Which programming languages will have the greatest future impact on coding? What you ask is the determining factor. Things are constantly evolving. Some languages, like Java and R, continue to be popular. Other languages that were popular just a few years ago have cooled a little, pushed out of the spotlight by newer options that make it easier to solve the kinds of problems that industry programmers face today.
Some things have changed, while others have remained the same. One thing is certain: language creators will continue to play the most important role in workflow automation. Here’s our list of the top programming languages that will define the future of coding (at least for the time being).
1. Ethereum
Many people consider Ethereum and bitcoin to be currencies, something you can use to buy coffee or perhaps anything illegal. However, beneath the hype and greed, there is a programming language buried therein that can describe fairly complex computations. Even if you accept that the nodes may impose a time limit on how long your code runs, the Ethereum language is Turing complete.
The Ethereum architects anticipated that anyone who used the currencies would want to do more than merely transfer money from one owner to another. They reasoned that the parties could wish to write complicated contracts with codicils, disclaimers, and conditions about when the wealth should transfer or not move and that these would be written in computer language.
For example, a 30-year mortgage could be expressed as a code that tallies monthly payments and only eliminates the lien after 360 payments have been made. These code-based contracts are the future of economics and commerce, and knowing the languages will be critical in structuring transactions in the future.
Highlights: Tightly connected with the monetary system, which necessitates a high level of accuracy and verification.
Headaches: The monetary system is tightly intertwined, resulting in regulation and paranoia.
2. React JSX
Although React web apps are written in JavaScript, many of the templates are built-in JSX, a strange combination of JavaScript and HTML designed by someone who was well-versed in both.
To save time, some of the vexing punctuation marks have been removed, while other structures have been simplified. You’ll need to understand this mix if you’re planning to write in React or one of its cousins, such as Vue or one of the future generations of web apps that also employ JSX.
This necessitates knowing when to use JavaScript and when to use HTML. It’s so dissimilar that it might as well be a foreign language.
Highlights: Using the shorthand, you can quickly create intricately interconnected web programs.
Headaches: You’ll be continuously perplexed because it’s neither HTML nor JavaScript.
3. TensorFlow
TensorFlow is one of the most popular open-source programs for experimenting with machine learning. TensorFlow is written in a blend of C++, Python, and the CUDA programming language. However, the majority of the code you write will be called the Tensor Flow APIs, which can be done in most major languages, including Java and JavaScript.
You can use whatever syntax and compiler you like, but the difficult part will be teaching your code to learn from your data. You must understand how to construct your model, and your code must reflect your expectations of the system. The first step in becoming a genius is to comprehend the API’s structure.
Highlights: At your fingertips, real capability to solve complex AI challenges.
Headaches: The outcomes are typically eerie black boxes that leave you perplexed as to what they’re doing.
4. TypeScript
It has a similar appearance to JavaScript and is intended to perform similar functions in browsers and Node.js apps. During the build process, a transpiler converts it to JavaScript. So, what’s the big deal?
Many programmers appreciate TypeScript’s typed safety, which ensures that no operation results in undefined behavior. This is the type of typed safety that has helped keep Java code cleaner and more specified. The developers didn’t make any significant changes to JavaScript here; instead, they introduced an extra layer that verifies that the data types line up, allowing you to catch some of the most common problems before the code executes.
It’s the foundation for the popular Angular framework, and it’s gaining popularity since you can use it almost everywhere that accepts JavaScript.
Highlights: It compiles JavaScript, allowing it to be used in a variety of situations.
Headaches: Not everyone appreciates the extra effort required to write well-typed code.
5. R
Although the terms “big data” may no longer elicit the same level of enthusiasm in a sales presentation as they did a few years ago, the necessity for statistics continues to drive many corporate choices. In fact, the techniques are more interconnected than ever before, making R, a statistical analysis language, more vital than ever.
R is a computer language created by statisticians and scientists to help them do their jobs more efficiently. Many of the most useful statistical techniques are already implemented as publicly released libraries, and it comes with most of the common functions used in data analysis. It contains the majority of the tools that data scientists require to do data-driven research.
Many developers use R inside an IDE as a high-powered scratchpad for playing with data, despite the fact that it’s a programming language for constructing arbitrary functions. As a result, it resembles a cross between a spreadsheet and a graphing calculator.
RCommander and RStudio are two popular front ends for loading data, chewing it up, and spitting it out as charts and tables. They turn it into a more interactive world where you can do your work rather than a compile-and-run language.
Highlights: Ingenious expressions for selecting and studying a subset of data.
Headaches: It’s mostly geared at desktop computers, rather than the world of big data, where technologies like Hadoop reign supreme.
6. Java 8
When the Java team looked around the Java Virtual Machine (JVM) world a few years ago, they discovered that many programmers were utilizing JVM for more than just running Java. Writing code in Scala, which combines all of the strength and discipline of a functional approach with the ability to connect some of the billions of Java archive files floating around the web, is one way to embrace this trend.
Java programmers no longer need to move to a different language to make use of the benefits of functional solutions. Java 8 is the same old Java, but with a few additional functional features to help you unleash the parallelism in your code.
Of course, parallelism is not required. You could continue to use the old Java because it is still functional. However, if you don’t employ the latest capabilities, you’ll be missing out on the opportunity to give JVMs even more structure for code optimization. You’ll miss out on the opportunity to think functionally and write code that’s cleaner, faster, and less buggy.
Highlights: Lambda expressions and concurrent code.
Headaches: A bolted-on feeling makes developers want to jump in with both feet and use Scala.
7. Go
Go is designed for today’s environment, where much of the intelligence resides on servers juggling thousands of clients and millions of API requests. It’s designed to be quick and light, with just enough functions to get the job done without getting in the way with too many more options.
