Airships, which are distinct from blimps by being much more rigid and sounding much less silly, are one of those unusual technologies that has been undergoing a resurgence recently after falling out of favor half a century ago. Airships have potential to be a very practical and cost effective way to move massive amounts of stuff from one place to another place, especially if the another place is low on infrastructure and has a reasonable amount of patience. Lockheed Martin's Skunk Works has been developing a particular kind of airship called a hybrid airship, which uses a combination of aerodynamics and lifting gas to get airborne, for the last decade or so. The P-791 technology demonstrator first flew in 2006, and a company called Hybrid Enterprises is taking Lockheed's airship technology to commercialization. Their LMH-1 will be able to carry over 20,000 kilograms of whatever you want, along with 19 passengers, up to 2,500 kilometers, and it's going to be a real thing: Hybrid Airships recently closed a US 480 million contract to built 12 of them for cargo delivery.

The high interconnectivity required by neural computers can be simply implemented in optics because channels for optical signals may be superimposed in three dimensions with little or no cross coupling. Since these channels may be formed holographically, optical neural systems can be designed to create and maintain interconnections very simply. Thus the optical system designer can to a large extent avoid the analytical and topological problems of determining individual interconnections for a given neural system and constructing physical paths for these interconnections. An archetypical design for a single layer of an optical neural computer is shown in Figure 1. Nonlinear thresholding elements, neurons, are arranged on two dimensional planes which are interconnected via the third dimension by holographic elements. The key concerns in implementing this design involve the need for suitable nonlinearities for the neural planes and high capacity, easily modifiable holographic elements. While it is possible to implement the neural function using entirely optical nonlinearities, for example using etalon arrays\ optoelectronic two dimensional spatial light modulators (2D SLMs) suitable for this purpose are more readily available.

The high interconnectivity required by neural computers can be simply implemented in optics because channels for optical signals may be superimposed in three dimensions with little or no cross coupling. Since these channels may be formed holographically, optical neural systems can be designed to create and maintain interconnections very simply. Thus the optical system designer can to a large extent avoid the analytical and topological problems of determining individual interconnections for a given neural system and constructing physical paths for these interconnections. An archetypical design for a single layer of an optical neural computer is shown in Figure 1. Nonlinear thresholding elements, neurons, are arranged on two dimensional planes which are interconnected via the third dimension by holographic elements. The key concerns in implementing this design involve the need for suitable nonlinearities for the neural planes and high capacity, easily modifiable holographic elements. While it is possible to implement the neural function using entirely optical nonlinearities, for example using etalon arrays\ optoelectronic two dimensional spatial light modulators (2D SLMs) suitable for this purpose are more readily available.

In the first a closed optical feedback loop is used to implement auto-associative image recall. In the second a perceptron-Iike learning algorithm is implemented with photorefractive holography.