Louis Rosenberg

Are we destined to be out-played by A.I.?

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Imagine a flying saucer lands in Time Square and an alien steps out. He’s a competitive fellow, so he arrives armed with a board game in hand – the game of Go. He walks up the first person he passes and says the classic line, “Take me to your best player.”

We humans are competitive too, so a tournament is quickly arranged. The alien is surprisingly
confident. What we don’t appreciate is that he’s spent years studying how humans play Go, analyzing
replays of every major match between top players.

It feels like Humanity is being set up for a humiliating defeat. After all, the alien is deeply prepared to play humans, while we had no opportunity to get ready for playing aliens. We could lose – badly.

And that’s exactly what happened earlier this year when an alien intelligence named AlphaGo played the human Go master, Lee Sedol. The alien beat the human soundly, prevailing in 4 out of 5 games.

Was this Alpha Go victory a milestone in A.I. research? Absolutely, but not because it proved an A.I. can be built that is highly skilled at playing the game of Go. No, this victory proved that an A.I. can be
built that is highly skilled at playing the game of humans.

After all, AlphaGo didn’t learn to play by studying the rules and thinking up a clever strategy. No, it learned by studying how people play, processing thousands upon thousands of matches to characterize how masters make moves, and react to moves, and what mistakes they’re likely to make.

All told, the system trained by reviewing 30 million moves by expert players. Thus, AlphaGo is not a system designed to optimize play an abstract game. No, it’s a system optimized to beat humans by
studying us inside and out, learning to predict what actions we’ll take, what reactions we’ll have, and
what errors we’ll most frequently stumble into.

Simply put, the A.I. did not learn to play Go – it learned to play us. According to published reports, AlphaGo was so well trained, it is able to correctly predict a human’s Go move 57% of the time. Imagine if you could correctly predict what a person would do 57% percent of the time – while negotiating a
business deal, or selling a product, or pushing a political agenda. Someone with that predictive ability
could use it to build an empire of political or economic power.

To me, this is terrifying. Not because computers can beat us at board games, but because from this
moment forward, we will always be at a disadvantage, facing the arrival of alien intelligences that are
better prepared to play us than we are to play them. Whether these aliens are named AlphaGo or
Alpha-Finance or Alpha-Geopolitical-Conflict, they will beat us at our own games. This suggests a future
where we humans can be manipulated by intelligent systems that can easily predict our tendencies,
inclinations, and biases, quickly finding our weaknesses and exploiting them.

To those who say we can put controls in place to keep A.I. from becoming dangerous, I say these technologies are already dangerous. After all, we have already faced a strategic opponent that
understands us better than we understand it. The only thing left is to apply these technologies to
disciplines that are more significant than games. This will occur, which means we are destined to be
out-matched, and not just in the game of Go, but overall game of life. So what can we do?

My view is that we humans need to stay one step ahead in the intelligence arms-race. There are many technologies being explored by researchers around the world that make us smarter, ranging from gene therapy to boost our minds, to implanted chips that augment our brains. Personally I prefer less
invasive methods. If we look to Mother Nature, countless other species have faced challenges during
their evolutionary development where survival required a boost in intelligence beyond the abilities of
their individual brains. Those species developed method to amplify their intelligence by “thinking
together” – forming systems that tap their combined knowledge, experience, insights, and instincts.

Yes, I’m talking about the dreaded “hive mind” and for a long time I was deeply against it. But over the last few years I’ve come to realize that pooling our intellectual resources in closed-loop systems may be our best approach to keeping humanity ahead of purely artificial intelligences. After all, a hive mind
is comprised of living, breathing, people, instilled with human values and emotions, and is motivated to
keep human interests at the forefront. A purely artificial intelligence has no reason to share our core
values or make decisions that support our interests.

Said another way, if we build a purely digital A.I., we need to view it as an alien intelligence. And like any alien that arrives on planet earth, we have to assume it could be a threat. But, if we build superintelligence as a “hive mind” of networked people – it’s not an alien intellect, but an evolution of human thinking, following the same developmental path as countless other species in the natural world.

