A Thousand Cooperative, Self-Organising Robots - This Is How the End of Human Dominance on Earth Will Occur

I suspect I am not the only one who finds a swarm of cooperative, self-organizing robots to be a rather obvious foreshadowing of the end of human dominance on Earth. Welcome to the singularity.

This is an excellent review of the new study published in Science (pay-walled, of course). It comes from National Geographic's Phenomena blog, of which Not Exactly Rocket Science is a part.

A Swarm of a Thousand Cooperative, Self-Organising Robots

by Ed Yong | Not Exactly Rocket Science
August 14, 2014

In a lab at Harvard’s Wyss Institute, the world’s largest swarm of cooperative robots is building a star… out of themselves. There are 1024 of these inch-wide ‘Kilobots’, and they can arrange themselves into different shapes, from a letter to a wrench. They are slow and comically jerky in their movements, but they are also autonomous. Once they’re given a shape, they can recreate it without any further instructions, simply by cooperating with their neighbours and organising themselves.

The Kilobots are the work of Mike Rubenstein, Alejandro Cornejo and Radhika Nagpal, who were inspired by natural swarms, where simple and limited units can cooperate to do great things. Thousands of fire ants can unite into living bridges, rafts and buildings. Billions of unthinking neurons can create the human brain. Trillions of cells can fashion a tree or a tyrannosaur. Scientists have tried to make artificial swarms with similar abilities, but building and programming them is expensive and difficult. Most of these robot herds consist of a few dozen units, and only a few include more than a hundred. The Kilobots smash that record.

They’re still a far cry from the combiner robots of my childhood cartoons: they’re arrange themselves into two-dimensional shapes rather than assembling Voltron-style into actual objects. But they’re already an impressive achievement. “This is not only the largest swarm of robots in the world but also an excellent test bed, allowing us to validate collective algorithms in practice,” says Roderich Gross from the University of Sheffield, who has bought 900 of the robots himself to use in his own experiments.

“This is a staggering work,” adds Iain Couzin, who studies collective animal behaviour at Princeton University. “It offers a vision of the future where robot groups could form structures on demand as, for example, in search-and-rescue in dangerous environments, or even the formation of miniature swarms within the body to detect and treat disease.”

“And I’ll form… the wrench!” Credit: Michael Rubenstein, Harvard University.

To create their legion, the team had to rethink every aspect of a typical robot. “If you have a power switch, it takes four seconds to push that, so it’ll take over an hour to turn on a thousand robots,” says Rubenstein. “Charging them, turning them on, sending them new instructions… everything you do with a thousand robots has to be at the level of all the robots at once.”

They also have to be cheap. Fancy parts might make each bot more powerful, but would turn a swarm into a budget-breaker. Even wheels were out. Instead, the team used simpler vibration motors. If you leave your phone on a table and it vibrates, it will also slide slightly: that’s how the Kilobots move. They have two motors: if either vibrates individually, the robot rotates; if both vibrate, it goes straight.

Well, straight-ish, anyway. The tyranny of cost-efficiency meant that the team had to lose any sensors that might tell the robots their bearings or positions. They can’t tell where they are, or if they’re going straight. But each one can shoot infrared beams to the surface below it, and sense the beams reflecting from its neighbours. By measuring how bright the reflections are, it can calculate its distance from other Kilobots.

This combination of stilted motion and dulled senses meant that each robot costs just $20. It also meant that “the robots were even more limited than we expected,” says Rubenstein. “The way they sense distance is noisy and imprecise. You can tell them to move and they won’t, and they’ll have no idea that they’re not moving.”

Fortunately, they have each other. A stuck Kilobot can’t tell if it’s stuck on its own, but it can communicate with its neighbours. If it thinks it’s moving but the distances from its neighbours change, it can deduce that something is wrong. And if neighbours estimate the distances between them and use the average, they can smooth out individual errors.

Using these principles, the team created a simple program that allows the robots to independently assemble into different shapes using just three behaviours. First, they move by skirting along the edges of a group. Second, they create gradients as a crude way of noting their position in the swarm. (A nominated source robot gets a gradient value of 0. Any adjacent robot that can see it sets its gradient value to 1. Any robot that sees 1 but not 0 sets its gradient to 2, and so on.) Finally, although they have no GPS, they can triangulate their position by talking to their neighbours. As long as the team nominates some robots as seeds, effectively turning them into the zero-point on a invisible graph, the rest of the swarm can then work out where they are.

Every Kilobot runs on the same program. The team only has to give them a shape and nominate four of them as seeds. Once that’s done, the rest slowly pour into the right pattern, in an endearingly life-like way. It takes them around 12 hours, but they do it all without any human intervention. And although the final shapes are always a little warped, that’s life-like too. Fire ants don’t have a Platonic ideal of what a bridge or raft should look like; they just work with their neighbours to get the job done.

