Since its inception, Artificial Intelligence (AI) has transformed nearly every industry it has touched, from transportation to healthcare and beyond. As AI continues its relentless integration into business and society, the need to actually develop and implement AI systems grows in tandem. But successfully creating AI applications requires choosing the right programming language for the task at hand.
In this comprehensive guide, I analyze the top 10 languages for AI development, outlining the unique capabilities of each and situations where they excel. Whether you‘re an aspiring AI developer or enterprise looking to leverage AI, this guide serves as an authoritative resource for selecting the ideal language in 2024 and navigating the AI development landscape.
A Brief Background on AI
Before diving into programming languages, it helps to level-set on what artificial intelligence actually is and how it functions.
At a basic level, AI refers to computer systems designed to perform tasks that typically require human cognition and perception. Using advanced algorithms and statistical models, AI can analyze data, identify patterns and make predictions or recommendations.
The two most common forms of AI are:
-
Machine learning – Systems that can automatically learn and improve from experience without being explicitly programmed to do so. Popular techniques like neural networks and deep learning fall under machine learning.
-
Natural language processing (NLP) – The ability of computer systems to understand, interpret and generate human languages like English. Powers chatbots, translation services and more.
So in summary, the overarching goal of AI is to create intelligent machines that can perform roles that previously required human beings.
Why the Programming Language Matters
Given the complexity of artificial intelligence, the programming language used to develop AI applications plays a crucial role in a project‘s success.
Choosing the right language can:
- Accelerate development and reduce costs
- Simplify the integration of different components like data pipelines and model training
- Provide access to robust AI-focused libraries and frameworks
- Future-proof projects for emerging techniques and paradigms
In short, the programming language serves as scaffolding for the entire application. Consider it carefully.
Now let‘s explore the top 10 languages AI developers should consider in 2024.
#10 – Rust
Although not the most popular option, Rust stands out as an efficient and secure language well-suited for AI systems. Rust excels in areas like security, speed and memory safety — attractive capabilities for performance-critical AI deployments.
Background
- Developed by Mozilla and released in 2010
- Advertised as a “safe” alternative to C and C++, without garbage collection pauses
- Syntax similar to C++ but major differences in memory management
AI Development Strengths
- Blazing fast performance crucial for real-time AI apps
- Automatic memory management prevents crashes/vulnerabilities
- Abstraction capabilities support complex statical modeling
Use Cases
Rust sees heavy usage in fields like scientific computing and embedded devices. It powers AI integrations for companies like Microsoft, AWS and Dropbox.
Key Libraries
- TensorFlow – Machine learning framework used across industries
- Torch-Rust – Neural networks library modeled after PyTorch
- Rocket – Web application framework for APIs
In summary, Rust‘s speed, safety and rapidly growing ecosystem make it a rising star for AI development in areas like real-time analytics and resource-constrained devices.
#9 – Haskell
Haskell offers a flexible and mathematic-oriented approach to AI programming challenges. Let‘s unravel its capabilities.
Background
- Purely functional programming language created in 1990
- Named after American logician Haskell Curry
- Promotes code reusability with abstract data types
AI Development Strengths
- Concise syntax reduces debugging workload
- Polymorphic type system catches errors at compile-time
- Referential transparency aids complex statistical modeling
- Lazy evaluation improves performance
Use Cases
Haskell is a niche but potent option for areas like compiler design, cryptography and mathematic-heavy domains.
Key Libraries
- HaskellNN – Framework for neural networks
- LambdaNet – Deep learning built on TensorFlow backend
- Grenade – Differentiable programming for gradient-based ML
For programmers with mathematical inclinations, Haskell provides an elegant paradigm for tackling advanced AI challenges through functional programming.
#8 Julia
Julia offers a dynamic approach focused on high-performance numerical and scientific computing — both pillars of modern AI.
Background
- Created in 2012 by a group of computer scientists from MIT
- Designed specifically for high-performance mathematical/scientific applications
- Integrates best parts of Python, R, MATLAB, Perl and Lisp
AI Development Strengths
- Just-in-time (JIT) compilation enables excellent runtime performance
- Dynamically typed but can indicate types for more error checking
- True macros and metaprogramming capabilities
- Generate and execute code dynamically
- Built-in package manager
Use Cases
Ideal for data science, machine learning and other technical computing projects. Used across sectors like aerospace, energy and material science.
