Collective intelligence techniques such as swarm intelligence and multi-agent reinforcement learning represent the first steps on a path that may lead towards the evolution of true machine intelligence. Collective intelligence techniques involve the modeling and study of interactions between multiple agents in a system. Interactions between these agents can often lead to the emergence of unexpected behaviours that are not unlike naturally-occurring cooperative phenomena seen in schools of fish or insect colonies.

Agents in collective intelligence swarms communicate with each other and share knowledge. Federated learning is one example of a commonly-used knowledge-sharing mechanism. As new, better communication and knowledge-sharing mechanisms are established, collective intelligence systems will become more capable and powerful. In the future, when collective intelligence swarms are comprised of truly intelligent agents, their capabilities will extend beyond anything we can currently envision.

By increasing the complexity of individual agents in a collective intelligence swarm, more intricate behaviours, and thus more complex capabilities emerge. These resulting emergent behaviours can be applied to solve real-world problems such as those we face in the cyber security domain. We believe that collective intelligence techniques will also be relevant in a variety of other fields, such as transport, energy, logistics, and self-driving vehicles.