Collective Artificial Intelligence
... a sub-topic of it that is more specifically focusing on the collective intelligence of human-machine systems is quite interesting!
Where does the name “aggregate intellect” come from anyway?
5 years ago when I was incorporating my company, I was looking for a fitting name. I didn’t know what my company would do exactly, but I knew that it would have something to do with “collective intelligence” and AI given that it was being born out of the AI community I had built.
Credit should go to my partner for the name because I was stuck finding a name that abbreviates to a.i. AND means “collective intelligence”; and she came up with “aggregate intellect”. It is a mouthful, but I immediately liked it, it had everything I wanted!
Now reflecting on the past 5 years, have we gotten closer to the vision I had?
Some people recently started talking about “intelligence 3.0” as the superintelligent machines that surpass human capabilities. That’s a fairly wishy washy topic for me given where we are and how things are going. But a sub-topic of it that is more specifically focusing on the collective intelligence of human-machine systems is quite interesting!
That is one of the areas that we are very deeply interested in and keenly focused on. Our research is exploring several interesting ideas in that space and we are ramping up to do some interesting experiments with our academic collaborators at McGill University and University of Toronto. The gist of the idea is, what are the design parameters of systems that combine a mixture of human and machine intelligence and use the latter to facilitate effective collaboration and problem solving between all the agents involved.
Some of the interesting concepts that exist in the space are:
Expert in the loop intelligence: the essential question here is creating AI systems, as point solutions, that collaborate with humans by taking care of the mundane tasks and delegating all the important decisions to the right human expert for robust and successful execution. We have been building rudimentary versions of this in the past and we are starting to see more sophisticated systems using LLMs as the linguistic interface with humans. Delegation often is based on thresholding the confidence of the system in handling a task.
Multi-agent LLM systems: the main question here is, what if we had an array of expert models each of which are particularly good at one task, say time series forecasting, physical simulation, image analysis and generation, and linguistic tasks, and they used LLMs’ ability to communicate via code and data, to coordinate and manage task execution and communication of observations. A system like this, could take in an objective and start breaking it down, delegating to the right experts, and going through observations, refinement of tasks based on observations, and iterations until the objective is achieved. Most of the existing multi-agent LLM projects are currently focused on primarily LLM based agents and exclude humans in their execution loop.
Mixture of experts (MoEs): This is a slightly more demanding approach and it’s rumored to be the architecture of GPT4. MoEs consist of a gating (routing?) network and a range of expert models that are all trained together. Effectively, through training, the network learns how to delegate subtasks to the right expert models, and how to combine the outputs of those for the execution of the primary task. This is the next natural step beyond what we loved in more traditional ML, aka ensemble methods, except that in the MOE case, not all expert models execute the incoming task, and a more sophisticated routing is learned.
I think the right solution is a combination of all 3 (and potentially other approaches like reinforcement learning). It is unsafe and honestly unlikely to build systems that completely exclude human experts, so the expert-in-the-loop aspect is very important. More interestingly, going beyond the interaction of one expert with the system and indeed augmenting how humans collaborate with each other and then with the machine could be quite interesting. With multi-agent systems, we can go beyond just point solutions and mundane tasks and slowly handle more complex scenarios. That will be a huge workflow boost. Using natural / formal language as an agent communication tool is definitely attractive, and important for explainability reasons. But that is most probably very limiting for many scenarios especially when machine agents are communicating within each other. That’s where MoEs would come in.
The final product would be a system that includes humans as operators and quality gate keepers, multi-agent LLM systems as workflow handlers where explainability is necessary, and MoEs where most efficient task handling is the priority.