Again, for a long time I was against the “hive mind” paradigm, fearing it would change what it means to be human. But at this point, I believe change is our only way to stay ahead of the alien intelligences that are quickly heading towards us. No, they’re not flying through space at light speed – if they were, we’d be preparing ourselves for their arrival. Instead, they’re coming towards us in a far more insidious way, emerging from research labs around the world. But still, we need to prepare.

We also need to be more vigilant about the near-term dangers of A.I. systems. Long before we see a machine intelligence that rivals human intellect in a general sense, A.I. technologies will become
remarkably good at charactering and predicting human behavior. If a machine can out-play a Go master
by predicting his actions and reactions, it won’t be long before intelligent systems can out-play us in all
aspects of life. To prevent this from happening, our best defense may be a strong offense – a largescale
effort to amplify human intelligence, keeping us competitive for as long as we can.

 

About the Author:

Louis RosenbergLouis Rosenberg received B.S., M.S. and Ph.D. degrees from Stanford University. His doctoral work produced the Virtual Fixtures platform for the U.S. Air Force, the first immersive Augmented Reality system created. Rosenberg then founded Immersion Corporation (Nasdaq: IMMR), a virtual reality company focused on advanced interfaces. More recently, he founded Unanimous A.I. an artificial intelligence company focused on harnessing collective intelligence by enabling online human swarms.

 

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Super-Intelligence and the virtues of a “Hive Mind”

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We humans pride ourselves on being rational thinkers with an inherent sense of morality that guides our actions towards the greater good.  These virtues hold true across all levels of society and yet collectively, on a global scale, we often make self-destructive decisions.  I’m talking about the kind of decisions that lead to war, pollution, poverty, inequality, and in recent years, climate change.

This begs the question, how can immoral decisions emerge from a society comprised overwhelmingly of moral individuals?  Philosophers have been pondering this for ages.  Nietzsche lamented – “Madness is rare in individuals – but in groups, political parties, nations, and eras it’s the rule.”   Renowned American theologian, Reinhold Niebuhr was even more blunt, expressing –“the group is more arrogant, hypocritical, self-centered, and more ruthless in the pursuit of its ends than the individual.” So, what is it about human groups that cause us to behave so differently together than we would behave alone?

Social scientists often cite the “Tragedy of the Commons” problem when pondering group morality. First postulated by the Victorian economist William Foster Lloyd in 1833, the premise is that individuals, who act both morally and rationally on a local scale, are prone to producing immoral results on a group scale.  He pointed to herdsman running cattle on open pastures.  As an individual rancher, it’s entirely rational and moral to maximize the size of your herd.  But, if all herdsman follow this individual morality, the shared pasture gets overrun and is ruined for all.  Thus individual morality is not always aligned with the common good. In fact, it may be misaligned more often than not.

Ecologist Garett Hardin brought this to modern relevance in a 1968 when he linked this to population growth in the journal Science.  He pointed out that on a local level, it’s a basic Human Right for parents to decide the number of children to have.  And for much of the world’s inhabitants, a large brood is fully rational, optimizing survival of the family.  On a global level, however, if all families behave under that same local morality, overpopulation will likely result, putting most families in danger.

So, how do we better handle social dilemmas in which the short-term interests of individuals are at odds with the long-term interests of the group? To date, the most successful path has been the use of democratic governance in which groups make decisions collectively, through direct or representative polling of the population. The presumption is that by revealing the consequences of their collective actions to the full group, democratic decisions will emerge that support the common good. The problem is, our current methods for polling groups often fall victim to the “Tragedy of the Commons” pitfall.

A clever example of this was recently performed at University of Maryland by Dylan Selterman.  He posed an extra–credit challenge to his Social Psychology class, allowing each of his students to indicate by secret ballot how many points of extra credit they wanted on their exam – 2 points or 6 points.  The only twist was that if more than 10% of the class asked for 6 points, nobody would get any bonus.  Clearly, it was in the best interest for everyone in the class to individually ask for 2 points, but that’s not what happened. Far too many students asked for 6 points and nobody received extra credit.

