L'intelligenza artificiale è ora diventata parte integrante della nostra vita quotidiana con tutti i vantaggi che offre in centinaia di casi e situazioni d'uso unici , per non parlare di quanto semplice e facile abbia reso le cose per noi.

Con la spinta degli ultimi anni, l' IA ha fatto molta strada per aiutare le aziende a crescere e raggiungere il loro pieno potenziale. Questi progressi nell'IA non sarebbero stati possibili senza i miglioramenti principali nei linguaggi di programmazione sottostanti .

Con il boom dell'intelligenza artificiale , la necessità di programmatori e ingegneri efficienti e qualificati salì alle stelle insieme a miglioramenti nei linguaggi di programmazione. Sebbene ci siano molti linguaggi di programmazione per iniziare a sviluppare sull'intelligenza artificiale, nessun singolo linguaggio di programmazione è una soluzione completa per la programmazione dell'intelligenza artificiale poiché vari obiettivi richiedono un approccio specifico per ogni progetto.

Discuteremo alcuni dei più popolari elencati di seguito e lasceremo il processo decisionale a te -

● Python

Python è il linguaggio più potente che puoi ancora leggere.
- Pau Dubois

Sviluppato nel 1991 , Python è stato un sondaggio che suggerisce che oltre il 57% degli sviluppatori è più propenso a scegliere Python piuttosto che C ++ come linguaggio di programmazione preferito per lo sviluppo di soluzioni AI. Essendo facile da imparare , Python offre un ingresso più semplice nel mondo dello sviluppo di IA sia per i programmatori che per i data scientist.

Python è un esperimento di quanta libertà hanno bisogno i programmatori. Troppa libertà e nessuno può leggere il codice di un altro; troppo poco e l'espressività è in pericolo.

- Guido van Rossum

Con Python, non solo ottieni un eccellente supporto da parte della comunità e un ampio set di librerie, ma goditi anche la flessibilità fornita dal linguaggio di programmazione. Alcune delle funzionalità di cui potresti trarre il massimo vantaggio da Python sono l'indipendenza dalla piattaforma e framework estesi per Deep Learning e Machine Learning.

La gioia di codificare Python dovrebbe essere nel vedere classi brevi, concise e leggibili che esprimono molta azione in una piccola quantità di codice chiaro - non in risme di codice banale che annoia a morte il lettore.

- Guido van Rossum

Esempio di frammento di codice Python:

Alcune delle sue biblioteche più popolari sono:

● TensorFlow , per carichi di lavoro di machine learning e utilizzo di set di dati

● scikit-learn , per l'addestramento di modelli di machine learning

● PyTorch , per la visione artificiale e l'elaborazione del linguaggio naturale

● Keras, as the code interface for highly complex mathematical calculations and operations

● SparkMLlib, like Apache Spark’s Machine Learning library, making machine learning easy for everyone with tools like algorithms and utilities

● MXNet, as another one of Apache’s library for easing deep learning workflows

● Theano, as the library for defining, optimizing and evaluating mathematical expressions

● Pybrain, for powerful machine learning algorithms

Also, Python has surpassed Java and became the 2nd most popular language according to GitHub repositories contributions. In fact, Stack Overflow calls it the “fastest growing” major programming language.”

Python Courses for Beginners —

● Java

Write once, run anywhere

Java is considered one of the best programming languages in the world and the last 20 years of its use is proof of that.

With its high user-friendliness, flexible nature and platform independence, Java has been used for developing for AI in various ways, read on to know about some of them:

● TensorFlow
TensorFlow’s list of supported programming languages also includes Java with an API. The support isn’t as feature-rich as other fully supported languages, but it’s there and is being improved at a rapid pace.

● Deep Java Library
Built by Amazon to create and deploy deep learning abilities using Java.

● Kubeflow
Kubeflow facilitates easy deployment and management of Machine Learning stacks on Kubernetes, providing ready to use ML solutions.

● OpenNLP
Apache’s OpenNLP is a machine learning tool for natural language processing.

● Java Machine Learning Library
Java-ML provides developers with several machine learning algorithms.

● Neuroph
Neuroph makes designing neural networks using the open-source framework of Java possible with the help of Neuroph GUI.

If Java had true garbage collection, most programs would delete themselves upon execution.
- Robert Sewell

Java Code Snippet Example:

Java Courses for Beginners —

● R

R was created by Ross Ihaka and Robert Gentleman with the first version being launched in 1995. Currently being maintained by the R Development Core Team, R is the implementation of S programming language and aids in developing statistical software and data analysis.

The qualities that are making R a good fit for AI programming among developers are:

● The fundamental feature of R being good at crunching huge numbers puts it in a better position than Python with its comparatively unrefined NumPy package.

● With R, you can work on various paradigms of programming such as functional programming, vectorial computation and object-oriented programming.