Stills from movies showing the Kilobots assembling into a K and a star. 

Credit: Michael Rubenstein, Harvard University.

Scientists have long been able to simulate huge swarms of life-like virtual particles in computers, using very simple rules. But the real world is full of pesky physics, inconvenient noise, and temperamental circuitry. Stuff goes wrong. By building an actual swarm, the team can address these problems and make their programs more robust. They’ve already had to deal with a litany of failed motors, stalled robots, collisions, and traffic jams. “The more times you run it, the more likely some random thing will show up that you don’t expect,” says Rubenstein. “That’s the problem with 1,000 robots: even rare things can happen very frequently.”

The next step will be to build robots that actually self-assemble by attaching to each other, says Marco Dorigo from the Free University of Brussels.. “We did so with tens of robots,” he says. “It will not be easy with one thousand.” Rubenstein agrees: “Physical connection is always difficult. If you have a dock, you tend to design the rest of the robot around that dock. It has a huge impact.”

Eventually, he also wants to get to a position where the robots can sense their environment and react accordingly, rather than just slide into some pre-determined shape. Like fire ants, when they get to a body of water, they wouldn’t have to be fed the image of a bridge; they would just self-assemble into one. “That’s a whole other level of intelligence, and it’s not really understood how to do that in robotics,” says Rubenstein. “But nature does it well.”


Reference:
Rubenstein, Cornejo, and Nagpal. (2014, Aug 15). Programmable self-assembly in a thousand-robot swarm. Science; 345(6198):795-799.  http://dx.doi.org/10.1126/science.1254295

More on robots:

• Robots in disguise: soft-bodied walking machine can camouflage itself
• Robots evolve to deceive one another
• Cockroaches and geckos disappear by swinging under ledges… and inspire robots
• Enter the nano-spiders – independent walking robots made of DNA
• A Bird-Like Flock of Autonomous Drones
• Robo-starfish learns about itself and adapts to injuries

Paul Fusella - Dynamic Systems Theory in Cognitive Science: Major Elements, Applications, and Debates Surrounding a Revolutionary Meta-Theory

Last week I posted an article that summarized some of the progress in dynamic systems theory and cognitive science over the last 20 years. This article takes up that same topic but examines some of the theoretical debates around the use and validity of dynamic systems theory (a good overview of DNS can be found here).

I am only posting the "Introduction" below, so follow the links to read the whole article (pdf).

Full Citation:
Fusella, PV. (2013). Dynamic Systems Theory in Cognitive Science: Major Elements, Applications, and Debates Surrounding a Revolutionary Meta-Theory. Dynamical Psychology. dynapsyc.org

Dynamic Systems Theory in Cognitive Science: Major Elements, Applications, and Debates Surrounding a Revolutionary Meta-Theory

Paul V. Fusella
January 15, 2013

Introduction

“It is the theory that decides what we can observe.” -Albert Einstein

Dynamic Systems Theory (DST) is a broad theoretical framework imported from the physical sciences and used in psychology and cognitive science in the past several decades that provides an alternative to the computational and information-processing approach that has governed main stream cognitive science since the dawn of the cognitive revolution in the mid-twentieth century (Beer, 2000; van Gelder & Port, 1995; van Gelder, 1998; Spivey, 2007). DST views all psychological processes and capacities as dynamic systems which are best described as complex, non-linear, self-organizing and emergent and whereby cognition develops over the life course and occurs over real-time as a probable description of many possible alternatives instead of linear-assembly-of-symbolic-processes (Spivey, 2007; van Gelder & Port, 1995). Psychological capacities are viewed as emerging as more complex unique forms from prior simpler states, moving from chaotic to more stable trajectories in a theoretical state-space that culminate in the manifestation of a specific thought in real-time or a developmental phenomenon over ontogenesis (Spivey, 2006, Thelen & Smith, 1994; van Geert, 1998). There is a sensitivity to initial conditions and a determination by multiple causality, whereby psychological phenomena, be it a developmental capacity or cognition more generally, are softly-assembled (Thelen & Smith, 2003).

This overarching and revolutionary view for cognitive science has been in the works for quite some time perhaps since the cybernetics movement in the mid-20th century but has become more popular in recent years and has been referred to by a number of different and related labels reflecting related ideas and ranging from chaos theory to complexity theory to nonlinear dynamical systems theory. These titles all refer to similar ideas but have subtle and nuanced differences. I choose to use the term dynamic systems theory because this is the term used by most cognitive scientists who subscribe to this viewpoint and who refer to their movement as the dynamical view in cognitive science and refer to themselves as dynamicists so I will continue with that tradition although the lexicon and conceptual hallmarks used are shared by all these related viewpoints.

