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All operating system software was written in Lisp. Xerox used Interlisp. Symbolics, LMI, and TI used Lisp Machine Lisp (descendant of MacLisp). With the appearance of Common Lisp, Common Lisp was supported on the Lisp Machines and some system software was ported to Common Lisp or later written in Common Lisp.
It has many of the features of Lisp Machine Lisp (a large Lisp dialect used to program Lisp Machines), but was designed to be efficiently implementable on any personal computer or workstation. Common Lisp is a general-purpose programming language and thus has a large language standard including many built-in data types, functions, macros and ...
Lisp Machine Lisp is a programming language, a dialect of the language Lisp. A direct descendant of Maclisp , it was initially developed in the mid to late 1970s as the system programming language for the Massachusetts Institute of Technology (MIT) Lisp machines .
Lisp Machines, Inc. was a company formed in 1979 by Richard Greenblatt of MIT's Artificial Intelligence Laboratory to build Lisp machines. It was based in Cambridge, Massachusetts . By 1979, the Lisp Machine Project at MIT, originated and headed by Greenblatt, had constructed over 30 CADR computers for various projects at MIT.
By the early 1980s several groups were already at work on diverse successors to MacLisp: Lisp Machine Lisp (aka ZetaLisp), Spice Lisp, NIL and S-1 Lisp. Common Lisp sought to unify, standardise, and extend the features of these MacLisp dialects. Common Lisp is not an implementation, but rather a language specification. [4]
It is mostly used for numerical analysis, computational science, and machine learning. [6] C# can be used to develop high level machine learning models using Microsoft’s .NET suite. ML.NET was developed to aid integration with existing .NET projects, simplifying the process for existing software using the .NET platform.
Pascal MicroEngine and Lisp machines are good examples of this. HLLCAs have often been advocated when a HLL has a radically different model of computation than imperative programming (which is a relatively good match for typical processors), notably for functional programming (Lisp) and logic programming (Prolog).
Specialized computer hardware is often used to execute artificial intelligence (AI) programs faster, and with less energy, such as Lisp machines, neuromorphic engineering, event cameras, and physical neural networks. Since 2017, several consumer grade CPUs and SoCs have on-die NPUs. As of 2023, the market for AI hardware is dominated by GPUs. [1]