Can generative AI help build a global hive mind?

In the world of science fiction, the notion that humanity will one day connect our brains together into a global “hive mind” dates back to a 1930 novel entitled “Last and First Men” by Olaf Stapledon. It recounts a fictional “future history” in which humanity evolves biologically into a species that can link telepathically and form collective minds of extreme intelligence.

In the real world this pursuit is called Collective Superintelligence, and it will not require telepathy or other fictional devices. Instead, it will use emerging generative AI technologies to connect large human groups into real-time deliberative systems, enabling us to solve difficult problems by harnessing our combined knowledge, wisdom and insight in powerful new ways.

This pursuit has been my personal focus as an AI researcher for the last decade and I believe it has the potential to produce superintelligent systems that maintain human values, morals and interests at the core of every insight, assessment or decision. Of course, for many people the idea of large human groups thinking together in real-time systems seems unnatural, or even creepy, but mother nature would disagree.

In fact, many social species have evolved naturally in this direction, developing the ability to make rapid decisions in large groups that greatly exceed the brainpower of individual members. Biologists call this Swarm Intelligence, and it enables schools of fish, swarms of bees and flocks of birds to quickly solve life-and-death problems at intelligence levels that far exceed the mental capacity of their individual minds.

One remarkable form of Swarm Intelligence that inspired my own academic research over the last decade is the humble fish school. Although it looks simple on the surface, the underlying dynamics enable thousands of members to make complex decisions with no individual in charge. In fact, fish schools can make good decisions even when no single member has sufficient information to address the problem at hand. Consider the image below: 

The fish school above faces a hypothetical problem of extreme importance: Three predators approach from three different directions. At the moment shown, no individual is aware of all three threats. In fact, most are not aware of any threats. There is a small group of fish in the upper left that is aware of the first predator, a small group in the lower left that is aware of the second predator, and a small group in the upper right that is aware of the third predator. Most of the other fish are unaware of the dangers.

So, how can this large group, in which every member has limited information (and no members possess all the information) solve this life and death problem and quickly move in the right direction? The first thing to know is that fish have a special organ on the sides of their body called the lateral line that allows them to track the speed and direction of neighboring fish based on pressure and vibrations in the water around them. Using this organ, they can perceive the intent of their neighbors (that is, the speed and direction that those fish believe the school should go). This communication is bidirectional so we can think of it as a local deliberation in which small groups decide on the motion of their small portion of the school.

This is interesting, but it does not explain how global decisions are made. After all, the group on the right that sees a predator approaching, likely decides the school should move left. At the same time, the two groups on the left likely decide the school should move right. And, groups in the middle, which have no knowledge of the predators, are likely to keep moving in the direction they were already going. So, how does this get resolved into a single rapid decision that evades the urgent threats?

The magic happens because every fish in the school “deliberates” with a different group of nearby neighbors. This means there are many “overlapping conversations” happening at once which enables information to quickly propagate across the full school. As shown below, the result is a swift and decisive collective solution to the problem.

In this way, schooling fish can make rapid and effective decisions across large populations, even when all members have limited information. Such a skill would be even more powerful for large human groups.  After all, the problems faced by groups of people are significantly more complex and involve far more perspectives. This begs the question: Could large human groups deliberate in real-time with the efficiency of fish schools and quickly reach optimized decisions?

For years this goal seemed impossible. That’s because human conversations have been shown to be most productive in small groups of four to seven people and quickly degrade as groups grow larger. This is because the “airtime per person” gets progressively squeezed and the wait-time to respond to others steadily increases. By 12 to 15 people, the conversational dynamics change from thoughtful debate to a series of monologues that become increasingly disjointed. By 20 people, the dialog ceases to be a conversation at all. This problem seemed impenetrable until advances in generative AI opened up new solutions.

The resulting technology is called Conversational Swarm Intelligence (CSI) and it promises to allow groups of potentially any size (200, 2000 or even 2 million people) to discuss complex problems in real-time and quickly converge on solutions with significantly amplified intelligence. The first step is to divide the population into small subgroups, each sized for thoughtful dialog. For example, a 1,000-person group could be divided into 200 subgroups of five, each routed into their own chat room or video conferencing session. Of course, this does not create a single unified conversation — it creates 200 parallel conversations.   

As described above, fish schools solve this problem by having local groups overlap, allowing information to quickly propagate across the full population. Unfortunately, we humans did not evolve with the ability to be in multiple conversations at once. In fact, if we try to pay attention to two conversations, we immediately get confused and can’t focus on either. This is commonly called the “cocktail party problem” because it happens often when small groups gather within earshot of each other. If you try to pay attention to a neighboring conversation you immediately lose track of the discussion you are in. 

So how can we overcome this human limitation?

CSI technology solves this problem by inserting LLM-powered “conversational surrogates” into each subgroup. These AI agents are tasked with distilling the real-time human insights within its assigned group and sharing those insights with surrogate agents in one or more other groups. The receiving agents express the received insights in their own groups as natural first-person dialog. In this way, each subgroup is given an artificial member that participates seamlessly in overlapping conversations, ensuring information freely propagates across the full population. 

A variety of recent studies suggest the approach is effective. For example, a 2023 study conducted at Carnegie Mellon University to compare real-time deliberations among approximately 50 people in traditional chatrooms versus conversational swarms. When using the CSI structure, groups were able to hold more coherent conversations that quickly converged on solutions. In addition, each individual was found to contribute 50% more content (on average) than participants using traditional methods.

But does this amplify group intelligence?

To explore this, a follow-up 2024 study by researchers at Carnegie Mellon and Unanimous AI tested the ability of networked human groups to take IQ tests as a real-time “hive mind.” Results showed that groups of 35 people who averaged an IQ of 100 (the 50th percentile), could score an effective IQ of 128 (the 97th percentile) when using an online CSI platform called Thinkscape. Although this study used conversational groups of only 35 participants, other recent studies have tested groups up to 250 with success.

While the above studies used text conversations, the core methods of CSI can be deployed for teleconferencing, videoconferencing or even VR meetings, enabling large groups of hundreds or even thousands of members to hold coherent real-time conversations that efficiently solve problems, prioritize options, brainstorm ideas and reach decisions, all with amplified group intelligence. This has the potential to enhance a wide range of fields from enterprise collaboration and market research to civic engagement and deliberative democracy. 

In the longer term, this approach could be used to build superintelligent systems that are inherently aligned with human values, morals, wisdom and sensibilities. In theory, we could use CSI technology to enable millions of individuals around the world to “think together” as a global brain-of-brains to solve our most difficult problems. For me, this is a safer path than relying on a purely artificial superintelligence, as AI systems may not maintain human values or interests over time. That’s why I believe we need technologies like Conversational Swarm Intelligence and tools like Thinkscape that leverage gen AI — but keep humans in the loop. 

Louis Rosenberg is a longtime researcher in the fields of AI, collective intelligence and mixed reality. He is CEO and chief scientist of Unanimous AI.