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AI and Programming are the masters that work behind the curtains to establish this make-believe world of wonders.
So how do they do it? Artificial Intelligence is a vast field with applications in almost every industry; to use AI in the applications, engineers working in the field need to feed instructions to the systems to become “intelligent” and behave in a certain way.
These instructions are fed through programming languages, and we will see the top ones used for AI.
Python is, as of now, the language that is used mostly when it comes to AI. It is also among the best languages and a fairly easy one to program. “Python is the most powerful language you can still read” – Pau Dubois.
After the first design of Python was released, a poll was conducted in 1991. According to that poll, 57% of programmers are more likely to choose Python over any other programming language such as C++, while building AI systems.
The program is easy to learn and picked by engineers and data scientists due to its freedom. It also has more comprehensive standard libraries than other languages, which makes it faster to develop.
Numpy is one such library used for solving scientific computation. SimpleAI, EasyAI, AIMA, pyDatalog are some other libraries available in Python for developing AI systems.
Some of the other libraries used extensively in Python for Machine Learning are TensorFlow, SparkMLLib, PyBrain, Scikit-learn. Likewise, for Deep Learning, there is MXNet, PyTorch for Natural Language Processing.
Guido Van Rossum, who released the 1991 version of Python, said, “Python is an experiment in how much freedom programmers need. Too much freedom and nobody can read another’s code; too little and expressiveness is endangered.”
Python Software Foundation is a not-for-profit organization that directs and controls Python and CPython development resources. CPython is a free and open-source model implementation.
Let us look at some of the reasons why this language is considered a good choice for AI:
A great ecosystem of libraries: which are nothing but modules that have pre-written codes that provide a base level of functionality, and coders don’t have to start from scratch each time
Faster to start programming: This refers to a rather low-entry barrier as compared to most other languages. Artificial Intelligence and Machine Learning experts don’t need to learn programming languages as a software engineer does. Instead, they would want to focus on data processing for their end purpose without learning a language. Python allows just that, which is why the AI programmers can get straight to programming without getting to know a lot about the language itself.
Flexibility: The huge flexibility that the language has offers a comfortable environment for programmers to work
Versatility: Python is a highly versatile language and can be run on at least 23 different platforms such as Linux, Windows, Unix. Not just that, it takes only small changes in lines of coding to be implemented to change from one platform to the other
Readability: AI or ML practitioners need to exchange many of their ideas, algorithms, and tools to work together on a subject. Suppose there is difficulty in reading the language; in that case, there are chances of making mistakes while picking up from another coder. While in the case of Python, due to its ease of reading, there is no confusion or conflicts while changing, copying, or sharing with other professionals.
Effective presentation: AI and ML researchers need to represent data in clear and visually intuitive formats such as charts, graphs, histograms. Several libraries in Python allow for such a possibility, one such being MatplotLib.
Few reasons why Python is indeed considered a top language for Artificial Intelligence professionals.
2. R (Programming Language)
R is the most widely used standard language for numerical, statistical data analysis. It is a favorite language for data miners and statisticians.
According to the TIOBE (The Importance of Being Earnest) index, R stands at 9th to measure popularity among programming languages. Google and Uber use R for their big data analysis.
Ross Ihaka and Robert Gentleman created and released R in 1995, two years after Python was first released. The R Development core team maintains R, which is a reimplementation of the S language. R is a free and open-source software used for statistical analysis.
R has several programming packages for Artificial Intelligence, such as Tm for text mining, Gmodels for model fitting, OneR for algorithms useful in machine learning, and RODBC.
Here are some of the explanations of why R is considered a frequent choice for AI:
R’s ability for data crunching with its NumPy package makes it an obvious choice for data processing
Vectorial computation, functional programming, object-oriented programming is some of the paradigms of programming which is possible to work on with R
Like Python (but not as much), R has a wide number of libraries to ease the lives of programmers, such as TensorFlow, Keras, MXNet
The active community support for R users is a great help for AI and ML professionals to garner instant and continuous help
Visual representation is crucial for Artificial Intelligence researchers, and R provides great options to represent analyzed data
It is possible to combine, extend, or link codes from other languages such as C, C++, Java, DotNet, Fortran, and even Python to manipulate results or perform computationally heavy tasks.