Specifically, what I mean by the DST approach in cognitive science (and later to what I refer to as the Complexity Theory (CT) approach) is something also related to work in theoretical computer science and artificial intelligence and empirical and theoretical work done there that applies to some distinct intelligent systems but particularly what it can say about the human mind as an intelligent system. Siegelmann (1997, 2003), Bringsford (2004), Kempis (1991), and Penrose (1990) have described the quintessential hallmark of the DST approach of this particular form of intelligent system as being trans-Turing (or super-Turing) and which possesses hyper-computational capabilities; that is brains and other certain forms of intelligent systems perform processes that go above and beyond the Turing-limit with it’s symbolic-serial processing of the traditional digital computer metaphor that has hallmarked much of the work done in cognitive science and which has been motivated by the information-processing (or computational) perspective. By computationalism I am referring to what has been the dominate theoretical framework in cognitive science since its inception, which has been motivated by the development of the digital computer and principally the work of Alan Turing and the Turing machine, and which uses as a metaphor for the mind, a symbolic-algorithmic-serial-processing digital-computer that computes at or below the Turing-limit.

The argument made by most dynamicists is that DST is a more suitable theoretical framework for situating psychological phenomena because it has achieved success in accounting for other phenomena in the natural world as diverse as meteorological phenomena to kinematics of the human body. The brain and mind are part of the natural world so logically they too can be accounted for by the dynamical view and perhaps more completely and accurately than the traditional computational and information-processing approach with its use of the digital computer as the metaphor for mind. The mind is an abstraction for the neurological underpinnings in the brain and these are not machines they are biological organs made up of cells and organic molecules and they are part of the natural world and could arguably be better accounted for by a meta-theory that has been successful in capturing the diverse natural phenomena that dynamic systems theory has been able to do. I am echoing the argument made by the dynamicists and arguing for a paradigm shift in the sense that Kuhn (1962) described, specifically in cognitive science, as a move away-from the computational, toward a dynamical theoretical framework and paradigm with an recognition that the computations made by the human mind are trans-Turing (or super-Turing) and go above and beyond the Turing-limit of the traditional information-processing approach.

Adopting DST reconciles a lot of the debates in cognitive science surrounding the phenomena that is investigated from monism vs. dualism, nativism vs. empiricism, and subjectivity vs. objectivity, not to mention the various anomalies discovered as a result of adopting a computational viewpoint. DST reorganizes the way that phenomena are studied and conceptualized; where some such as Varela, Thompson, and Rosch (1991) have argued for first person methods in the study of consciousness utilizing methods from traditional Buddhist psychology and which reflects a post-positivist view of how science is conducted. However, others including Spivey (2007), Beer (2000), Thelen and Smith (1994), van Gelder and Port (1995), and Schoner (2008) continue to work empirically with a positivist empirical framework in studying various psychological capacities from a DST perspective and these are the mainstream in the field. Thus, be it the manner in which phenomena are empirically investigated or the manner in which theories are constructed, DST is beginning to be accepted as a viable alternative to the 20th century traditions of computationalism and positivism. DST provides an account of cognitive phenomena that is dynamical, embodied, completely situated and ecologically-grounded and the ways that cognitive scientists go about conducting research and theory building is likely to be influenced by these fundamental aspects to this meta-theory.

In this paper, I will set out to provide an overview of the dynamical approach in cognitive science reviewing the more important work that has been done in recent decades and especially at the turn of the 21rst century. I will focus on a review of two recent debates that were published recently: one in 1998 in the journal Behavioral and Brain Sciences and the other in 2012 in TopiCS in Cognitive Science where contributing scientists debate the legitimacy of the dynamical view. During the review of these debates I will touch on the different facets of DST including work done in embodied cognition and ecologically grounded cognitive phenomena and also work that has been done in applying DST to experiments in psycholinguistics wherein the role of the body, the context, and the environment are united in one framework that is guided by DST.

During this review of the work in the application of DST in cognitive science and reviewing the various formal debates that showed up in peer reviewed journals and discussing them critically, I will echo what others have proclaimed in the past and make the argument that DST is ultimately a more suitable theoretical framework for guiding empirical research and theory building in cognitive science and should at some point in the not so distant future, but especially for moving the field forward in this new century, replace the outdated computational and information-processing approach which appears to have run its course. DST has much promise in providing an overarching and unifying theoretical framework for the cognitive sciences but like anything new it is met with staunch criticisms and rejection. However, the more people that join this movement the more the basic principles embodied in the DST approach will become grounded in empirical evidence. I will begin to conclude the review with a recap of the major controversies that adopting DST provokes from its criticizers and will finish with my modest vision for the future role DST can play in reorganizing the way that science is conducted in the cognitive sciences building off the work that has been done in dynamical cognition from the beginning and reacting against and incorporating within it the good that came from the traditional approaches in cognitive science into the future of what this revolutionary meta-theory means for cognitive science.