So, are we humans doomed to make self-destructive global decisions because of something flawed within us?  Or is the Tragedy of the Commons problem a consequence not of our nature, but of our methods for group decision-making?  An optimist, my view is that our tendency for self-destructive decisions is not because of a fundamental human flaw, but because our modern decision-making process is broken – the way we mediate opposing interests, weigh competing alternatives, and converge on final outcomes.  The fact is, our current methods are highly influenced by special interests, the more extreme the position the more attention given, thereby producing solutions that not optimal for the common good.

And the problem is getting worse, for we’ve become a “poll obsessed” society, overusing a crude tool meant for quantifying groups, while forgetting that polls do little to encourage consensus or help groups reach smart decisions that support the common good.  Much the opposite, polls usually are polarizing, highlighting the differences in a population, while encouraging special-interests to entrench.  This is why rational and moral individuals are often unable to agree on solutions that are best for the population at large, even in a democracy that aims to achieve this. Instead, we either stagnate with no decision being reached, or we polarize, entrenching around positions that go against the group’s long-term self-interest.

So, is there a way to encourage rational and moral group decisions?  We could look to Mother Nature for guidance. Countless species have evolved methods for quickly reaching group decisions based on input from large populations of diverse individuals. From schools of fish and flocks of birds, to colonies of ants and swarms of bees, nature achieves this feat, not by taking votes or polls, but by enabling groups to form real-time dynamics systems that negotiate in synchrony and converge on optimal outcomes.

Biologists call the phenomenon Swarm Intelligence. It’s the way nature has learned to tap into the diverse knowledge, intuition, experiences, and instincts of groups and produce decisions that are better for the common good than could be produced by any single individual.  In fact, research has shown that swarming amplifies the intelligence of the species, resulting in “super-organisms” that can solve problems and make decisions that are beyond the capacity of the individual members.

Bee_Swarm
Fig 1 “Swarm of Honeybees”

The most deeply researched swarms in nature are those formed by honeybees.  As studied in detail by Tomas Seeley at Cornell University, the decision-making process of honeybee swarms has been shown to remarkably similar to that performed by neurological brains.  Both employ large populations of simple units (i.e., bees and neurons) that work in parallel to integrate noisy evidence, weigh competing alternatives, and converge on decisions in synchrony.  In this sense, Honeybees and other organisms that swarm are able make decisions by forming a “brain of brains” that is far more intelligent than any individual contributor.

For example, honeybees face a life-or-death decision when selecting a location for a new colony.  After searching a 30 square mile area, scout bees bring information about dozens of potential sites back to the swarm for consideration. Each site is assessed across many competing criteria, including – safety from predators, insulation for winter, ventilation for summer, and storage capacity for honey.  Using body vibrations known as a “waggle dance”, the scout bees form a real-time swarm where they express preferences for various sites based on the many quality factors. Through dynamic negotiation among the competing signals, a decision is reached. The amazing thing is that honeybees, as studied by Seeley, converge on the optimal decision 80% of the time.  They don’t get mired in stagnation and indecision.  They don’t get hijacked by special interest groups.  Instead, they pool their diverse knowledge and preferences, and through the natural process of swarming, efficiently reach an optimized decision that is best for the survival of the group as a whole.

So what’s so terrible about a “hive mind?”  I suspect the negative connotations are primarily a consequence of deep misconceptions about bees.  Many people assume that bees are “drones” that take blind direction from an all-powerful queen.  This is simply incorrect.  The queen has no influence on colony decisions.  Instead, honeybees make decisions by convening a swarm of 300 to 500 of their most experienced scouts, who negotiate in real-time, weighing the alternatives in a democratic and thoughtful manner.  In many ways, their process is less “drone-like” than elections held by us humans wherein most participants reflectively vote along party lines, the decisions being made by a small percentage of independents in the middle.  The fact is, bees negotiate and compromise while we polarize and entrench.  I would argue that nature’s “hive minds” are more enlightened than we humans appreciate.

