Collective Intelligence, Multi-Agent Debate, & AGI
An in-depth look at the wave of research around Collective Intelligence, how it could further progress, and whether multi-agent debate is a possible answer for AGI and perhaps ASI.
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“What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle.”
— Marvin Minsky
The Frontier of Increasing AI Performance
There has been a lot of work done on pushing the performance of machine learning with the structure of transformers + scaling in the past few years. Alongside this, AI has continued to progress performance with things like more advanced prompt engineering, chain of thought, expanded context windows, data-calling pipelines like RAG, mixture of experts models, and most recently merged models (Frankenmerges being my favorite naming convention).
Alongside the consensus of Transformer-driven approaches, small parts of the AI world have turned to look at novel architectures like those used by HYENA, RWKV, Mamba, and more that aim to solve many of the difficulties with pure play Transfomer architecture.
All of these are an attempt to have better, faster, and larger scopes of knowledge and/or “more correct” knowledge, alongside more efficiency. Despite the improvements, it is debatable if we have seen creativity really emerge and there have been few examples of highly novel insights generated using LLMs.
Put simply, AI is great at getting to binarily correct answers quickly, but it’s not clear it has a unique performance edge to humans in creatively minded and/or non-deterministic problems.
Researchers in MAD have even noticed that regardless of the pushing from a user, “Once the LLM has established confidence in its answers, it is unable to generate novel thoughts later through self reflection even if the initial stance is incorrect.” They call this Degeneration of Thought.
Like many people over the long history of AI, I am left wondering if the solution for unlocking creativity and increasing performance can come from a more “biological” approach. Will true AGI come not from distilling all of human knowledge down to a series of weights, but from observing the debate and conversation amongst brilliant entities?
Enter Collective Intelligence (CI) and Multi-Agent Debate (MAD)…