How To Choose The Right AI Programming Language

Artificial intelligence is transforming businesses across every industry. From chatbots to recommendation engines to inventory management, companies are finding innovative ways to use AI to engage with customers better and gain a competitive edge.

As interest in AI grows, many leaders wonder: what programming language should we use to develop our AI applications? With new languages and libraries launching constantly in this fast-moving field, it can be difficult to keep up.

In this post, I’ll help you narrow down the options and choose the right programming language for your AI projects based on your goals, resources, and constraints.

#1 Align on your AI objectives

Before evaluating any AI programming language, clarify your objectives for using AI. Do you want to develop a customer service chatbot? Enhance a mobile app with image recognition capabilities? Improve demand forecasting in your supply chain?

Understand the types of AI capabilities you want to build that will help you in programming language selection. Certain languages are better suited for some AI approaches than others.

Key AI Capabilities to Consider:

• Machine learning: Statistical models that analyze data to deliver insights, predictions, and decisions without explicit programming
• Natural language processing: Algorithms that can understand, interpret, and generate text in different languages.
• Computer vision: Image and video recognition, analysis, and generation
• Conversational AI: Human-like dialogue abilities for applications like chatbots
• Predictive analytics: Identifies patterns to forecast potential future outcomes.

#2 Take stock of your resources

Once your AI goals are clear, assess the talent and technology resources you have in-house.

For example, do you have data scientists and ML engineers on staff? What programming languages and tools are they skilled in already? Taking stock of this technical expertise and gaps will prevent you from choosing an AI programming language that your team needs to be trained to execute.

Plus, assess what coding languages may already power the systems you want to integrate AI into. Building off existing infrastructures is faster and more cost-effective than introducing a new language and platform.

#3 Weigh the pros and cons of each option

Now comes the part where you discover the top AI programming languages for your project! Here are five of the most popular current options:

Python

Pros:

• It is a very popular and versatile general-purpose language with a vast community and ecosystem of libraries and frameworks specifically for AI/ML.
• Readable syntax and designed for productivity and collaboration. Easy for non-programmers to pick up.
• Great for prototyping and MVP development. Mature tools like TensorFlow, PyTorch, Keras, and scikit-learn.

Cons:

• As a dynamic language, it can be slower in production than static languages. Solutions exist like Cython.
• It is not the best for highly complex statistical applications or where computational performance is critical.

R

Pros:

• It is specialized for statistical analysis and visualization. And has extensive packages for data science.
• Flexible with C/C++/Python integration. Customizable and extensible.
• A large community of data scientists and researchers publishing new techniques.

Cons:

• Primarily used for research, not production deployment.
• Syntax and packages can be inconsistent. It has a steeper learning curve than Python for beginners.

Java

Pros:

• Statically typed, efficient, and designed for large enterprise applications and platforms.
• It runs on any platform with robust tooling and easy integration and deployment options.

Cons:

• Verbose language without REPL environment. Not as productive for experimentation.
• Requires more code for common machine learning tasks than Python/R.

Julia

Pros:

• Fast, dynamic language specialized for technical and scientific computing.
• It is designed for parallel computing and handles big data well.
• Good for experimenting with new models and algorithms.

Cons:

• It is a relatively young language, still developing production use cases.
• Limited tools/libraries compared to more established choices and a smaller community.

JavaScript

Pros:

• It can be used for AI and ML (especially with libraries like TensorFlow.js)
• Full-stack development from front end to cloud services. Great for demonstrations.

Cons:

• It is not explicitly designed for data science or AI and requires external libraries.
• Performance impacts for complex applications and provide weak support for Math/statistics.

Lisp

Pros:

• Long history with AI and pioneered important techniques like recursion.
• Code is very flexible and extensible through macros and homoiconicity.
• Great for trying experimental approaches rapidly.

Cons:

• It has dated syntax and niche usage presently. Esoteric for beginners. Few modern libraries/frameworks are tailored for today’s applications.

#4 Determine your deployment plans

Consider the development along with how and where you plan to deploy the AI models and applications you build.

For example, if the end goal is to create a mobile app powered by AI, using JavaScript or building off Swift or Java makes sense. For a customer service chatbot on your website, Python or Lisp integrate well.

If you intend to analyze IoT sensor data with AI internally before operationalizing insights, R or Python are good choices. If the AI component will run within a larger enterprise software system, choose a language aligned with that platform.

Understand deployment early to make sure you pick a programming language suited for integration in those downstream environments.

#5 prototyping options before committing

With so many AI programming languages to choose from, it can be tempting just to pick one and dive in. However, taking the time to build prototypes first is advisable, especially for larger projects.

Prototyping involves creating basic versions of your intended AI application in different languages as a test run. While only spending a few days on each prototype, you can better understand:

• How easily does the language integrate with your technical infrastructure?
• How performant the AI models are for your use case?
• How conducive the language is to deployment in your production environments?

Prototyping reduces risk by letting you validate assumptions and uncover any blockers before launching development in a given language. It also lets your team dip their toes in and determine which language they can be most productive in.

Bring it all together

With all these considerations — your AI priorities, resources, team skills, existing infrastructures, and eventual deployments — here is a framework to guide your programming language selection:

1. Define your key AI capabilities and objectives
2. Document existing talent, tech stacks, and architectures
3. Research languages that best fit your AI approaches and weigh the pros and cons for developer experience, performance, integrations, etc.
4. Validate deployment path alignment with intended environments
5. Prototype options with spike projects to test viability

While following these steps requires some upfront diligence, it will prevent the pain of having to rewrite AI models in alternate languages. Committing to flexibility is also wise — being open to integrating multiple languages as new needs and opportunities arise.

The AI programming ecosystem continues evolving rapidly. As leaders stay focused on business goals while keeping in tune with technical advancements, they can confidently use AI to create significant value.