Path: utzoo!utgpu!watmath!iuvax!rutgers!aramis.rutgers.edu!athos.rutgers.edu!nanotech From: dmo@turkey.philips.com (Dan Offutt) Newsgroups: sci.nanotech Subject: Intractability of active-shield testing Message-ID: Date: 21 Jun 89 03:27:20 GMT Organization: Philips Laboratories, Briarcliff Manor, NY Lines: 100 Approved: nanotech@aramis.rutgers.edu Suppose that AI-based design systems that can think a million times as fast as a human designer become possible, inexpensive, and numerous. What changes would this imply in the rate of technological advance? It seems clear that there will be *some* increase in the rate of technological advance. But the increase will be much less than proportional to the hardware speedup obtained. Million-times-faster designers cannot bring in one year the designs that unspeeded designers would bring in a million years. One reason, briefly, is that a speedup in conscious design cannot serve as a substitute for real-world testing of design realizations. Real-world testing takes time, cannot be speeded up without substantial risk, and produces empirical data about design performance that cannot be obtained in any other way and which is a critical ingredient in subsequent design efforts. For example, the testing of a particular make and model of automobile is performed by consumers who drive the automobiles through precisely the environment to which it must be fit, if the design is to be a success. This testing process produces a steady stream of feedback to designers: consumer complaints about performance and asthetics, manner of failure in accidents, repair rates and types, median useful lifetime and so forth. This information is invaluable in uncovering *in-principle-unpredictable* design flaws. Testing cannot be speeded up: One must wait patiently for consumers to slowly generate design-performance data as they go about their everyday driving activities. The objection may be raised that design flaws are predictable by simulation or simplified mock-ups of real environments. But many design flaws are still unpredictable because design failure can be a function, in part, of almost anything the environment to which the design must be fit. And complete information about such environments is never available to the designer, simulation programmer, or mock-up builder. These remarks apply to designs in general, and nanomachine designs in particular. Nanomachines are likely to be more complex than present-day machines (holding size constant). In general, the more complex the machine, the more difficult it will be to predict its interaction with the environment to which it must be fit. Consequently, collecting performance data during the testing phase will be at least as important for nanomachines as it is for today's machines. Thus the time required for testing nanomachines will limit the rate of nanotechnological progress to much less than might be suspected, given the availability of a million-fold speedup in the speed of AI-based design programs. These observations apply to the distributed nanomachines called active shields. If a prototypical active shield is not tested in the real-world then many or most of its design flaws will not be identified. If it is tested in the real world under the actual noxious conditions it is supposed to protect against, then it is already too late to be of help. If it is tested in a scaled-down sealed ecosystem (a sealed greenhouse, for example), then any characteristic of the complete environoment not present in that scaled-down enviroment is a potential source of design failure. Simulations are even more unsatisfactory. It follows that active shields are less likely to be ready in time to protect against the replicating nanomchines that will inevitably be unleashed into the environment. Caveats: For a given design, some types of quality feedback will come earlier and some later. The lifetime of some automobiles is ten years. Certain facts about such automobiles are discovered only during the tenth year, and not earlier. One may invest more or less effort in acquiring information about the environment to which one's design must be fit. There is the issue of which types of feedback to seek out. The building of a model of this environment is a resource consuming task itself. There is the issue of how much small errors or incompletenesses in the designer's information about the target environement affect the success of a design. Personally, I suspect that seemingly insignificant details about an artifact's environment can often have a very large impact upon success, especially if those details have a long period of time over which to act. The strictly-internal interactions among the components of a design can be complex enough to make the success of a design unpredictable even when the environment is both simple and fully understood. Consider the failure of Apollo 13. Empty space is a very simple environment. A Saturn-V rocket is fairly complex. There is the question of whether the design realization can be neatly distinguished from its environment. Design affects choice of environment since different artifacts will be sorted into different niches. Sports cars are sorted into different niches in the economy than passenger sedans. Sports cars end up in accidents more frequently than passenger sedans. Dan Offutt dmo@philabs.philips.com