Hive Mind in Action
FIG 2. Swarm of Networked Users

This begs the question, can humans swarm? And if so, can we achieve similar benefits?  The answer to both question appears to be yes.  My personal experience with swarms has been at the Silicon Valley startup Unanimous A.I., which has been developing technologies that enable online human swarming.  Their recently published studies have shown that swarming allows groups to make predictions and craft estimates that are more accurate than those achieved by polls, votes, surveys, and traditional forms of group decision making.  For example, swarms of networked users have been shown to make accurate predictions about the outcome of sporting events, the price of commodities, and the winners of awards like the Oscars.  But amplified intelligence is only one reason why Mother Nature evolved the process of Swarm Intelligence. The other reason is enabling groups to converge on decisions that support the common good.

So, can we humans use swarming to make decisions that better reflect our common values? And more importantly, can swarming help human groups avoid the Tragedy of the Commons pitfall?  Early testing suggests the answer is yes.  A recent study by Unanimous A.I. compared the decisions made by networked groups, first as disconnected individuals, then as a unified swarm.  The study was modeled as a traditional “Tragedy of the Commons” dilemma in which subjects are asked to choose a cash bonus, the awarding of which is dependent upon the behavior of the full group. The test engaged 18 randomly selected online users, each paid $1.00 for their participation.  All were told they would get an added bonus of $0.30 or $0.90 – they simply had to indicate on a blind survey which bonus they wanted.  Of course there was a catch: if more than 25% of the group asked for $0.90, then nobody would get anything.  This means oversubscription of the $0.90 option would defeat their common interests. And that’s exactly what happened – a whopping 67% of the group asked for a $0.90 bonus on the survey, well beyond the 25% threshold.  Thus, nobody received a bonus, the group failing to achieve their common interest.

The test was repeated by forming a real-time swarm, rather than taking a survey.  This was done using an online platform called UNU.  The interface allows networked groups to answer questions as a real-time human swarm, collectively exploring a decision-space and converging on a preferred solution.

Because people can’t waggle dance like bees, the UNU platform, was designed to provide a humanfriendly interface that enables the same type of synchronous feedback loops.  It works by allowing networked users to collaboratively move a graphical puck to select an answer, each person controlling their own small magnet to influence the direction and speed of the overall system.  With everyone pulling in real-time, adapting hundreds of times per minute, a unified system emerges that reflects the collective will of the swarm. In this experiment, any of the users pulling towards $0.30 at the end of the decision would get that bonus, while anyone pulling towards $0.90 get that bonus.  Thus, users were able to pursue their individual interests while helping to guide the overall swarm.

FIG 3. Snapshot of a Swarm in Action
FIG 3. Snapshot of a Swarm in Action us

The results were inspiring: The swarm configured itself such that 24% of the total pull on the puck was towards $0.90, with 70% of the total pull towards $0.30, and 6% abstaining.  Figure 3 shows a snapshot of that swarm in action, each magnet controlled by a unique user as the group worked together to move the puck.  It’s important to stress that the users could only see the puck and their own magnet, but not the full swarm of other magnets.  This means they had no direct indication of how many users were pulling in each direction.  Still, the group, when functioning as a unified system, connected by real-time feedback loops, avoided the Tragedy of the Commons pitfall, instead converging on a solution that was best for group as a whole.

What does Swarm Intelligence mean for our future?  It could point us to new methods for reaching group decisions – methods that encourage groups to combine their individual knowledge, opinions, and interests in support of the common good, avoiding entrenchment and stagnation.  Further, human swarming could enable groups to reach decisions that better reflect our core morals and values, even in large groups where collective moralities often falter.  And while many people still refer to the “hive mind” in a pejorative sense, I am now convinced this stems from misconceptions about how natural swarms work.  The fact is, swarming is Mother Nature’s brand of democracy, resulting from millions of years of evolution, and driven by a single selective motivator – to enable groups to work together for the good of the population as a whole.

