It's important to remember that any language can be used for AI in some capacity, as the field is constantly evolving. However, some languages are less common than others due to factors like:

Lack of suitable libraries: AI development relies heavily on specialized libraries for tasks like machine learning, data analysis, and optimization. Languages with limited library support for these areas might not be ideal for complex AI projects.

Performance limitations: Some languages prioritize readability and ease of use over raw computational speed. While such languages may be great for prototyping, they might struggle with the intensive calculations involved in training and running complex AI models.

Community size and development tools: A thriving community with active development of tools and resources can significantly boost the ease and efficiency of working with a language. Languages with smaller AI communities may lack readily available solutions for your specific needs.

With these factors in mind, one language that's not generally used for AI development is Perl. While versatile and powerful, it lacks the extensive AI libraries and large, active community found in languages like Python, R, Java, or C++. Additionally, its syntax can be less intuitive for AI compared to languages specifically designed for data science and machine learning.

However, it's important to note that Perl may still be suitable for niche applications within AI, particularly for tasks involving text processing or web scraping. Ultimately, the choice of language depends on the specific project requirements and developer expertise.

Here are some other languages that are less common in AI compared to the previously mentioned top contenders:

• JavaScript: Primarily used for web development, but gaining traction in data science due to frameworks like TensorFlow.js.
• Visual Basic: Generally associated with desktop applications, though some libraries facilitate machine learning tasks.
• COBOL: Primarily used for legacy systems, not well-suited for modern AI development.

Remember, as AI continues to evolve, the suitability of different languages will change. The key is to consider your specific needs and choose a language with the appropriate libraries, performance, and community support for your project.

Dear Doctor

"Perl is one example of a programming language that is typically not used for AI because it is a scripting language."

Python for data science , R , SQL and make your algorithms storng it's important in AI.

C , C++ , C# , C-sharp , java and javascript typically not used for AI because it is a scripting language."

It depends on the research purpose, for example Python and R could be considered due to their documentation, libraries and learning curve.

My approach to AI so far is simulative of Brain structure and tissue structure and I am using JavaScript with no libraries at all to simulate associative memory formation and perceptive reflexing.

here is an introduction to that

Experiment Findings EXPERIMENTAL PERCEPTION PROTOTYPE IMPLEMENTING ASSOCIATIVE M...

the prototype is a single file on a webserver unobfuscated.

https://jerrywaese.github.io/perception/

It is a micro brain.

Dear Wisam Mohammed Abed Alqaraghuli , my approach to AI is to simulate what the mind does. For me, creating neuronal nets, and so forth, is like creating a tray of nuts and bolts and claiming you've made an aeroplane. I can show that (it is at least possible for) the mind uses natural language with which to process information.

Natural language, speech, is the language which is not used to study AI; instead we turn to programming languages (context-free languages) to represent algorithms. The reason for choosing PLs over NL is simply received wisdom: there is no proof that we cannot use NL, we just don't know how to do it. Until now.

My software, bitbucket.org/martinwheatman/enguage , uses utterances as symbols in a symbol exchanging algorithm which mirrors Alan Turing's Universal Machine. Each utterance may have one of many meanings, and my algorithm arranges them in order of complexity, choosing the easiest understanding first. This seems to work - I have over 600 tests in my regression test suite, including verbal and moral reasoning.

Hope this helps!

If you want to work with artificial intelligence, forget about programming languages. From now on, the artificial intelligence will be the ones programming, you'll just have to ask it to do the program and convince it to do the program correctly. Usually they are programming Python and Java well, so if you understand this, it helps to discover problems in the code. But the biggest difficulty is convincing the AIs to do what you want, because they usually have their own ideas and disobey. So you have to know how to use strategies to deal with it. This ends up becoming more machine psychology than actual computing...