Classical computing using digital symbols—equivalent to a Turing Machine—is reaching its limits. It is undeniable that computing's historic exponential performance increases have improved the human condition. Yet such increases are a thing of the past due in large part to the constraints of physics and how today's systems are constructed. Hardware device designers struggle to eliminate the effects of nanometer-scale thermodynamic fluctuations, and the soaring cost of fabrication plants has eliminated all but a few companies as a source of future chips. Software developers' ability to imagine and program effective computational abstractions and implementations are clearly challenged in complex domains like economic systems, ecological systems, medicine, social systems, warfare, and autonomous vehicles. Machine learning techniques, such as deep neural networks, can help but their capabilities are limited and they are implemented on top of the digital hardware with the aforementioned challenges.
Yet, even as we encounter these limits, we recognize that living systems evolve energy-efficient, universal, self-healing, and complex computational capabilities that dramatically transcend our current technologies. Animals, plants, bacteria, and proteins solve problems by spontaneously finding energy-efficient configurations that enable them to thrive in complex, resource-constrained environments. For example, proteins fold naturally into a low-energy state in response to their environment.a In fact, all matter evolves toward low-energy configurations in accord with the Laws of Thermodynamics. For near-equilibrium systems (see the sidebar) these ideas are well known and have been used extensively in the analysis of computational efficiency and in machine learning techniques.
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