After remaining somewhat stagnant for decades, artificial intelligence (AI) is going through a period of powerful acceleration. With demand for AI skills more than doubling in recent years, a career in AI has become a highly attractive option for people with interest in data science and software engineering.
According to PwC’s recent report “Sizing the Prize,” global GDP is forecasted to be 14 percent (or $15.7 trillion) higher in 2030 because of AI. This makes it the most significant commercial opportunity in today’s economy.
As AI boosts productivity, product quality, and consumption, the most dramatic sector gains will be in financial services, healthcare, and retail. AI opens up a whole new world of possibilities for both enterprises and software engineers, so if you’re eager to take advantage of this opportunity, you might be wondering where you should start.
You might ask yourself questions like what’s the fastest path to a career in AI or what’s the best programming language for AI?
The answer to these questions will depend on your knowledge and experience, the type of AI project you’re interested in, and current (and future) industry trends. At the moment, there isn’t a specific AI language dedicated to this technology field, but it supports a multitude of popular programming languages.
However, to improve your chances of kick-starting a career in AI quickly, you’ll want to learn AI programming languages that are supported by several machine learning (ML) and deep learning (DL) libraries. You’ll be learning an AI language that benefits from a healthy ecosystem of tools, support packages, and a large community of programmers.
Having said all that, there still are plenty of options to consider. So let’s dive right in and take a look at the five best programming languages for AI.
When it comes to AI programming languages, Python leads the pack with its unparalleled community support and pre-built libraries (like NumPy, Pandas, Pybrain, and SciPy) that help expedite AI development. For example, you can leverage proven libraries like scikit-learn for ML and use regularly updated libraries like Apache MXNet, PyTorch, and TensorFlow for DL projects.
For Natural Language Processing (NLP), you can go old school with NLTK or take advantage of lightening-fast SpaCy. Python is the leading coding language for NLP because of its simple syntax, structure, and rich text processing tool.
However, while it’s sometimes referred to as the best programming language for AI, you’ll have to look past its five different packaging systems that are all broken down in different ways, some white spacing issues, and the disconnect between Python 2 and Python 3.
But in the grand scheme of things, it makes perfect sense to learn Python, as it boasts the most comprehensive frameworks for both DL and ML. As this highly flexible AI language is platform agnostic, you’ll only have to make minor changes to the code to get it up and running in a new operating system.
(Use this free curriculum to build a strong foundation in ML, with concise yet rigorous and hands-on Python tutorials.)
We can’t discuss the best programming language for AI without talking about the object-oriented programming language, Java. Since it first emerged in 1995, Java has grown to become a highly portable, maintainable, and transparent language that’s supported by a wealth of libraries.
Like some of the programming languages on this list, Java is also highly user-friendly, easy to debug, and runs across platforms without the need to engage in any additional recompilation. This is because its Virtual Machine Technology allows the code to run on all Java-supported platforms.
When it comes to working with NLP, it’s easy to find enough support from the vibrant community that’s built around it. As Java enables seamless access to big data platforms like Apache Spark and Apache Hadoop, it has cemented its place within data analytics-related AI development.
If you need more reasons to learn Java, consider the fact that it works seamlessly with search engine algorithms, improves user interconnections, and its simplified framework supports large-scale projects efficiently.
Whenever a task demands high-performance numerical computing and analysis, Julia (developed by MIT) will be the best programming language for AI projects. Explicitly designed to focus on the numerical computing that’s required by AI, you can get results without the typical requirement of separate compilation. Its core programming paradigm includes a type system with parametric polymorphism and multiple dispatch capabilities.
Unlike the languages above, Julia isn’t exactly the go-to language right now. As a result, it’s not supported by a wealth of libraries or a rapidly growing community.
However, as an open-source language (under a liberal MIT license), its popularity is slowly increasing. Wrappers like TensorFlow.jl and Mocha provide excellent support for DL, so there is help out there—just not the same amount as Python.
One of the primary benefits of working with Julia is its ability to translate algorithms from research papers into code without any loss. This significantly reduces model risk and improves safety.
Engaging in AI programming with Julia reduces errors and cuts costs because it combines the familiar syntax and ease of use of languages like C++, Python, and R. This negates the need to estimate a model in one language and reproduce it in a faster production language.
Haskell is a standardized strong static typing (general) language developed in the 1990s with non-strict semantics (based on the Miranda programming language).
Its popularity is primarily concentrated in academic circles, but tech giants such as Facebook and Google have also been known to use it. Haskell is used in research projects because it supports embedded domain-specific languages that play a significant role in programming language research and AI.
Unlike Java, Haskell is perfect for engaging in abstract mathematics, as it allows expressive and efficient libraries to create AI algorithms. For example, HLearn leverages common algebraic structures like modules and monoids to express and accelerate the speed of simple ML algorithms.
While you can code these algorithms in any AI language, Haskell makes them far more expressive than others while maintaining an acceptable level of performance.
It’s also an excellent host for probabilistic programming and helps developers quickly identify errors during the compile phase of the iteration. As Haskell isn’t very popular in enterprise environments, you can’t expect the same level of support enjoyed by the likes Java and Python.
While AI has only started making a significant consumer impact in recent years, research and development within this field goes back as far as the 1950s. Since the early days, Lisp has been at the heart of AI development, and that remains true today.
Some consider it to be the best language for AI because it was created by computer scientist and father of AI John McCarthy in 1958. It’s also highly suited for AI development because its unique features enable the effective processing of symbolic information.
Lisp can be described as a practical mathematical notation for computer programs. AI developers often turn to Lisp for AI projects that are heavy on ML because it offers rapid prototyping capabilities, support for symbolic expressions, a library of collection types, and is highly flexible and adaptable to their problem-solving needs.
It’s also popular among AI programming languages because it allows the easy dynamic creation of new objects, with automatic garbage collection. While the program is still running, you can also enable interactive evaluation of expressions and recompilation of functions or files concurrently.
However, in recent years some of the key features that made it special have migrated into several other languages, so it’s no longer as unique an option in the world of AI.
While it’s difficult to pick any one language as the best programming language for AI, the five listed above would likely make anyone’s top 10. Many other coding languages can be leveraged for AI, including C++, R, and Prolog, so it really comes down to you and the unique demands of the project.
If I were forced to choose one as the best programming language for AI, I would go with Python because it’s at the forefront of AI research. It’s also a language that is more or less omnipresent in ML, data science, and cybersecurity.
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