How to keep your nano-environment from running amok.
1. Small programs for big tasks. How do we fit small programs into small nanites to perform complex tasks? Fractal algorithms may hold the key, but we don't yet have any science of fractal program compression.
2. Many nanites make light work. How do we get trillions of nanites to work together? Self-organizing systems may be the route here. But we don't yet understand emergent properties, let alone how to design systems to exhibit some required emergent property.
These questions were posed at the end of a talk entitled: "Nanotechnology and complexity: consequences for computing." What struck me was that logically would it not be more feasible to have a distinct hierarchy of nanobots? Instead of attempting to program each machine with a separate set of instructions, it would prove more productive to transmit the programming from a remote source, so that the nano-environment is controlled collectively. The task of constructing an object would then be split into its respective parts.
Nanobots designed for a specific purpose would not be pre-programmed to implement only one set of tasks. On their own they would be empty shells. The driving force would be an external program that could regulate the progress of the machines. This would solve the problem of control. Self replication would be regulated by the larger system, which would then carry out the task of building the structures, constantly monitoring the manufacturing process and thus giving all control to the larger system.
Worries that circulate in concern to the possibility of faulty nanobots are therefore obsolete. If the "drone" nanobot needs the external programs of the mother system to function then a leak of bots into our natural environment would not be fatal. They would not be able to self-replicate and they would not be able to disassemble matter -- they would be inert.
Has anyone considered the ways in which these machines will be powered? No doubt the future will bring artificial life forms that can interact with the environment (as their natural counterparts do) to produce energy to run their own systems giving them independence from their creators -- a dangerous gift. For controlled production-line scenarios it would be better to have an external source of power, again controlled by the main system.
The mechanisms by which the instructions and power are transmitted are debatable. There would most likely be an infrastructure. Also these machines need not all be "worker" bots, some receive the instructions and relay it to their respective groups of machines. Some regulate the functioning and maintenance of the bots in their group by removing mechanically faulty or dysfunctioning machines. Others observe the progress and relay information back to the (human) control system.
I realize all this is heavily hypothetical but there is a lot to be said for mirroring existing methods of organization. It's just a matter of time before we can implement the plausibility of such organization.
Phil Smith is Web designer with experience in IT support. He has an enduring interest in fringe science, in particular its application to the betterment of mankind.
A Ubiquity symposium is an organized debate around a proposition or point of view. It is a means to explore a complex issue from multiple perspectives. An early example of a symposium on teaching computer science appeared in Communications of the ACM (December 1989).
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Ubiquity Symposium: Big Data
- Big Data, Digitization, and Social Change (Opening Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic
- Big Data and the Attention Economy by Bernardo A. Huberman
- Big Data for Social Science Research by Mark Birkin
- Technology and Business Challenges of Big Data in the Digital Economy by Dave Penkler
- High Performance Synthetic Information Environments: An integrating architecture in the age of pervasive data and computing By Christopher L. Barrett, Jeffery Johnson, and Madhav Marathe
- Developing an Open Source "Big Data" Cognitive Computing Platform by Michael Kowolenko and Mladen Vouk
- When Good Machine Learning Leads to Bad Cyber Security by Tegjyot Singh Sethi and Mehmed Kantardzic
- Corporate Security is a Big Data Problem by Louisa Saunier and Kemal Delic
- Big Data: Business, technology, education, and science by Jeffrey Johnson, Luca Tesei, Marco Piangerelli, Emanuela Merelli, Riccardo Paci, Nenad Stojanovic, Paulo Leitão, José Barbosa, and Marco Amador
- Big Data or Big Brother? That is the question now (Closing Statement) by Jeffrey Johnson, Peter Denning, David Sousa-Rodrigues, Kemal A. Delic