R too is one of the top-most languages studied and used by AI professionals and Machine Learning experts. Needless to say, it is among the more popular ones too.
The name LISP, which derives from List Processor, is among the first programming languages, second to Fortran. LISP was created by John McCarthy, one of the founding members of Artificial Intelligence, in 1958 at the Massachusetts Institute of Technology (MIT).
Since its inception, LISP was used as the primary language for Artificial Intelligence because it was designed for mathematical computation.
SHRDLU was a natural language understanding computer program designed in 1968. It was a famous AI system of that time that worked on the programming language named Micro Planner. The LISP was built as a reimplementation of Micro Planner.
Here are some notable features of LISP which make it a great option of choice for Artificial Intelligence using Machine Learning:
LISP is a great tool for rapid prototyping. This means, for a problem with an unidentified solution, LISP works best to tackle it. As Kent Pitman, a notable programmer for designing and implementing LISP and Scheme, had said, “LISP is a language for doing what you have been told is impossible.”
Flexibility with the language makes it an obvious choice for AI professionals. It is also more extensible than other languages
Artificial Intelligence in the past used symbolic data, and LISP supports symbolic programming
Because of its ability to not distinguish between data and code, LISP is a lot more powerful than most other languages
Having said that, over time, many other newer languages have tried to incorporate the features of LISP into themselves. However, LISP remains a natural choice because its inception was to tackle Artificial Intelligence problems by McCarthy.
Julia is among the latest dynamic programming languages which can be used for many applications. Still, its features support data analysis and numerical sciences, therefore used widely in AI.
Stefan K, Jeff Bezanson, Viral B. Shah, and Alan Edelman started designing Julia in 2009 and released it for users in 2012.
Exactly how the designers had intended, Julia became extremely popular because it is high performing and dynamic.
Over 1500 universities, 10000 companies, and 20,000,000 downloads (as of September 2020) show its increasing demand.
Investment management corporations to Insurance companies to the Federal Reserve Bank of New York, Julia, have been used by some of these high-profile clients for time-series analytics, risk calculations, and “make models of the United States Economy” respectively. Julia is also claimed to be ten times faster than its predecessor MATLAB.
Here are some reasons why Julia is proving to be better and sometimes at par with Python:
It is the best-suited language for writing Deep Learning codes at the backend
Its speed in implementing intensive numerical computation, mathematical tasks, and scientific queries
Julia is built with the best of all other contemporary languages – the simplicity of Python, big-data manipulation abilities of Spark, the syntax of Basic (Microsoft’s), and MATLAB. It is therefore very easy to transition into
jl, Flux.jl are highly powerful tools native to Julia perfect for machine learning and Artificial Intelligence
The language is highly scalable and faster than Python and R
Like LISP, PROLOG is one of the first languages that came into being for developing Artificial Intelligence, especially in computer linguistics like natural language processing (NLP).
PROLOG, which means logic programming, was first created by Alain Colmerauer in 1972. ELIZA, which was among the first chatbots capable of attempting the Turing test, was created with PROLOG.
Given that PROLOG had come long back, here’s why it is still used for AI:
PROLOG has a relatively smaller set of basic operations such as pattern matching and tree-based data structuring, which makes it a powerful programming framework
The PROLOG could backtrack the search path to find an alternate path if the previous attempt was a failure. This also sometimes helps in finding multiple solutions to a problem
PROLOG is declarative rather than imperative, which is why an AI professional can focus on writing the facts and rules of the problem instead of giving commands for fetching solutions. The language has an in-built search mechanism that takes care of the execution of the problem
Although several advanced languages have come into existence after PROLOG, it remains a favorite with many Machine Learning practitioners.
Each of these languages has features that make AI engineers choose one above the other for specific purposes.
With further AI advancements, more languages will evolve that will be better suited to tackle ML problems and less complex. The current generation of engineers will certainly go forward and be those pioneers who will lead this change.
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