Why is/was LISP good for AI programming?

In summary, Lisp was thought to be a good language for implementing artificial intelligence because it had features that made it well-suited for the task, such as recursive processing and the ability to write code on the fly and evaluate it.
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Stephen Tashi

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Why is/was LISP thought to be good for AI programming?
Why is/was the LISP programming language thought to be good for writing programs that implement artificial intelligence?

As I imagine it, early AI programs were oriented toward verbal (i.e. text) inputs and outputs. So perhaps a list processing language is/was well suited for that?
 
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I think the symbolic manipulation feature was why. You could write a lisp program that wrote and ran a lisp program ie you could dynamically change the programs processing steps.
 
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Lisp is a very old language, contemporary to languages like FORTRAN. When AI started becoming popular there were few alternatives that could handle non-arithmetic problems. As @jedishrfu says, the ability to perform symbolic and non-arithmetic operations was considered important for AI. Also, as @jedishrfu says, the ability for the program to piece together code on the run and evaluate it was useful. That ability also made FORTH a language to consider.
 
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I always felt Forth was Lisp written RPN.

Lisp was also known as ‘Lots of InSipid Parentheses’
 
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jedishrfu said:
I always felt Forth was Lisp written RPN.
apt descriptor. But Forth seemed so deceptively simple.
 
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jedishrfu said:
Lisp was also known as ‘Lots of InSipid Parentheses’
And anyone who ever had to work with Lisp code in a regular, non-Lisp-oriented, text editor, has to agree.
 
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jedishrfu said:
Lisp was also known as ‘Lots of InSipid Parentheses’
The way I always saw it was "Lots of Stupid Parentheses" or "Lots of Silly Parentheses". :smile:
 
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A reader of this thread suggests LISP is a dead language. However, it was developed for particular applications and as such is a better language than the popular C's, Java's, or Python.

Here is a good review of its utility. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952920/
 
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I had a friend pull up a similar article years ago on Clojure aka lisp on JVM I tried it but felt my team would never consider it even though there were some great benefits. Sometimes procedural / OO folks are a hard crowd to please.
 
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Stephen Tashi said:
Summary:: Why is/was LISP thought to be good for AI programming?

Why is/was the LISP programming language thought to be good for writing programs that implement artificial intelligence?

As I imagine it, early AI programs were oriented toward verbal (i.e. text) inputs and outputs. So perhaps a list processing language is/was well suited for that?
No. Recursion and meta-progamming and functional features were the pro's of LISP.
 
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gleem said:
A reader of this thread suggests LISP is a dead language. However, it was developed for particular applications and as such is a better language than the popular C's, Java's, or Python.

Here is a good review of its utility. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5952920/
Considering that Perl was a significant programming language of the Human Genome Project, I can't help but notice that the referenced article is very vague on any advantage that Lisp has to Perl. As a Perl programmer, I came close (although overcome by events) to joining that effort.

See How Perl Saved the Human Genome Project and BioPerl for more details.
 
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I remember reading a Dummies book on Bioinformatics and it mentioned Perl a lot. It struck me as funny because Perl was on the outs and python was ascending for general programming use.

Often academic projects latch onto programming languages based on what the grad student or principal investigator was familiar with instead of what’s the best overall language to use. Bioinformatics was very text search intensive making Perl an ideal choice but later as other tools gain traction Perl loses to python or some other more performant language. Sometimes choices are made on the free tools and libraries the project latches onto.

I never understood why Clojure/Lisp was gaining adherents when it seemed easy to really get lost in the details of the implementation of an application. Small programs were elegant by scaling up presented developer issues for maintainability. The article my friend had provided said that this startup team had switched from Scala to Clojure and saw huge benefits in maintainability and performance but again I never fully understood why they just didn’t use Java or something similar.
 
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jtbell said:
The way I always saw it was "Lots of Stupid Parentheses" or "Lots of Silly Parentheses". :smile:
Argh, I left out a word. Both should begin, “Lots of Insane…” o0)
 
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I haven't touched LISP in years, so I will only make two comments:
  1. The fact that LISP ignores trailing parentheses is a mistake. That encourages the programmer to stick a zillion at the end, thus covering up a logic error.
  2. There is AI and there is AI. Modern AI is not so concerned with what LISP does well - what I would call machine deduction" - as 1980's AI was.
 

1. Why is LISP considered to be good for AI programming?

LISP (List Processing) is considered to be good for AI programming because it was specifically designed to handle symbolic processing, which is essential for AI tasks such as natural language processing and logic-based reasoning. Its simple syntax and powerful data structures, such as lists and trees, allow for efficient manipulation and representation of complex data. Additionally, LISP's dynamic nature and support for functional programming make it well-suited for developing flexible and adaptive AI systems.

2. How does LISP support the development of AI applications?

LISP provides a variety of features and tools that support the development of AI applications. These include built-in functions for manipulating lists and trees, powerful pattern matching capabilities, and support for both symbolic and numerical computation. LISP also allows for the creation of custom data structures and functions, making it highly customizable for specific AI tasks. Additionally, LISP's interactive development environment and debugging tools make it easier to test and refine AI programs.

3. What advantages does LISP offer compared to other programming languages for AI?

LISP offers several advantages over other programming languages for AI, including its flexibility, expressiveness, and efficiency. Its syntax is simple and consistent, making it easier to read and write compared to other languages. LISP's functional programming paradigm also allows for the development of concise and elegant code for complex AI tasks. Furthermore, LISP's built-in support for recursion and automatic memory management can improve the performance and scalability of AI programs.

4. Is LISP still relevant for AI programming today?

Yes, LISP is still widely used for AI programming today. Many modern AI frameworks and libraries, such as TensorFlow and PyTorch, have LISP-inspired syntax and concepts. LISP also continues to be used in research and development of cutting-edge AI technologies, including natural language processing, machine learning, and robotics. Its unique features and powerful tools make it a valuable language for tackling complex AI problems.

5. Are there any challenges associated with using LISP for AI programming?

While LISP has many advantages for AI programming, there are also some challenges that may arise. Its syntax and functional programming paradigm may be unfamiliar to programmers who are used to more traditional languages, making it harder to learn. Additionally, LISP's dynamic nature and lack of static type checking may make it more prone to errors and bugs. However, with practice and proper coding techniques, these challenges can be overcome, and LISP can be a powerful tool for AI development.

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