Artificial intelligence (AI) is increasingly ubiquitous and already transforming many aspects of our lives from how we manage our health to how we access news. Yet for all the hype and investment, most AI has focused on a relatively narrow set of applications with little attention given to the relationship between artificial intelligence and our collective human intelligence (CI) - the enhanced capacity that is created when groups think and work together to solve problems. We are at a critical turning point to set the trajectory of AI and we need to continue to challenge our thinking about what we want from AI in society and what role we want it to play. Unless we do, both AI and CI will continue to fall short of our expectations. So how do we start to think differently about AI's potential?
Teamwork and collective intelligence drive the production of goods and knowledge in our society and are an integral part of our increasingly networked lives. However, we are still far from understanding the causal mechanisms that underlie effective organization and communication in teams, and the study of human factors in many fields of computer science has tended toward abstractions of behavior over the understanding of fundamental social processes. I argue that the rapid growth of socio-technical systems on the Internet presents both an impetus and an opportunity for a more causal approach to understanding teamwork and collective intelligence. We can approach this goal through broader deployment of experiments, fostering closer ties between theory and empirical work, and by bridging the gap across different disciplines.
The Canadian poet Dennis Lee once wrote that the consolations of existence might be improved if we thought, worked, and lived as though we were inhabiting "the early days of a better civilization." The test of this would be whether humans, separately and together, are able to generate and make better choices. This is as much a question about wisdom as it is about science.
The Canadian poet Dennis Lee once wrote that the consolations of existence might be improved if we thought, worked, and lived as though we were inhabiting "the early days of a better civilization." The test of this would be whether humans, separately and together, are able to generate and make better choices. This is as much a question about wisdom as it is about science. We don't find it too hard to imagine continued progress in science and technology. We can extrapolate from the experiences of the last century toward a more advanced civilization that simply knows more, can control more, and is less vulnerable to threats.
This white paper was prepared by the participants of the fall 2016 long program Understanding Many-Particle Systems with Machine Learning. Interactions between many constituent particles, i.e. quarks, electrons, atoms, molecules, or materials, generally give rise to collective or emergent phenomena in matter. Even when the interactions between the particles are well defined and the governing equations of the system are understood, the collective behavior of the system as a whole does not trivially emerge from these equations. Despite many decades of prominent work on interacting many-particle (MP) systems, the problem of N interacting particles is not exactly soluble. In fact, computational complexity typically increases exponentially with N.