Nanotechnology as seen from 20 years ago

The neat, clear vision of nanotechnology we had in 1989 rested on two key aspects that would make it a transformative, rather than merely an evolutionary, technology:

• The ability to construct and observe at the atomic scale, and the construction of machines at that scale, taking advantage of various phenomena
• These machines could be production machinery for more machines, shortening capital formation times and increasing economic growth rates

The reality of nanotechnology is shaping up differently from the neat visions of those times, but shaping up it is. There is substantial coverage of the first point today: the techniques for manipulating and observing at the molecular scale are well advanced over 1989. There are things that are arguably machines as well: by some definitions, the last two generations of computer processors have been flat-out nanotechnology. On the atomically precise front, which is closer to what we think really makes a difference as far as nanotechnology is concerned, an increasing proportion of work involves nanostructures with electronic or catalytic properties that perform useful functions.

On the second point there remains an odd dichotomy. Researchers working from the direction of biosystems understand and use the autogenous properties of biomolecular components (e.g. polymerases) and use them as a matter of course. Those coming from the chemistry/surface physics direction, however, don’t seem to have picked up on it, or at least haven’t managed to make the right tools yet.

The bottom line is that 20 years on, the world has picked up strongly on one of the main legs of the nanotech vision, working at atomic scale and precision. The other one, autogenous systems, has been sorely neglected.

In some sense, the two legs of the nanotech vision are the same two properties of life that make living things so different from non-living ones: they have mechanism that is atomically precise and works on that scale; and they reproduce themselves. Besides life, autogenous systems in the real world range from the simple physical models of machine shops that make parts for shop machines, to the memetic ecosystem of ideas that is science itself. Questions that seem like mere technical details, such as growth rates and feedstock closure, turn out to be crucial in understanding major effects ranging from the possibility of gray goo to the prospect of economic displacement. A better understanding of autogeny in software is likely to give us more robust systems and ultimately, true artificial intelligence, since a learning mind is clearly autogenous.

Foresight was a thought leader in 1989 because we had a vision that allowed us to see future possibilities, opportunities and dangers alike, in ways that were not generally apprehended. That is still true. The world at large has picked up on the “atomic scale” leg of the vision, but hasn’t understood the importance of the autogenous systems one.

The first Foresight Conference was notable, among other things, because it was extremely interdisciplinary. Working at the atomic scale involved pulling together knowledge from many branches of physics, chemistry, biology, and other physical sciences. Leading the way in the unfinished business of autogeny will likewise involve pulling together knowledge from a wide variety of fields, ranging from biology and evolution to computer science (consider von Neumann’s classic study of self-reproducing automata) to economics.

For the 20th anniversary of that first groundbreaking conference, Foresight organized a new conference to concentrate on the principles, techniques, and impacts (social and economic) of autogenous systems, from nanofactories to self-improving AIs. Your suggestions and help were invaluable in making it a success, and the videos are now available at http://www.vimeo.com/album/176287.