| > There are additional difficulties brought about by the economics of large scale production. Programmers have become "proletarianized". The elite expert programmer who crafted a system and stayed with it for many years, finely tuning it and adding new bells and whistles with ease, has by and large been superseeded by an entire generation of college graduates who were introduced to computing in their courses, and who are hired and fired by programming shops in accord with the winds of the market place. > A radical approach to the complexity problem has been to suggest that the easiest way out is simply to make the machine do everything; i.e. automatic programming. [...] this approach does seem seductive, it is our estimate that it will not in the short run produce results of much value to the designer of [...] large scale programs > the belief that man-machine interaction can be a symbiotic relationship in which the overall productivity is greater than the sum of the parts. > how a knowledgeable computer could help an already competent programmer. It has been our
experience that we can produce better and cleaner code faster when working with a partner who shares our understanding of the intentions and goal structure of our program. We, therefore, believe that the appropriate metaphor for our work is that of creating a program with the capabilities of a junior colleague working on a joint project. The program should know the problem domain, implementation techniques, and the programming language being used fairly well. It need not know everything in advance; it can always ask its senior partner for advice or further information. Furthermore, this program might well be capable of paying more attention to details, of writing trivial parts of the code, of checking that certain constraints are satisfied, and even (in some cases) of cleaning up a large system after it has been put together. > First Scenario: Initial Design > I'd like to build a hash table `O.K. youll need an insert, a lookup, an array, a hasher, and optionally a delete routine.` The P.A. knows the main parts of a hashing system. > parallels [...] between understanding a program [...and...] natural language. In both cases, a key component in the understanding system is the background knowledge base, which establishes a context for understanding the semantics of the particular utterance in question. The huge problem in natural language understanding research is that if you try to advance beyond conversations in toy domains like the blocks world, this background knowledge quickly amounts to having a common-sense model of the whole world of human existence. Unfortunately, building such a representation of the world is exactly the central unsolved research project of the entire A.I. community. > The transition from tab equipment systems to the modern day computer utility, exemplified by MULTICS, has taken little more than two decades. Understanding LISP Programs: Towards a Programmer's Apprentice (1974) https://dspace.mit.edu/handle/1721.1/41117 |
That's hilarious! In 1974, that was decades from actually happening.