Looking further out, online human swarms may be a path to super-intelligent systems. After all, a single honeybee lacks the intellectual capacity to even consider a complex problem like selecting a new home site for a colony, and yet swarms of bees have been shown to not only solve that multi-faceted problem, but find optimal solutions.  If we humans could form similar swarms, we may be able to achieve similar boosts in intellect, solving problems that we currently, as individuals, can’t even conceive.  This is not only an exciting way to build smart systems, it’s a path that keeps humans in the loop – ensuring that any super-intelligence that emerges has our core values and interests at its core.

 

SOURCES:

Cohen, Tara R., et al. “Group Morality and Intergroup Relations”. Cross-Cultural and Experimental Evidence Personality and Social Psychology Bulletin, Vol. 32, No. 11. (Nov 2006), pp. 1559-1572

Seeley, Thomas D., et al. “Stop signals provide cross inhibition in collective decision-making by honeybee swarms.” Science 335.6064 (2012): 108-111.

K.m. Passino, T.F. Seeley, P.K. Visscher, Swarm Cognition in honeybees, Behav. Ecol. Sociobiol. 62, 401 (2008).

J.A.R. Marchall, R. Bogacz, A. Dornhaus, R. Planque, T.Kovacs, N.R. Franks, On optimal decision making in brains and social insect colonies,

J.R. Soc Interface 6,1065 (2009).

I.D. Couzin, Collective Cognition in Animal Groups, Trends Cogn. Sci. 13,36 (2008).

Seeley, Thomas D., Visscher, P. Kirk. Choosing a home: How the scouts in a honey bee swarm perceive the completion of their group decision making. Behavioral Ecology and Sociobiology 54 (5) 511-520.

Rosenberg, L.B, “Human Swarms, a real-time paradigm for collective intelligence.” Collective Intelligence 2015, Santa Clara CA.

Rosenberg, L.B., “Human Swarms and collective intelligence.” – European Conference on Artificial Life 2015, pp. 658-659

Rosenberg, L.B., “Human Swarming, a real-time method for Parallel Distributed Intelligence.” – IEEE, 2015 Swarm/Human Blended Intelligence, Cleveland OH, USA.

https://commons.wikimedia.org/wiki/File:Bee_Swarm.JPG 

 

 

About the Author:

Louis RosenbergLouis Rosenberg received B.S., M.S. and Ph.D. degrees from Stanford University. His doctoral work produced the Virtual Fixtures platform for the U.S. Air Force, the first immersive Augmented Reality system created. Rosenberg then founded Immersion Corporation (Nasdaq: IMMR), a virtual reality company focused on advanced interfaces. More recently, he founded Unanimous A.I. an artificial intelligence company focused on harnessing collective intelligence by enabling online human swarms.

 

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Human Swarming and the future of Collective Intelligence

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SwarmIt all goes back to the birds and the bees.  The fish too.  Even slime-molds.  Really, it goes to all social creatures that amplify their collective intelligence by forming real-time synchronous systems. We have many names for these natural assemblages, including flocks, schools, shoals, blooms, colonies, herds, and swarms. Whatever we call them, one thing is clear – millions of years of evolution produced these highly coordinated behaviors because of the survival benefits they provide to a great many species.  In this way, nature had demonstrated that social creatures, by functioning together in closed-loop systems, can outperform the vast majority of individual members when solving problems and making decisions, thereby boosting overall survival of their population.1,2

For convenience I use the word “swarm” to refer to cohesive groupings of individual members, all working together as a unified dynamic system, their collective behavior tightly coordinated by real-time feedback loops. Unlike discordant groups (i.e. crowds), swarms behave as unique entities, operating as a coherent unit that displays emergent intelligence, even emergent personality. With that definition in mind, the big question that has propelled my explorations over the last few years is simply this: “Can humans swarm?”

Certainly humans didn’t evolve the ability to swarm, for we lack the innate connections that other species use to establish feedback-loops among individual members. Ants use chemical traces. Fish detect vibrations in the water around them.  Bees use high speed gestures.  Birds detect motions propagating through the flock.  Whatever method is used for establishing the interstitial connectivity, the resulting swarms possess capabilities as a group that the individuals alone can’t match.  For example, high speed feedback-control among flapping birds enables thousands of starlings to make precision hairpin turns in winds gusting to 40 miles per hour.  It’s simply remarkable.

