Lush is the direct descendent of the SN and TL3 systems. SN was originally developed by Yann LeCun and Leon Bottou in 1987 as the front-end of a neural network simulator. Lush was brought to you by the same people who brought you DjVu.

Various incarnations of SN/Lush have been developed continuously since 1987, some of which were sold commercially by Neuristique S.A. in France until 2001. The system eventually grew into a full-fledged prototyping and development environment. Neuristique released a GPLed version of the Lisp interpreter part of SN in 2000. A version of SN was developed at AT&T Bell Laboratories between 1988 and 1996, and at AT&T Labs between 1996 and 2001. AT&T released their code under the GPL in 2001.

Transforming the various incarnations of SN into Lush was done at the NEC Research Institute by Leon Bottou, Yann LeCun, and Jie Huang Fu.

The version developed at AT&T Bell Labs, and then at AT&T Labs was used to build many successful technologies and products. The most notable ones are:

• a handwriting recognition system used by many banks across the world to read checks automatically. In fact, some ATM machines made by NCR (that can read checks) run compiled SN code on embedded DSP boards. A version of this check reader written in SN is embedded in large check reading engines sold by NCR and OrboGraph. According to some estimates, this system automatically reads between 10 and 20 percent of all the checks written in the US.
• The first prototype of the DjVu image and document compression system was written in SN, including the first decoder and the first foreground/background segmenter.
• Numerous machine learning algorithms developed at AT&T since 1988 have been developed with SN. This includes the first convolutional neural network system for image recognition known as LeNet. This also includes some of the first implementations of Vladimir Vapnik's famous Support Vector Machine algorithm.

SN(1987) neural network simulator for AmigaOS (Leon Bottou, Yann LeCun)
 |
SN1(1988) ported to SunOS. added shared-weight neural nets and graphics (LeCun)
 |   \ 
 |   SN1.3(1989) commercial version for Unix (Neuristique)
 |   /
SN2(1990) new lisp interpreter and graphic functions (Bottou)
   |   \ 
   |   SN2.2(1991) commercial version (Neuristique)
   |    |
   |   SN2.5(1991) ogre GUI toolkit (Neuristique)
   |   / \ 
    \ /  SN2.8(1993+) enhanced version (Neuristique)
     |     \ 
     |   TL3(1993+) lisp interpreter for Unix and Win32 (Neuristique)
     |      [GPL]
     |        \_______________________________________________
     |                                                        |
   SN27ATT(1991) custom AT&T version                          |
     |        (LeCun, Bottou, Simard, AT&T Labs)              |
     |                                                        |
   SN3(1992) IDX matrix engine, Lisp->C compiler/loader and   |
     |       gradient-based learning library                  |
     |       (Bottou, LeCun, AT&T)                            |
     |                                                        |
   SN3.1(1995) redesigned compiler, added OpenGL and SGI VL   |
     |         support (Bottou, LeCun, Simard, AT&T Labs)     |
     |                                                        |
   SN3.2(2000) hardened/cleanup SN3.x code,                   |
     |         added SDL support (LeCun)                      |
     | _______________________________________________________|
     |/
     |
   ATTLUSH(2001) merging of TL3 interpreter + SN3.2 compiler
   [GPL]         and libraries (Bottou, LeCun, AT&T Labs).
     |
   LUSH(2002) rewrote the compiler/loader (Bottou, NEC Research Institute)
   [GPL]
     |
   LUSH(2002) rewrote library, documentation, and interfaced packages
   [GPL]      (LeCun, Huang-Fu, NEC) 

Lush Credits

Lush (or its predecessor SN) was initially written by Leon Bottou and Yann LeCun, but many people contributed to it over the years: Patrice Simard, Patrick Haffner, Yoshua Bengio, Jie Hang Fu, Jean Bourrely, Xavier Driancourt, Pascal Vincent, Sergey Ioffe, and others.