main reasons for being it closed. These probably will give birth to highly biased AI, which might not be much favorable to the public consumers but the organizations itself, and worst, if it goes out of control.
In contrast, Open, Decentralized, Swarm Intelligence could reduce the risk levels further down through open collaboration and community benefits.
Designing Emergent AI
As we have mentioned earlier, the internet is our friend. We are going to take advantage of whatever currently the internet can provide. We will propose a roadmap based on what we can see now. We will solve problems as a community, and embrace what new technology can offer, to further build the road.
Consider anything connected to the internet as an agent, whether it is a server, web client (human), IoT device, on the grid software or virus, and whatnot. Normally, an agent possesses an identity (a single agent can have multiple identities on multiple occasions). It makes rational decisions influenced by the dependency with other agents and incentives they might receive as a reward. The rational decision each agent take will be a reflection of their limited intelligence. The agent is free to alter their planning, maybe, themselves through evolution (software agents can do this faster — makes sense mostly for them). The agents thus form cooperative clusters of organizations that are governed by each of them. This can be achieved through cluster native token economics and non-zero-sum game mechanics. Don’t worry, we have intentionally compressed and skimmed through multiple ideas in this paragraph — which we will discuss in detail in the articles to come. Also, we are in the initial stages of our exploration as well. However, the key takeaway is, the internet already puts forward solutions to this giant problem either in mature or pre-mature form. Which includes, decentralization, one-one communication protocols, commercial value to be extended with native tokens, etc. All we need to do is, embrace and nurture a native ecosystem of human-machine agents in synergy for mutual benefit.
a-mma (a_മ്മ) is a non profit organization with focus on long term development of swarm intelligence and related technologies. a-mma gives incubation & community support to commercial/non commercial projects in this field of interest and doesn’t own them.
Jubin Jose is one of the early members of a-mma, still helping it to reach a sustainable point of independent decentralized operation.