Key Libraries
- DifferentialEquations.jl – Differential equation solving
- Flux – Deep learning library for Julia
- MLJ – All-purpose machine learning framework
- Evolutionary – Genetic algorithms for AI optimization
For number-crunching AI tasks, Julia’s mathematical backbone offers a robust, high-performance approach worth considering.
#7 – R
When tackling AI projects steeped in statistics, look no further than R. Though not the strongest full-stack option, R excels at statistical analysis.
Background
- Open-source programming language focused on statistical computing
- Initiated in 1990s based on predecessors like S language
- Heavily used for data analysis across academia and industry
AI Development Strengths
- Access to wide range of robust statistical and modeling techniques
- Powerful data visualization capabilities
- Large ecosystem of domain-specific packages
- Inherently designed for data exploration tasks
Use Cases
Ideal for statistical analysis, predictive modeling/forecasting and data mining applications. Heavily leveraged in quantitative finance, bioinformatics, and social science research.
Key Libraries
- Caret – Simplifies the model building process
- dplyr – Fast data manipulation and transformation
- ggplot2 – Sophisticated data visualization capabilities
While lacking some full-stack development capabilities, R’s fine-tuned statistical arsenal makes it a top choice for multiple pillars of AI like predictive analytics and data science.
#6 – C++
As one of the most widely-used languages globally, C++ offers a potent low-level approach for AI development.
Background
- Originally created in 1983 by Bjarne Stroustrup
- Built as extension of C with object-oriented principles
- Provides efficiency of C with features like classes, abstraction and exceptions
AI Development Strengths
- Excellent runtime performance efficiency
- Ability to directly access hardware and memory
- Vast libraries for nearly any application domain
Use Cases
C++ is leveraged extensively in fields like operating systems, game engines, desktop apps and performance-critical computing. It powers AI integrations inside tax software, antivirus programs, search engines and more.
Key Libraries
- TensorFlow – C++ frontend for Google‘s TensorFlow machine learning framework
- Caffe2 – Facebook’s deep learning framework
- MLPack – Scalable C++ machine learning library
For low-level AI programming interfacing with hardware or needing high performance, C++ remains a dominant, battle-tested choice after 40 years.
#5 – Lisp
The oldest language on this list, Lisp pioneered multiple AI concepts and remains relevant decades later.
Background
- Born at MIT in 1958, one of oldest high-level languages
- Heavily used in early AI research, including expert systems
- Unique fully parenthesized prefix notation syntax
AI Development Strengths
- Reflection and metaprogramming techniques
- Symbolic differentiation eases calculus operations
- Garbage collection automatic memory management
- Linked list data structure built-in
Use Cases
Historically used for early AI research and knowledge representation. Modern applications in natural language processing, machine learning and robotics.
Key Libraries
- SymbolicC++ – High performance symbolic computation
- Clasp – ASP solver for answer set programming
- DeepLearning4j – Distributed deep learning framework
While not as widespread today, Lisp’s longevity and influence in early AI earns it a spot as a viable option for select applications.
#4 – Prolog
When it comes to logical reasoning, few languages approach Prolog. Let‘s analyze its AI capabilities.
Background
- Designed in 1972 for natural language processing
- Based on logical programming that models facts/rules like human reasoning
- Declarative syntax focused on defining objective vs. steps
AI Development Strengths
- Excellent for knowledge representation tasks
- Learning algorithms use existing facts/rules to derive new ones
- Relationships mapped efficiently as “facts”
- Embed logic queries directly
Use Cases
Prolog facilitates intricate knowledge management AI systems. Utilized famously by IBM Watson to answer natural language questions by crawling knowledge bases.
Key Libraries
- TensorFlow – Interface for integrating Prolog-based systems into neural networks
- DeepProlog – Prolog-based framework for deep learning
Where precise knowledge formalization, storage and querying is critical, Prolog offers a robust paradigm for crafting AI systems that mirror human logic.
#3 – JavaScript
Given its ubiquity across the web, JavaScript creates intriguing opportunities for AI development.
Background
- Created originally in 1995 to add dynamic capabilities to static web pages
- Found today on over 97% of websites globally
- Can execute applications on both server and browser environments
AI Development Strengths
- Frontend and backend development in one language
- Simplifies architecture
- Event-driven model fits well for user interactions
- Can leverage GPU hardware acceleration
Use Cases
JavaScript lowered barrier for web-based AI experiments. Enables interactive demos/visualizations hosted online to explain machine learning models. Also used to embed intelligent analytics in web apps.
