![]() Working with my UK graduate students and postdocs, and colleagues elsewhere, I have been able to understand the dictionary in a class of such models which, among other things, provide a microscopic description of black holes. As in many other areas of physics, much progress has been made by studying toy models which capture the essential physics. One aspect of my current research involves deciphering this dictionary. I have been involved in developing this view of emergent space and gravity from its inception. The main challenge here is to discover the dictionary which relates the microscopic and macroscopic descriptions. The equations which describe the physics in terms of these concepts are the equations of General Relativity - analogous to the Navier Stokes equation. In this description additional space dimensions emerge and the distance between points become dynamical. Pretty much like water, at the macroscopic level an alternative and more useful description emerges. These microscopic models do not contain gravity and they are defined either in a lower dimensional fixed space, or in some cases, with no space at all. At the microscopic level, physical processes are described in terms of very different concepts which can be treated in a quantum mechanical fashion. In this current view of gravity, smooth dynamical space itself is such an emergent concept, pretty much like a smooth density of water. Notions like density and viscosity are examples of emergent concepts. There is a well-known equation called the Navier Stokes equation which describes this motion. These concepts are nonexistent at the molecular level. Rather, it is much more useful to describe the water in terms of completely different concepts, such as density, viscosity, etc. In fact, even if it is possible to do so, such a description will not be useful. However, since there are a huge number of molecules, the problem of describing the motion of water in terms of molecules becomes enormously complicated. Microscopically this a collection of molecules which are streaming along and colliding with each other and with the walls of the pipe. Emergent concepts are ubiquitous in physics.Ĭonsider, for example, water flowing through a pipe. In this approach, which originated in String Theory, the very notion of dynamical space is not fundamental. Recent theoretical developments have led to a radically different approach to the problem. Attempts to apply the laws of quantum mechanics naively to dynamical space-time have failed. In this sense, points and distances between points are dynamical. A gravitational wave stretches or contracts space along its line of propagation. However, in the well tested theory of gravity - Einstein’s General Relativity - space- time itself is dynamical. For example, light is an electromagnetic wave which propagates in this fixed space-time. The other forces can be understood as processes happening in a fixed space-time background, where spatial distances and time intervals are fixed once and for all. One reason for this difficulty is the following. However ever since Einstein discovered the laws of gravity in 1915, reconciling gravity with quantum mechanics has been famously problematic. It is natural to think that gravity should also be governed by quantum mechanics. The first three are governed by the laws of quantum mechanics. Ultimately, these complex manifestations of emergent phenomena lead to questions on the nature of intelligence.By Professor Sumit Das, College of Arts & Sciences Distinguished Professor (2019)Īs far as we know, almost all natural phenomena stem from four fundamental interactions: electromagnetism, weak interaction, strong interaction, and gravity. Using behaviorally framed descriptions, the similarities between life forms and highly complex computing machines will become apparent. This technologically networked paradigm shift is happening just as breakthroughs in science unveil emergent phenomena on minute levels. In the grander scheme of things, however, the combination of: ease of manipulation, randomness, simple rule-following, and sociability can be seen as a recipe for emergence in our Web 2.0 era. Beginning with inorganic emergent phenomena, and computerized neural networks, the reader will see what artificial intelligence and human intelligence have in common. Emergence begins from the ground up, with uncomplicated sets of rules that, over time, lead to complex patterning in examples such as the formation of snowflakes and the movements of flocks of birds. Abstract: This paper tours through computing behavior and that of various life forms using emergent phenomena as a point of commonality.
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