Read the whole article (pdf).

Mark Leonard - Mindfulness: Not Solving Difficult Problems

From Mark Leonard at The RSA, on the role of mindfulness in creating a happier, healthier world. I like his definition of mindfulness: "mindfulness is learning to ‘not-do’ anything at all, with purpose."

Mindfulness: Not solving difficult problems

By Mark Leonard on Tuesday, January 8th, 2013

There is currently an explosion of interest in mindfulness, crudely put, the paying of attention to the moment. Mark Leonard argues that simple but powerful mindfulness exercises and broader access to training in the workplace could help us harness the power of our demons for good.

We are a problem solving species. We can make handheld communication devices that do everything apart from wash the dishes; we make dishwashers to do that. However, there is a limit to the problems we can solve. We may be able to make a world where increasing numbers of us have all we could possibly need, but for all our ingenuity, it seems we have unleashed demons we cannot control.

Homo sapiens evolved around a quarter of a million years ago. Seven billion of us now live in a very different world, one in which our society and economy and even the planet itself are under threat.

If you are reading this, you are most probably one of those of us who are fortunate enough to have a roof overhead, comfortable clothing, enough food to eat and a whole load of other stuff. Yet, even amongst those who have these things and more, stress and depression have reached epidemic proportions. What role could mindfulness play in making a better, happier world?

Solving problems is great for all kinds of things, but we are beginning to understand that our minds do not work like machines. Like social groups and the ecosystem that supports life on the planet, our minds are complex systems that are difficult to control but have the remarkable ability to self-organise if given the chance. If placed under stress, our minds can buffer it up to a point but if adverse conditions continue they will reach a tipping point and can fail, often catastrophically.

The current renewed interest in mindfulness has sprung from recent scientific work on depression. For some people a bad day can trigger a downward spiral into the depths of depression; others can acknowledge that they are just having a bad day and sooner or later the fog lifts all of itself. People who suffer from depression notice a low mood and begin to worry that things are going to get worse and sure enough they do. Part of the reason for this is because they ask themselves the question: ‘Why do I feel so bad?’ They treat a bad day like a problem that they need to solve and sure enough they can’t.

By practicing mindfulness exercises we learn to ‘not-do’ anything at all, with purpose. Mindfulness training teaches people who suffer from depression to give themselves a break from using problem solving strategies inappropriately on their emotions. With greater self-acceptance that comes with ‘not-doing’ anything, things tend to get better on their own.

How much better could things be if we could tackle the challenges we face everyday by learning to diffuse stress more effectively? Mindfulness exercises help us to do this in a similar way to the way they help prevent the recurrence of depression. They can give us the tools to uncouple the feedback of over-thinking from our emotions, reducing stress, enabling us to think about things more clearly when it’s useful and to know when it is not.

Of course increasing levels of stress and depression cannot all be put down to applying problem-solving strategies inappropriately. But when we use them on our emotions they tend to backfire and when applied to other people, without taking proper account of the way they feel, the impact can be damaging.

For example, if management strategies are implemented without making sure that employees feel fairly treated, we risk creating a vicious circle of reduced motivation, poor performance and further management intervention. A bit like depression in an individual, this ends up in a destructive spiral of stress and disengagement in an organisation.

What might happen if a significant percentage of people in an organisation were able to break this vicious cycle caused by the stress of over-thinking by practicing mindfulness exercises? Not only would people be less stressed but they would be more aware of the impact of their behaviour on others. Their engagement and happiness would be less dependent on the conditions they experience. They would act as a social buffer to stress in the organisation.

If, individually and collectively, we can be more mindful of the way we employ problem-solving strategies, we could release a great deal of human potential from the damaging effects of stress. If mindfulness training in the workplace became more widely available, organisational cultures would be more likely to emerge from a virtuous circle of employee engagement, collaborative working styles and outstanding performance.

However, releasing human potential from the ravages of stress is not just about improved individual and collective performance. An organisation, which emerges from a culture where employees understand themselves and others around them better, will be more likely to express similar characteristics. When ethical behavior is the norm within an organisation, it will be more likely to take its corporate social responsibility more seriously and behave more ethically with all its stakeholders and society in general.