Lisp was originally created as a practical mathematical notation for computer programs, influenced by the notation of Alonzo Church's lambda calculus. It quickly became the favored programming language for artificial intelligence (AI) research, according to Wikipedia.

If Lisp is still used in AI, then is it worthy of learning it, particularly in the context of machine learning and deep learning?


LISP was popular because back in the old days of AI because of the functional syntax, which worked well with the GOFAI paradigm of the time.

Nowadays most researchers have given up on the classical computational theory of mind (read: language of thought), and thus also the GOFAI paradigm that it associates with.

LISP is not what you want to learn if you want to do neural network stuff, but the philosophical background is still important to know.


AI is a very diverse field of research, technology and science, so many computer technologies and programming languages are used in various AI-related projects.

Most of the recent developments and breakthroughs are happening in the machine learning, deep-learning areas where the most widely used programming language is Python. The reason is that the major deep learning frameworks (see Tensorflow, Theano, Keras, neon, Caffe) have Python interfaces. LISP is not really used in these areas, however you can find some deep learning frameworks (for example Cortex by Thinktopic) implemented in Clojure.

LISP was the language of choice for other kind of AI projects, mostly for natural language processing (see SHRDLU, Cyc).


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