The language is sparse—which is precisely the goal. Google was looking for a solution that was incredibly simple. It discarded many of the more sophisticated ideas found in other languages, keeping only enough to be “simple enough to store in one programmer’s head,” as one investor put it.
In Go, there are no complicated metaprogramming structures or elaborate abstractions—just the most crucial characteristics given in simple grammar.
This can make things easy for everyone on a team because no one has to worry if someone else comes up with a great idea from the language specification’s far reaches.
Highlights: It’s just a clean, straightforward data manipulation language.
Headaches: There are instances when you require a creative function that isn’t available.
8. CUDA
Originally, video cards were nothing more than specialized silicon for juggling millions of triangles so that someone could fantasize about avoiding death, facing danger, and blasting both death and danger with dozens of sophisticated weaponry.
However, somewhere along the way, some astute programmers peeked behind the hood and spotted an opportunity to speed up their code by taking advantage of gaming’s sophisticated processing capacity.
Many video cards today can simulate complex scientific models or perform advanced AI algorithms. If you have a lot of computing demands, the CUDA programming language is a fantastic way to put the power of your graphics processing units (GPUs) to use for things other than shooting zombies and robots. To solve your problem, you can even rent racks of them in the cloud.
Learning to identify the parallel sections of your code is the most difficult aspect of using CUDA. Once you’ve located them, you can use the CUDA code to speed up these parts by utilizing the video card’s natural parallel power.
Some jobs, such as bitcoin mining, are rather straightforward, but others, such as sorting and molecular dynamics, may necessitate a little more thought. For massive, multidimensional simulations, scientists prefer to use CUDA code.
Highlights: Extremely fast performance, at least when it comes to parallel coding.
Headaches: It’s not always easy to figure out which areas of code can be easily parallelized.
9. YAML
Most programmers would tell you that YAML is not a programming language, but rather a markup language, and “yet another.” However, as more layers of code are controlled by the parameters specified in these once-trivial files, you’ll see YAML configuration files and their cousins, formatted in JSON, in more places.
They may be pawns in the programming game, but anyone trying to get their Node.js stack to work will spend a lot of time obsessing about keystrokes in their YAML files. Even if such work isn’t technically programming, the placement of those characters can cause you to pull out just as much hair.
Highlights: The format is mostly punctuation-free, but it’s rich enough to express a lot of information.
Headaches: Beyond the basic format, the structures and standards might differ dramatically.
10. DSL
The acronym DSL was once used to refer to Internet service provided by a telephone provider. For developers, however, it’s evolved to imply “domain-specific language,” a broad, nebulous term that’s cropping up everywhere. The snag is that the letters don’t correspond to a specific language that needs to be learned and mastered.
DSL is based on the idea that a language should be suited to the task at hand rather than attempting to be a general-purpose language that can be used anywhere. For example, a language for a theater lighting controller might include a keyword for turning the lights on and off, but no additional for writing data to a file or connecting to the LAN. Another language developed to manage a steel mill might be able to communicate with the blast furnaces, but not with an API for converting coordinates into maps.
Designers are discovering that user needs are too complex for a few menus, checkboxes, or command-line parameters, which has led to the development of these DSLs. Users of the DSL wish to express their wants in several lines by stringing domain-specific keywords together and providing domain-specific facts.
Unfortunately, each DSL is unique, which is kind of the idea. The DSL concept isn’t made any easier to understand by the shared acronym. You won’t be able to study for a universal DSL certification, but you should expect to see them more frequently in the future.
Highlights: Brings real power to many applications.
Headaches: As different as the various domains.
A continued focus on workflow
The same programmers who are reimagining stores, hospitals, and automobiles with computers are also reimagining their own occupations. They’re speeding up the process of producing software by layering on additional sophistication, making tools faster and wiser at catching errors.
The latest computer languages are easier to learn because they have built-in intelligence that allows the programmer to accomplish more with fewer keystrokes. They also tend to deliver fewer bugs.
More structure and abstraction are employed in the newer techniques, allowing the languages’ guts to do what programmers used to have to do individually. These automatic features provide developers more time to focus on the more important challenges. They also produce superior performance in many circumstances since automated mechanisms are better equipped to uncover chances for efficiency and parallel computation while avoiding some of the common pitfalls.
Some of this automation is based on designs and ideas that are making their way out of the lab and into the real world. Artificial intelligence (AI) programmers used to create code in their preferred language. Some libraries, such as TensorFlow, are now stable enough to be extensively used, and their API functions practically as a separate language.
Programmers are experts at stringing together API calls, much like they are at stringing together loops or functions.
Some of the “languages” on this list may or may not be regarded as official computer languages. Programmers frequently distinguish between a comprehensive computer language that allows them to describe any arbitrary algorithm (commonly referred to as Turing complete) and a smaller set of characters that allows them to spell out only a few parameters or setup information.
However, the distinction can be philosophical, and it may be relevant solely in academic discussions and barroom debates. Because sometimes all that stands between you and your destination is if the right characters are in the proper location. When you’re ripping your hair out trying to get everything to do the proper thing, it doesn’t matter if your keystrokes are expressing some grammar worthy of being dubbed a full language.
These ten languages are revolutionizing the way we instruct computers. Some of these languages are quite new, while others are already well-known. Some aren’t actually languages at all, despite the fact that we conceive of them as such.
It’s all about doing more with less
The items on this list have little in common except that programmers are gradually figuring out how to make each keystroke do a little more work on average. They’re condensing the languages and producing more concise and powerful code as a result. In certain circumstances, the languages serve as a springboard to whole new possibilities, such as artificial intelligence or massively parallel algorithms.
It’s not quite full automation, but these languages help programmers accomplish more in less time.
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