Are High or Low-Level Languages Preferable for Creating AI Applications?

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Are High or Low-Level Languages Preferable for Creating AI Applications?

Python is one of the most popular languages for AI programming. However, it is not necessarily the best.

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I am often asked what the best programming language is for creating artificial intelligence applications. New programmers also often ask if Python or TensorFlow are the best for these kinds of applications.

It is true that Python is the preferred language of many AI developers. However, it is not better at its core. There are many instances when it is better to use a low-level language instead. The main reason developers prefer Python for AI applications is its convenience and efficiency. The quality of the applications is usually not as high.

Does this mean that developers are wrong to use Python over C or other low-level languages? No. There are different factors that need to be weighed. For many applications, Python or other high-level languages could be fine. However, for developing highly sophisticated applications like Forex trading instruments, low-level languages might be necessary.

The truth is that all programming languages have their own advantages. There are some instances when high-level languages are best for developing AI applications. There are also situations when low-level languages are preferable. You need to understand the nuances of each, so you can decide which is best for your AI application.

Understanding the Core Objectives of Your AI Application is Essential to Choose the Right Language

Every AI programmer needs to outline their principle objectives before selecting a language. Before you can make this determination, it is important to understand the building blocks of any AI project.

AI is not an easy concept to explain. AI is not a set of instructions, nor is it a new type of data. It consists of a series of methodologies in which we can provide our developments with behaviors perceived as intelligent. In short, they are methodologies, and not instructions.

The nature of some of these behaviors are more intricate than others. You need to understand the scope of the behaviors that you are trying to emulate before settling on a high-level or low-level programming language.

What programming languages are there?

There are a lot of programming languages out there. The TIOBE list has shared some data on the most popular, which are listed below in order of popularity:

  • Java                       
  • C
  • Python
  • C++
  • Visual Basic .NET              
  • JavaScript
  • C#           
  • PHP
  • SQL
  • Objective-C

Any of the programming languages on this list could be perfectly valid for developing AI applications, (except probably the SQL language, which is oriented towards database queries rather than application development). But which one is ideal for creating an AI project? The answer is going to depend on the nature of your project. Countless AI programmers swear by Python (a general purpose, high-level language), but there are instances when low-level languages are more suitable.

You should be aware of the different language level classifications before adapting one for your AI project. We could classify these languages in three main groups:

  • Low level languages. These languages have better performance, but the programmer must make many decisions to optimize the execution of the program. Since the programmer has to write more intricate code, the probability of making mistakes is higher. Examples of these languages are C, C++ and Objective-C.
  • High level languages. The languages offer a series of abstractions, so that the compiler himself makes some decisions. This frees the developer from making them. Unfortunately, the tradeoff is that these languages are usually less efficient in the execution, since the decisions that are automatically taken by the compiler are not usually as intelligent as those taken by a human being. However, since the developer has to make fewer decisions, the chances of compiler errors is much lower. Examples of these languages are Python and Javascript.
  • Intermediate level languages: We find within this group those languages that seek an optimal balance between performance and difficulty. In this way, they put many decisions in the hands of the programmer user, but at the same time they offer a series of facilities and tools to prevent and fix code errors. An example of this type of language is Java.

So, which languages are ideal for AI projects? Before choosing a high-level or low-level language, you need to analyze the nuances of the project. Python tends to be a popular language for AI projects. However, it may not be robust enough to handle really cutting-edge AI applications.

Therefore, the first factor to evaluate is performance. You probably want all AI applications run as fast as possible. This criterion is important, since many artificial intelligence applications must work and provide results in an acceptable time, otherwise our applications could be considered intelligent, but slow (and therefore could be perceived by the end users as "not so intelligent" applications, even though speed is not directly related to the quality of the results).

Of course, expediency is more of a priority in some applications than others. If you want to prioritize speed of execution, you should probably choose a low-level language, such as C or C++.

Obviously, in this case, since the very learning and implementation of artificial intelligence methodologies in a specific language is complex, we would have to have a fairly fluid starting level in these languages.

Another possible criterion to look at the ease of use. This is a greater priority for less experienced programmers. Less experienced programmers are only concerned with the complexities derived from the implementation of AI, rather than the complexities of the programming language itself. This is the reason why languages like Python are so famous in training courses related to this field, contrary to the popular belief that it is due to their power, it is because of their ease and flexibility.

You will observe a noticeable difference in the speed of executions between high-level and low-level languages. For the same calculation, low-level languages like C take between 24 and 25 seconds to complete their task, while high level languages like JavaScript take 38 seconds (which means they are approximately 52% slower), but other languages like Python can take up to 157 seconds (which means they are about 650% slower than low level languages). Therefore, these languages are not chosen for their performance but for their ease of use.

Don’t Discount the Value of Low-Level Languages for AI

Python is one of the most popular languages for AI programming. However, it is not necessarily the best. Low-level languages actually create faster and more powerful applications. At the same time, there are plenty of instances when the compiler speed is not a priority, so Python and other high-level languages are perfectly feasible. You need to outline the priorities of your application before choosing one.

ai, ai artificial intelligence, language basics, python

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