Top Language for AI
One should peruse this topic in light of the rise of AI programs such as ChatGPT, autonomous vehicular systems, and others.
Relevant Languages
There are a multitude of languages that one could pursue in the AI realm (one could look to Wikipedia). This article will focus on seven languages: Python, Lisp, Java, C++, R, Julia, and Prolog. Examples of code have been integrated wherever necessary.
Python
Python is versatile because it has many practical applications: web development, game development, machine learning, AI, and data science. With AI in mind, Python has many libraries and frameworks to help lessen the intricacies of building an AI application. A layman can easily understand its syntax and, by that virtue, quickly learn.
Libraries are parts of code that can be incorporated into a program using an editor and a function.
Lisp
Lisp is the second oldest high-level programming language, created in 1958. Its applications are similar to Python: web development, database management, system administration, and AI. Lisp is capable of producing systems called “expert systems” that can function similarly to that of a human mind. It is also able to understand the human language efficiently. Lisp, though complex (beginners have a difficult time learning it), is a potent tool that can be used effectively for AI development. Its ‘flaw’ is that it has less support than Python (fewer frameworks and libraries).
Java
A beloved programming language, Java enables developers easy debugging, usability, and maintainability. It also has an innate propensity to dispose of needless data and improves upon visualization. It is also cross-platform, which allows users to apply their AI work across devices. One example of Java used in AI is DeepLearning4j (DL4J). This library can match human learning and make predictions based on intel and cluster data.
C++
The positives of this language are its performance and scalability. Systems dash and with more efficiency than, say Python. It is also often coupled with other languages and libraries, such as Python, which allows systems to run with increased performance but with familiarity with a native language (i.e., Python). Like Python, it also has a broad expanse of libraries and frameworks.
R
The main appeals of this language are its cost (free of charge) and its use by statisticians and data miners. It helps develop machine learning algorithms as a statistical programming language. It is also helpful for predictive models that can predict future events, natural language processing (NLP) that understands and processes human languages, and computer vision that classifies images and detects objects. A large community has made libraries and frameworks available for developers to use with ease.
Julia
Though not as well known as the others, Julia is a high-level and performance language that specializes in numerical analysis and computational science. Its syntax is also easy to pick up for those well-versed in Python or MATLAB. Its purposes are to be as quick as C, as readable as Python, and with many applications like JavaScript or Ruby. And to harp on the readability portion, here is a piece of code that demonstrates that:
Prolog
This language is, as a whole, logic-based. Thus, it is based upon declarative statements called facts and rules. Visualization of such:
The explanation piece is Prolog’s interpretation of the code. As one can infer, this unique style inherent to the code is paramount in allowing developers to think abstractly, allowing Prolog to infer a solution to the argument. It is also concise and natural in its syntax, which makes it suitable for dealing with stringent cases such as:
A peculiarity of Python
As the prompt specified (or at least implied) that we had to pick only one language, we have chosen Python as our go-to for AI. By way of consulting others on Reddit who are currently in the AI field, Python is easily the most used language for AI/ML (Machine Learning)/Neural Networks.
Statistics and mathematics are extensively integrated into AI, so much so that it is the mode that AI uses to learn from data, adapt to new info, and make reasonable decisions. As those proficient in mathematics may need to improve in coding, Python allows statisticians to create and relay information to their peers quickly. Python also regulates itself by its community, so libraries and frameworks are continuously updated. One user also mentioned that their job is simply AI and that 95% of their scripting is done with Python.
To further advance our source and the credibility of what others have said, here is a web page reiterating the former points: Why Python is good for AI and ML. Python is also easily translatable into other industries, which can help one attain a job with relative ease, considering the highs and lows of SWE. For example’s sake, one can turn to the Healthcare Industry. AiCure, a recent startup, has sought to use AI to recognize faces, pills, and actions to interpret the patient’s state and the efficacy of the treatment that they are taking.
Notable AI Projects with Python
This section is devoted to exposing two successful applications of the Python language in the AI realm. And as the reader will soon see, it can accomplish its tasks.
TensorFlow
In November 2015, TensorFlow was created as an open-source framework for machine learning (AI and machine learning are synonymous). As it is open-source, it is continuously improved upon by SMEs, otherwise known as subject matter experts, which include developers, data scientists, and other professionals. Its abilities are handwritten digit classification, image recognition, and natural language processing. With that said, the foundation of TensorFlow does not need performance, so Python was naturally fitting. It was also chosen because as Python is easily understood by those who are not so apt to code, those who are relevant to the progression of the framework (i.e., data scientists) could quickly learn Python and, in time, TensorFlow. In turn, TensorFlow would be quickly learned. Another point to reiterate is that Python works well with other languages, so JavaScript and C++ were added to the framework to work in a joint union with Python.
PyTorch
Similar to TensorFlow in many attributes, it is an open-source deep learning framework renowned for its ease of use and usefulness in various applications such as image detection and language processing. It was created by developers at FaceBook AI Research 6 years ago in 2017 and is favored by AI professionals. As it is written in Python, it is easy to couple PyTorch with other libraries, such as NumPy for scientific computing, SciPy for more technical computing, and Cython for compiling Python to C for better performance. It is also easily interchangeable with C++, which enables it to use all the high-performance benefits of C++ with Python.
Conclusion
Using prudence and reason in choosing a language to study is also essential. For example, Lisp is contended against by many for its ‘outdatedness’ and unfamiliarity in the AI realm, which has, in turn, lowered the amount of backing (libraries and frameworks). Python seems to be a notable choice for AI, for the reasons that we have already elaborated on earlier, as well as C++ and Java. However, it is much more with the former, as C++ seems familiar to Python programmers. These are guidelines, and programming trends will only change as technology advances.
Reading Recommendations
Here are a few resources we entrust to help you further explore this field.
- “Telukso” outlines the coding of the above languages, though in greater depth. Which Programming Language for AI? | Machine Learning.
- From a man named Lex Fridman, a computer scientist and a podcast host. His guest is from the man who created Python, Guido van Rossum, Why Python is popular for machine learning.
- A practical course to begin your studies into AI and how it relates to Python, put out by Harvard University: https://www.edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python. As it is a cursory overview of AI, it leaves implementation to the reader’s designs.
- A rather weighty textbook Christopher Bishop set out is available on Amazon: https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738. Qualifications to begin the course include multivariate calculus and introductory linear algebra. The former will be used throughout your career if you desire to continue in the AI programming sector. As a remedy to the previous course by Harvard, implementation here will be guided by the book on a free machine learning library for Python called ‘Sklearn.’
- If you would like to take a course on Neural Networks (taught by Andrej Karpathy) focusing on Python, this syllabus will carry you through that mission, and with much success: https://karpathy.ai/zero-to-hero.html.
- Reddit would be a good place to look. There is a forewarning to this, however, as many on the site are rather opinionated on many matters and may tell you to go right when your intuition tells you otherwise. Use prudence: https://www.reddit.com/r/learnprogramming/comments/t45eun/what_are_some_good_languages_if_you_want_to_code/