But what about humans?  We don’t possess the natural ability to form real-time swarms, but can we design technologies that fill in the missing pieces, using artificial means to form the critical interstitial connections? And if so, will swarming allow us humans to achieve the same types of intelligence amplification that other species have attained via synchrony?  If we consider the leap in intelligence between an individual ant and a full ant colony working as one, can we expect the same level of amplification as we go from single individual humans to an elevated ‘hyper-mind’ that emerges from real-time human swarming?

I founded Unanimous AI with these questions in mind.  Now 18 months into platform development, I feel confident in answering a few of the basics. Yes, humans can swarm.  And yes, new technology is the key. Humans can swarm only if we develop technologies that fill in missing the pieces evolution hasn’t yet provided.  More specifically, swarming can occur among groups of online users by “closing the loop” around populations of networked individuals such that they behave as a real-time synchronous system.  This is what we’ve been working on at Unanimous and the results are very exciting.

I should point out, I’m not talking about simple “crowd sourcing” that employs votes or polls or markets to sequentially aggregate input from large numbers of individuals. Those methods have great value when it comes to characterizing populations, but they don’t allow a population to express itself as a coherent unified entity. Polls and votes and markets reveal the average sentiments within groups, identifying central trends, but that’s not the same thing as allowing a group to think as one, the parties negotiating in real-time until the group converges on solutions that optimizes support and satisfaction.

The fact is, polls are polarizing, highlighting the differences in populations without doing anything to help groups find common ground. Swarms on the other hand, are unifying, enabling groups to find the commonality among them and converge on solutions that optimize support.  Said another way, polls promote entrenchment, driving groups apart, while swarms foster collaboration, pulling groups together.  And it’s the “together” aspect of swarms that enables a group to unleash its emergent, collective intelligence.

This is why I believe online tools for groups need a major overhaul, moving away asynchronous polling and voting, to synchronous real-time systems – human swarms that can attack a problem together. I know this sounds like a jump in technical complexity, and it is, but it is also a return to our roots. After all, millions of years of evolution suggest that swarms are better suited for unleashing group intelligence.

Petri dish with mold colonies isolated on whiteConsider the humble Slime Mold – It’s a single celled organism that congregates by the millions to form a super-cell that behaves as a single unified entity.  And although each individual cell is very simple, the unified swarm can successfully forage woodlands for rotting vegetation.  In fact, slime molds have been shown by researchers at Hokkaido University in Japan to be able to navigate mazes, finding the shortest and most efficient route between two points of food.3,4  In other words, as a unified swarm these very simple cells display collective intelligence that exceeded any of the individuals. This brings us back to people.  Can we see similar benefits?

This question was purely theoretical until last year when we started testing UNU™, our real-time platform for human swarming. As an online environment that closes the loop around networked users, UNU is the computational glue that allows people to work together in swarms that can answer questions, make decisions, generate ideas, even express opinions.  And thus far, the results have been fascinating, suggesting that enhanced intelligence through human swarming is a very real possibility.

When using the UNU platform, swarms of online users can answer questions and make decisions by collaboratively moving a graphical puck to select from a set of possible answers. The puck is generated by a central server and modeled as a real-world physical system with a defined mass, damping and friction. Each participant in the swarm connects to the server and is provided a controllable graphical magnet that allows the user to freely apply force vectors on the puck in real time (Fig. 1). The puck moves in response to swarm’s influence, not based on the input of any individual participant, but based on a dynamic feedback loop that is closed around all swarm members. In this way, real-time synchronous control is enabled across a swarm of distributed networked users.

SWARM 2

Figure 1: a human swarm collaborates in synchrony

Through the collaborative control of the graphical puck, a real-time physical negotiation emerges among the networked members. This occurs because all of the participating users are able to push and pull on the puck at the same time, collectively exploring the decision-space and converging upon the most agreeable answers. But do the answers have value?

