The HP2116A/B/C Computer
HP's first computer, the HP 2116A was developed as a controller for HP's programmable instruments. It was the largest single mechanical package HP had ever built to date, and it marked HP's first use of integrated circuits.
When HP first entered the field, most computers on the market had to be pampered in air-conditioned rooms on spring-loaded floors. HP assumed that the 2116A, an instrumentation computer, should pass the same environmental tests as the instruments it would team with. So HP quickly took the lead in ruggedness and reliability, and helped transform the computer into a go-anywhere, do-anything tool.
AICS Research's Text
The Bug Counter
The obvious similarities of HP's photograph and the one above was the impetus that prompted me to put together this short series of web pages. The picture above was taken in my office at New Mexico State University.
In 1968, after completing four years of satellite tracking and a change of departments, we purchased an HP2116C to build an automated insect indentification and classification system. The "bug counter" eventually came to be capable of indentifying approximately four thousand insects an hour using pattern recognition techniques.
We wrote the disc operating system for the HP2116 from scratch. Indeed, all initial programs had to be entered through the switch register. Although a FORTRAN compiler/operating system was available for the HP2116, we couldn't use it. Speed and memory constraints (8K of core RAM initially) were such for the insect counter that we found that we had to write every routine and driver by hand to be as small and as efficient as possible.
The bug counter's accuracy against soft-bodied insects (hemipterans, homopterans, etc.) evolved to be generally 70 to 85%. But, on hard-bodied insects, such as coleopterans, its accuracy climbed reliably to between 90 and 95% correct.
The purpose in assembling the bug counter was to as quickly count and indentify as many species of insects as possible to determine the likelihood of an onset of infestation of several primary pest insects in cotton (cotton bollworm and pink bollworm, principally). If these outbreaks could be seen sufficiently far enough in advance, then parasitoid and predator insects could be released with sufficient efficacy that the massive use of insectides could be forestalled or eliminated altogether. The concept underlying the construction of the insect counter is termed Integrated Pest Management, a very real attempt to wean farming from the intense use of insecticides.
About thirty "scouts" (generally undergraduate and graduate students) would regularly sweep cotton fields throughout the area. The insects they collected would be brought back to the lab, separated from the plant trash, and placed onto a large scanning table to the left of the HP2116 (not visible in this photograph).
The operating system we constructed by hand was composed of three layers, all operating simultaneously. The first tier was wholly foreground and driven off of the interrupts generated by the scanning table. As the table moved, an HP A/D converter was electrically commanded by the table to absorb readings in three visual channels (red, green and blue) from a series of photomultipliers that we had constructed. Because there was no memory buffering available to us, the HP2116 was, by necessity, commanded to immediately write this data to the 10MB Iomega disc drive (seen to the right of the computer).
The second tier of processing, simultaneous with the table's motion, monitored the completion of an insect's image on the disc. Once done, the software parsed the insect from the accumulating field of data and handed it off to the third (and most background) layer of processing, the pattern recognition algorithms, where once identified, was added to the accruing counts of insects seen.
We knew from the beginning that the pattern recognition routines would be least stable part of the software. Two groups from the Electrical & Computer Engineering Department at New Mexico State created learning algorithms to constantly better improve their respective recognition accuracies. One team emphasized a form of pattern recognition associated with cluster analyses and the identification of their centroids in a multidimensional hyperspace. The second team used what would now be known as an artificial neural net (then known as a connection of threshold logic units). The third, and most successful, approach proved to be to let the cotton scouts (the students, who were for the most part, wildlife and biology students) look at the images printed out on the TTY and let them describe patterns that they found to be most valuable in manually identifying and segregating the various insects. These student suggestions were then programmed up as a series of simple linear discriminant functions in an IF-THEN-DO format.
It was deflating to everyone's ego that the student-generated discriminant functions always outperformed the elaborate learning algorithms that were variously generated by the professorial teams, but that has been a surprisingly common lesson in the history of artificial intelligence research to date.