Some of the AI programming packages available for R are:

● Gmodels provides a collection of several tools for model fitting

● Tm, as a framework for text mining applications

● RODBC as an ODBC interface for R

● OneR, for implementing One Rule Machine Learning classification algorithm, useful for machine learning models

Used widely among Data Miners and Statisticians, features provided by R are:

● Wide variety of libraries and packages to extend its functionalities

● Active and supportive community

● Able to work in tandem with C, C++ and Fortran

● Several packages help extend the functionalities

● Support for producing high-quality graphs

Something Interesting —
Covid-19 Interactive Map made using R

● Prolog

Short for Logic Programming, Prolog first showed up in 1972. It makes for an exciting tool for developing Artificial Intelligence, specifically Natural Language Processing. Prolog works best for creating chatbots, ELIZA was the first-ever chatbot created with Prolog to have ever existed.

To understand Prolog, you must familiarize yourself with some of the fundamental terms of Prolog’s that guide it’s working, they are explained in brief below:

● Facts define the true statements

● Rules define the statement but with additional conditions

● Goals define where the submitted statements stand according to the knowledgebase

● Queries define the how of making your statement true and the final analysis of facts and rules

Prolog offers two approaches for implementing AI that has been in practice for a long time and is well-known among data scientists and researchers:

● The Symbolic Approach includes rule-based expert systems, theorem provers, constraint-based approaches.

● The Statistical approach includes neural nets, data mining, machine learning and several others.

●Lisp

Short for List Processing, it is the second oldest programming language next to Fortran. Called as one of the Founding Fathers of AI, Lisp was created by John McCarthy in 1958.

Lisp is a language for doing what you’ve been told is impossible.

-Kent Pitman

Built as a practical mathematical notation for programs, Lisp soon became the choice of AI programming language for developers very quickly. Below are some of the Lisp features that make it one of the best options for AI projects on Machine Learning:

● Rapid Prototyping

● Dynamic Object Creation

● Garbage Collection

● Flexibility

With major improvements in other competing programming languages, several features specific to Lisp have made their way into other languages. Some of the notable projects that involved Lisp at some point in time are Reddit and HackerNews.

Take Lisp, you know its the most beautiful language in the world — at least up until Haskell came along.
-Larry Wall

● Haskell

Defined in 1990 and named after the famous mathematician Haskell Brooks Curry, Haskell is a purely functional and statically typed programming language, paired with lazy evaluation and shorter code.

It is considered a very safe programming language as it tends to offer more flexibility in terms of handling errors as they happen so rarely in Haskell compared to other programming languages. Even if they do occur, a majority of the non-syntactical errors are caught at compile-time instead of runtime. Some of the features offered by Haskell are:

● Strong abstraction capabilities

● Built-in memory management

● Code reusability

● Easy to understand

SQL, Lisp , and Haskell are the only programming languages that I’ve seen where one spends more time thinking than typing.
-Philip Greenspun

Its features help improve the productivity of the programmer. Haskell is a lot like the other programming languages, just used by a niche group of developers. Putting the challenges aside, Haskell can prove to be just as good as other competing languages for AI with increased adoption by the developer community.

● Julia

Julia is a high-performance and general-purpose dynamic programming language tailored to create almost any application but is highly suited for numerical analysis and computational science. Various tools available for working with Julia are:

● Popular editors such as Vim and Emacs

● IDEs such as Juno and Visual Studio

Some of the several features offered by Julia that make it a noteworthy option for AI programming, Machine Learning, statistics, and data modeling are:

● Dynamic type system

● Built-in package manager

● Able to work for parallel and distributed computing

● Macros and metaprogramming abilities

● Support for Multiple dispatches

● Direct support for C functions

Built to eliminate the weaknesses of other programming languages, Julia can also be used for Machine Learning applications with integrations with tools such as TensorFlow.jl, MLBase.jl, MXNet.jl and many more that utilize the scalability provided by Julia.

Google Trend — Julia Interest Over Time

JuliaCon 2019 Highlights —

Conclusion

With several AI programming languages to choose from, AI engineers and scientists can pick the right one that suits the needs of their project. Every AI programming language comes with its fair share of pros and cons. With improvements made to these languages regularly, it won’t be long when developing for AI would become more comfortable than how it is today so that more people could join this wave of innovation. Outstanding community support has made things even better for new people, and the community contributions towards several packages and extensions make life easier for everyone.

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I hope you’ve found this article useful! Below are additional resources if you’re interested in learning more: —

The 7 Programming Languages & Frameworks to Learn in 2020

About Author

Claire D. is a Content Crafter and Marketer at Digitalogy — a tech sourcing and custom matchmaking marketplace that connects people with pre-screened & top-notch developers and designers based on their specific needs across the globe. Connect with Digitalogy on Linkedin, Twitter, Instagram.