Computer-aided translation (CAT), also referred to as computer-assisted translation or computer-aided human translation (CAHT), is the use of software to assist a human translator in the translation process. The translation is created by a human, and certain aspects of the process are facilitated by software; this is in contrast with machine translation (MT), in which the translation is created by a computer, optionally with some human intervention (e.g. pre-editing and post-editing). CAT tools are typically understood to mean programs that specifically facilitate the actual translation process. Most CAT tools have (a) the ability to translate a variety of source file formats in a single editing environment without needing to use the file format's associated software for most or all of the translation process, (b) translation memory, and (c) integration of various utilities or processes that increase productivity and consistency in translation. Computer-assisted translation is a broad and imprecise term covering a range of tools.
For decades it has been the dominant metaphor in neuroscience. But could this idea have been leading us astray all along? We are living through one of the greatest of scientific endeavours - the attempt to understand the most complex object in the universe, the brain. Scientists are accumulating vast amounts of data about structure and function in a huge array of brains, from the tiniest to our own. Tens of thousands of researchers are devoting massive amounts of time and energy to thinking about what brains do, and astonishing new technology is enabling us to both describe and manipulate that activity. We can now make a mouse remember something about a smell it has never encountered, turn a bad mouse memory into a good one, and even use a surge of electricity to change how people perceive faces. We are drawing up increasingly detailed and complex functional maps of the brain, human and otherwise. In some species, we can change the brain’s very structure at will, altering the animal’s behaviour as a result.
The IP metaphor, after all, has been guiding the writing and thinking of a large number of researchers in multiple fields for decades. In a classroom exercise I have conducted many times over the years, I begin by recruiting a student to draw a detailed picture of a dollar bill - ‘as detailed as possible’, I say - on the blackboard in front of the room. When the student has finished, I cover the drawing with a sheet of paper, remove a dollar bill from my wallet, tape it to the board, and ask the student to repeat the task. When he or she is done, I remove the cover from the first drawing, and the class comments on the differences. Because you might never have seen a demonstration like this, or because you might have trouble imagining the outcome, I have asked Jinny Hyun, one of the student interns at the institute where I conduct my research, to make the two drawings.
All of these, when used correctly, improve both the programmer's efficiency and the clarity of the code by making the intended purpose more explicit. A consequence of syntactic abstraction is also that any Lisp dialect and in fact almost any programming language can, in principle, be implemented in any modern Lisp with significantly reduced (but still non-trivial in some cases) effort when compared to "more traditional" programming languages such as Python, C or Java. Analysts have developed various methods to formally specify software systems. Knowledge-based techniques (Refine, Gist). Specification languages generally rely on abstractions of one kind or another, since specifications are typically defined earlier in a project, (and at a more abstract level) than an eventual implementation. The UML specification language, for example, allows the definition of abstract classes, which in a waterfall project, remain abstract during the architecture and specification phase of the project. Programming languages offer control abstraction as one of the main purposes of their use.
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