To test the value of human swarms, researchers at Unanimous A.I. enlisted groups of novice users and asked them perform a number verifiable intellectual tasks. For example, these groups were asked to make predictions about the winners of the NFL playoffs, the Golden Globes, the Super Bowl, the 2015 Oscars, the Stanley Cup, the NBA finals, and most recently the Women’s World Cup. In all cases, the predictions made by swarms were more accurate than the predictions made by the individuals who comprised the swarms.  In fact, the swarms consistently performed better than even the most skilled individuals in each group. The swarm also exceeded the tally of “votes” given by the groups, trumping the traditional methods of characterizing populations.  In short, initial testing suggests that human swarms do more than reveal the “wisdom of the crowd” – they can unlock the collective intelligence of populations.

For example, when predicting the 2015 Academy Awards, we asked 48 individuals to predict the top 15 award categories. Using the most popular predictions to represent “the wisdom of the crowd”, the group collectively achieved 6 correct predictions for the top 15 award categories (40% success). This was our baseline dataset, the low success rate reflecting the fact that this group of users had no special knowledge about movies. To test swarming, we then selected a sub-group of the full population and asked them to make the same predictions, but now as a unified swarm. The sub-group were typical performers on the written poll, ensuring equity. Never-the-less, when working as a unified swarm, the group achieved 11 correct predictions for the top 15 award categories (73% success). We believe this is a very promising result and speaks to the potential for harnessing the wisdom of social groups through real-time swarming.

Below is video that shows a Social Swarm in action.  It’s comprised of 52 people, networked together from around the world, working together as a synchronous dynamic system. They were asked a question that’s on many people’s minds in the US these days, “Who will be the Republican nominee for president?” What’s interesting about political questions like this is that often inspires conflict and indecision, and yet when working as a swarm, these 52 people came up with a solid prediction in only 22 seconds.

Just like biological swarms, these artificial swarms have the capacity to outperform the individuals who comprise it.  And that makes good sense, for the benefits of swarming are as natural as, well… the birds and bees.  Even more exciting is that swarm intelligence offers humanity a way to build enhanced intelligences without replacing ourselves with bits and bytes. In fact, I view human swarming as a safer form of A.I., for it uses technology to produce emergent intelligence, but does so while keeping people in the loop, ensuring that human sensibilities and moralities are integral to its thought processes.

In fact, I see human swarming as the first “human-in the loop” approach to A.I., for it combines the benefits of computational infrastructure and software efficiencies, with the unique values that humans bring to the table in terms of creativity, empathy, morality, and justice. And because a swarm-based emergent intelligence is rooted in human input, the resulting intelligence, no matter how smart, is highly likely to be aligned with humanity not just in values and morals, but goals and objectives. We can’t say that about a pure A.I., which could easily have goals and objectives that are not aligned at all with our own. And because we can’t prevent researchers from creating pure A.I. technologies that may rival humanity, it’s my hope that human swarming will offer us a way to stay one step ahead of the machines. After all, we have billions of brains, and with tools like UNU to connect us in swarms, we may one day be able to boost our intellectual abilities as a society.

Of course, we must ask another question – will people want to swarm?  After all, just because swarming provides intellectual benefits, that doesn’t mean that large numbers of people will want to collaborate in swarms to answer questions and make decisions.  It was with this uncertainty in mind that we interviewed a great many users to capture their subjective feedback after engaging in real-time swarming experiences.

It turns out, most people who try swarming agree – swarms are fun.  In a study performed recently at California State University with college seniors, 60% rated the experience of swarming as “very fun” on a subjective scale, with no users expressing negative feelings. Of course, we have to ask ourselves a fundamental question – why? What is it about working together in swarms that triggers the feeling of “fun” in users?

