How to choose the right programming language for AI
Choosing the right programming language for your Artificial Intelligence (AI) model can have a major impact on its performance and success. Numerous different types of AI models can be developed, so it’s important to choose the best one that suits your needs.
Applications using natural language processing or predictive analytics can all aid various functions like virtual chatbots and forecasting models. So, skip the most trendy or “simple” languages for one that is best suited for your project.
Understanding the Landscape of AI
AI is a diverse field of study with many subsets that all have their specific aims within the domain. It begins with Machine Learning which involves algorithms that are used to spot patterns and make decisions, self-learning in the process. Deep learning, on the other hand, is a subcategory of ML that incorporates layers between the input and output for greater processing capabilities and is normally associated with large datasets collected online.
However, there are many smaller, niche categories of AI too such as natural language processing, computer vision, and generative adversarial networks that all serve unique purposes. It is important to choose the right programming language wisely due to the large and diverse array of categories underneath the branch of AI.
Factors to Consider when Choosing a Language for AI
There are many factors to consider when choosing the right language for development. The most integral factor to consider is the purpose of your project and what you intend to accomplish. Once this has been decided there are a number of other factors to consider as well:
Performance: AI models have to handle vast amounts of data, especially if they are meant to be used online. So, identify the capabilities of your AI model, is it intended to be used in real-time, and is input/output data meant to be numerical or verbal?
Library and Framework Support: Libraries and frameworks can have a massive impact on the efficiency of your development. Libraries like TensorFlow and PyTorch contain huge amounts of pre-written code that can ease a lot of the burden.
Community Support: A thriving community can turn development from a struggle to a breeze. AI is becoming popular around the world and many communities are seeing active growth which can help you troubleshoot a lot of programming issues.
Scalability: As your project becomes more popular, you will eventually need to sell it correctly in order to keep it functioning. While most languages can help with this problem, choosing the right programming language can make this process much easier.
Learning Curve: Your proficiency as a programmer should also come into consideration. What languages are you best at using? Does your team have a specific preference? Answering this honestly can have a strong influence on the direction of your project.
Top Programming Languages for AI and Their Strengths
Some of the most popular programming choices might look familiar, but there are always others worth considering that are less well-known. Here are a few examples:
Python: One of the most popular programming languages to use, Python has a massive community and vast libraries to choose from. Its syntax is also easy for many to read too, making it perfect for general-purpose AI models.
R: This language is great for mathematical models and other logic-based systems because of its emphasis on statistics. Financial forecasting, predictive analytics, and other numerically-focused models will do well with R.
Java: Scalability is a major strength of Java and has many accessible qualities that are programmer-friendly. This language is perfect for AI models that require fast processing for real-time applications.
C++: Performance is the name of the game for C++, making it ideal for algorithms that require high-powered simulations like video game development. Its low-level memory access also makes it ideal for resource-intensive programs.
Prolog and LISP: One of the oldest languages associated with AI development, Prolog and LISP utilize symbolic logic to train AI models. While useful in the past, these languages are slightly outdated but are still useful for developing expert-based domains.
Julia: This language combines the simplicity of Python with the performance power of C++, making it ideal for more modern AI models. However, the language is still growing and communities are less common than other languages.
The Role of Domain-Specific Languages & Tools
Beyond general programming languages like Python and C++, there are also many domain-specific languages that can be considered as well. However, these are specialized languages and are written for specific purposes, so their use in general AI models might not be recommended.
SQL: Structured query language is primarily designed for managing and querying relational databases. This can be highly effective for certain Deep Learning models because of their need to manage vast datasets.
CUDA: Compute unified device architecture is a parallel computing platform developed by Nvidia that leverages GPU computation. This can be especially helpful for certain neural networks and applied to intensive models that need extra computing resources.
HLSL/GLSL: High-level shading language and OpenGL shading languages are both used to render graphics. These can be highly effective for games that use AI to render high-quality graphics or create radiant quests/dungeons.
OpenCL: Open computing language is another programming language that utilizes both CPU and GPU processing power, giving AI models more diverse computational resources.
Conclusion
Choosing the right programming language for your AI model requires more than just deciding what is easiest. Developers need to weigh a variety of factors including the purpose of their AI model as well as how it is intended to function along with the resources needed to operate it.
Fortunately, with the exponential growth of AI spreading across the world, there are huge libraries and communities present within nearly every language for developers to draw help from. So, always make sure to pick the most appropriate language for your model to ensure success.