Key Libraries
- TensorFlow.js – Run ML models directly in browser
- Brain.js – ML library focused on neural networks
- Natural – NLP library for node.js environment
Given its front-end focus, JavaScript facilitates captivating opportunities for interactive AI experiments and analytics embedded directly into interfaces users already know.
#2 – Java
Boasting rock-solid enterprise adoption, Java brings industrial-strength support for large-scale AI.
Background
- Created originally in 1995 by Sun Microsystems
- Built to have fewer implementation dependencies – “write once, run anywhere” ethos
- Now open-source ecosystem maintained by Oracle
AI Development Strengths
- Faster runtime performance versus interpreted languages
- Widespread usage drives wealth of stable open libraries
- Static typing catches errors before execution
- Backed by well-established community
Use Cases
Java is the backbone for major big data and ML platforms used by Fortune 500 companies worldwide, including those offered from AWS, IBM and Microsoft Azure.
Key Libraries
- DL4J – Distributed deep learning framework
- OpenCV – Image processing and computer vision algorithms
- Weka – Data mining and machine learning toolkits
- Mahout – Algorithms for recommendation systems
For enterprise-scale AI systems needing robustness and reliability, Java and its powerful ecosystems prove a safe choice packed with potential.
#1 – Python
Python remains the dominant, flexible language used by nearly all AI practitioners today. Let‘s review its capabilities and ecosystem.
Background
- First appeared 1991, designed for simplicity and readability
- Dynamically typed interpreted language
- Has surged in popularity within technology and data science circles
AI Development Strengths
- Huge collection of ML/data science libraries
- Dynamic flexibility, interpretability and scripting
- Intuitive syntax to learn, interface and maintain
- Interoperability with platforms like C++ and Java
Use Cases
A vast amount of today’s AI innovation happens in Python – from algorithms research to product development and deployment. Dominates academia and industry currently.
Key Libraries
- TensorFlow/Keras – Leading neural network/deep learning libraries
- NumPy/SciPy – Foundational math/engineering capabilities
- Scikit-learn – Core machine learning toolkit
- NLTK – Natural language processing library
Boasting excellent flexibility for experimentation paired with advanced packages for every AI need, Python delivers a potent formula that fuels modern AI proliferation.
Compare Languages by AI Application
| Application | Recommended Languages |
|---|---|
| Robotics, embedded systems | C++, Rust |
| Hardware optimization | C++, Rust, Java |
| Web development | JavaScript, Java, Python |
| Mathematical models | Julia, Python, R |
| Large enterprise systems | Java, C++ |
| Visualizations, BI analytics | Python, R, JavaScript |
| Statistical analytics | R, Python |
| Knowledge representation | Prolog, Java, C++ |
| Scientific computing | Python, Julia, R |
| App prototyping, MVPs | Python, JavaScript |
The Future of AI Programming Languages
Looking ahead, Python will likely continue its reign in the near-term given its foothold within AI research and education. Supporting libraries like TensorFlow continuously expand Python‘s capabilities to cover more complex applications like computer vision and NLP. Easy integration with supplements like R and Java extend functionality further.
However, expect to see languages like Julia and Rust capture more mindshare as their ecosystems evolve to address shortcomings around enterprise tooling and scalability. Julia in particular shows promise on highly technical fronts like differential equation solving and quantum machine learning.
Overall the future remains bright for a diversity of programming languages powering AI development. Just be sure to carefully weigh your options and match each language‘s strengths to your project‘s needs.
Starting Your AI Programming Journey
Hopefully this guide has shed light on languages of choice for AI development in 2024 and provided some signposts to begin upskilling. For those looking to get started:
- Python – highest recommend entry point given simplicity and supportive community for beginners
- JavaScript – lower barrier entry for web-centric explorations and visualizations
- R – excellent gateway for statistics-focused data science pathways
- Java – Scalable option for systems-focused software engineers
- C++ – performant choice for those with prior lower-level software skills
No matter where you begin, learning the fundamentals around working with data and training basic machine learning models will start unlocking AI concepts quickly.
Choose your language adventure and start building the future of AI, one line of code at a time. The potential remains boundless.
