What Programming Languages You Should Learn to Be an AI Engineer?

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Artificial intelligence (AI) continues to transform the world dramatically; it is not just improving business processes but also reshaping entire sectors. The demand for skilled AI/ML developers who can design and deploy these intelligent systems is rising as more companies attempt to integrate AI into their operations. Do you wish to work in the cutting-edge AI sector or are you a newcomer interested in machine learning and artificial intelligence but unsure about which programming language to learn?

This article explores the top 7 programming languages that give AI developers/engineers the comprehensive know-how to fully utilize AI. We'll go over each language's advantages and disadvantages as well as the best applications for them in the field of AI development.

7 programming languages to learn to be an AI engineer

Here’s a list of top seven programming languages that are used extensively in AI/ML engineering.

1. Python programming language

Python is an open-source powerhouse and the most often used language for AI programming. Its high-level syntax, vast libraries, and lively community make it a great choice for both new and experienced developers.

Python has a robust ecosystem of machine learning libraries, including TensorFlow, PyTorch, and Scikit-learn. These libraries include pre-built functions and algorithms that speed up the building of ML models, allowing developers to focus on the core logic and problem-solving components of AI.

Python's capabilities in data manipulation and analysis make it ideal for preparing and cleaning huge datasets, which is an important step in training and assessing AI models. Libraries such as Pandas, Polar and NumPy provide strong capabilities for data manipulation and numerical computing.

Also Read: Latest developments in the AI sector to keep an eye on

2. Java

AI development also heavily relies on Java, a popular language renowned for its object-oriented capabilities and durability, particularly in the context of mobile applications. Java, the main language used to develop Android apps, makes it easy to integrate AI features into mobile apps.

Libraries like TensorFlow Lite enable developers to deploy trained machine learning models on mobile devices, simplifying on-device AI processing.

Because of Java's enormous popularity, the developers’ community always remains vibrant and helpful in the long run.

Java's virtual machine guarantees reliable performance on a variety of hardware configurations. Its object-oriented design also encourages modular coding, which enables the scaling and maintenance of AI-powered mobile applications.

3. Lisp

Lisp, a venerable language dating back to the 1960s, has an important role in AI history. Lisp excels at manipulating symbols and data structures, which is essential for AI tasks such as natural language processing and knowledge representation.

On top of that, Lisp allows code to be viewed as data and data as code, resulting in a versatile and expressive programming paradigm ideal for AI research. Plus, Lisp's built-in recursion support makes it easier to develop algorithms that use repetitive problem-solving strategies, which are common in AI applications.

Also Read: 5 online AI communities to know

4. C++

C++, a long-standing programming language noted for its speed and efficiency, is essential in certain areas of AI development, especially when performance is critical.

C++ is a compiled language that enables close-to-the-metal programming, resulting in extremely efficient and performant programs. This makes it perfect for computationally heavy activities such as deep learning algorithms and real-time artificial intelligence applications.

C++ provides fine-grained control over both memory management and hardware interaction, allowing developers to optimize code for specific hardware setups, hence increasing performance.

Finally, the coding language contains a wide ecosystem of libraries, many of which are specifically developed for scientific computing and numerical operations. This existing codebase can be used to expedite AI development for emerging brands and startups.

5. R

R is a computer language and software environment created primarily for statistical computing and graphics. It has become a favorite among data scientists due to its proficiency in data analysis and manipulation, making it a useful tool at many phases of the AI development pipeline.

R excels at data exploration, which is a vital first phase in understanding the characteristics and patterns of a dataset. It also excels in model fitting and statistical learning and allows data scientists to try out different algorithms and evaluate their success.

6. BigQuery SQL

BigQuery SQL is a variant of SQL, a query language built primarily for interaction with relational databases. In the context of AI research, BigQuery SQL enables data scientists and engineers to efficiently extract, transform, and load (ETL) huge datasets from Google's BigQuery, a large-scale data warehouse service.

BigQuery's ability to handle petabytes of data makes it a game changer for AI projects that need to process and analyze massive amounts of data. BigQuery SQL enables users to query, filter, and aggregate data at scale, either for further research or directly into machine learning models. This rapid data retrieval and manipulation is useful for training and testing AI models since the quality and relevance of data have a substantial impact on the models' performance.

Also Read: Important AI journals to follow

7. Scala

Scala is a general-purpose, object-oriented programming language noted for its ability to manage large-scale projects while maintaining a high level of expressiveness. It has gained popularity in the field of artificial intelligence due to a number of qualities that are well-suited to the demands of machine learning and advanced AI research.

Scala's functional programming paradigm encourages code reuse and modularity, which are both critical components in developing and maintaining large-scale AI systems. Scala treats functions as first-class citizens, making it easier to compose and manipulate code blocks, which is very useful when working with sophisticated machine learning algorithms.

Moreover, Scala's Spark framework provides a solid foundation for distributed computing. This allows developers to use machine clusters for large-scale data processing and model training, which is an important feature of modern AI development as datasets can grow to be massive.

LanguageStrengthsWeaknessUsabilityTechnologyExamples of AI Apps
PythonSimple syntax, vast libraries, productivePerformance issues, GIL for threadingHighMachine learning, computer vision, NLPSiri, Alexa, Tesla Autopilot
RStatistical modeling and analysis built-inNot general purpose, steep learning curveModerateStatistical analysis, data miningIBM Watson, Google BigQuery ML
JavaCross-platform, enterprise-ready, static typingVerbose, not AI specializedModerateSearch algorithms, NLP, enterprise AIGoogle Search, Cortana, Banking APIs
C++Speed and efficiency, hardware controlComplex, systems programmingHardOptimized ML apps, roboticsStock trading systems, self-driving cars
LispSymbolic AI, code as data constructsNot widely used today, limited librariesModerateEarly AI research, pattern recognitionSPIKE, the planning and scheduling application for the Hubble Space Telescope
ScalaLeverages Java, functional capabilitiesSteep learning curveModerateML on Spark, distributed systemsReal-time cybersecurity solutions

That’s a wrap

This article covered the top seven languages utilized in major AI applications such as machine learning, deep learning, computer vision, and NLP. Python, R, Java, C++, and Scala are all popular programming languages used in industry and research.

If you want to work in AI engineering but have no programming experience, start with a boot camp or online course. If you have anything to add to this article, please contact us.

Additional References

7 AI programming languages to know

The 13 essential AI engineering skills to know