Again, it all goes back to evolution – at least, that’s my best guess. When users come together in swarms, they’re engaging in a synchronous activity that makes them feel connected to others in real-time. They become part of something larger than themselves. This affinity for synchrony has deep roots in human development and has been cited as the reason people are inherently drawn to music and dance, as well as team sports. A recent study at UCLA showed even just having groups march together in rhythm gives participants a greater sense of cohesion, group confidence, and enhanced capacity for coordination. This isn’t surprising. Many studies have shown that coordinated activities enhance cooperation and allegiance within groups.5,6,7,8,9

Synchrony gives people an emotional and physiological rush whether they’re jamming in a band or executing a double-play. Similar effects are seen across the animal kingdom, especially in social animals like primates and whales, which use synchronous group behavior as shows of strength, solidarity, and cohesion.10,11,12  And now, with swarming platforms like UNU, the benefits of real-time synchrony are brought into online social environments, connecting people all over the world for collaborative experiences that are naturally satisfying, unifying, and fun.

So if swarming is both productive and fun, users will flock to systems that enable it. Human swarming will transition from an intellectual curiosity to a powerful tool that unleashes group intelligence in a wide variety of fields, applications, and settings.

Fortunately, the swarms are growing quickly.  Just last week, the largest real-time swarm was formed by 88 eager users, all working in unison to answer dozens of questions.  Below is a replay of that swarm, predicting box office results with speed and accuracy.

LINK TO REPLAY:  http://www.unumsays.com/unum_replay/1623

Overall, I am more excited than ever about the promise of human swarming. I see tools like UNU bringing people together in fun new ways, while in the process boosting human intelligence to new levels.  Looking to the future, I see swarming as offering great value on many fronts, from enabling truly social forms of social media where content is not just shared by groups but created by groups working together as an emergent intelligence, to providing humanity a more human-friendly alternative to traditional A.I, for swarming builds new intelligences while keeping humans in the loop.  It all goes back to the old saying, many minds are better than one.  I believe this is true, especially if by pooling our intellectual resources, we humans can stay one step ahead of pure A.I. alternatives.

 

SOURCES:

1. Deneubourg, J.L., Goss, S.Collective Patterns and Decision Making. Ethology, Ecology, & Evolution 1: 295-311, 1989

2. Axelrod R, Hamilton WD (1981) The evolution of cooperation. Science 211:1390–1396.

3. http://www.scientificamerican.com/article/brainless-slime-molds/

4. http://www.itsokaytobesmart.com/post/37228609143/beyond-their-pretty-remarkable-ability-to-think

5. McNeill WH. Keeping together in time: Dance and drill in human history.  Harvard University Press; 1995.

6. Hagen EH, Bryant GA. Music and dance as a coalition signaling system. Human Nature, 14, 21-41.  2003.

7. Marsh KL, Richardson MJ, Schmidt RC. Social connection through joint action and interpersonal coordination. Topics Cog Sci. 1, 320-339.  2009

8. Wiltermuth SS, Heath C. 2009. Synchrony and cooperation. Psychol. Sci. 20, 1-5.

9. Reddish P, Bulbulia J, Fischer R. 2013.  Does synchrony promote general pro-sociality?  Relig, Brain & Behav.  4, 3-19.

10. Cusick JA, Herzing DL. 2014.  The dynamics of aggression: How individual and group factors affect the long-term interspecific aggression between two sympatric species of dolphin.  Ethology. 120 287-303.

11. Senigalia V, de Stephanis R, Verborgh P, Lusseau D.  2012.  The role of syhconized swimming as affiliative and anti-predatory behavior in long-finned pilot whales.  Behav. Processes.  91, 8-14.

12. Fedurek P, Machanda ZP, Schel AM, Slocombe KE. 2013. Pan hoot chorusing and social bonds in male chimpanzees. Anim. Behav. 86, 129-196.

 

 

About the Author: 

Louis RosenbergLouis Rosenberg, PhD did his doctoral work at Stanford University in robotics, virtual reality, and human-computer interaction. A pioneer in the field of Augmented Reality, he developed the Virtual Fixtures system for the U.S. Air Force in the early 1990s.   He then founded of the VR company Immersion Corp (Nasdaq: IMMR).  Currently, Rosenberg is founder of Unanimous A.I. where he pursues his work on human swarming.

 

NOTE:

To become a beta-user of the UNUM platform for human swarming go to:  http://unanimousai.com/unum/