Businesses in search of accelerated growth have long acknowledged that Artificial Intelligence (AI) is an expanding domain. AI can be leveraged to automate and improve repetitive tasks, which can lead to enhanced efficiency and cost reduction for your enterprise. With automation through AI, outcomes tend to be quicker, more precise, and consistent, surpassing any manual labour-related results.
When analyzing data and making decisions based on multiple sources of information like user feedback and datasets, AI outnumbers human capabilities in terms of speed. AI can be integrated in numerous shapes, including analytical tools, chatbots, and mobile and online apps to reveal patterns and generate better solutions. To sum up, AI can enhance various aspects of our lives.
Competent software engineers are vital for any enterprise that is considering including AI in its products and services. Moreover, they should have proficiency in the most efficient AI programming languages. Explore our website to find skilled software engineers who can help your business realize its AI aspirations.
What programming languages are accessible to facilitate the integration of Artificial Intelligence (AI)? Check out some of the commonly used languages to accomplish the goal.
Python
Since its inception, Python has been a top favourite language for Artificial Intelligence (AI), even before it became a requirement for businesses. It is considered to be the go-to for Machine Learning, which is a component of AI, and its roots and triumph with big data have contributed to its widespread acceptance in AI research and advancement.
Python has earned a prominent spot in the current technology landscape primarily due to the availability of dedicated AI frameworks tailored for this programming language. Developers typically employ TensorFlow, an open-source toolkit specifically created for machine learning, to train and execute deep neural networks. Other AI-oriented frameworks are also available, including:
- scikit-learn is a tool utilized to build and operate machine learning training models.
- Python library PyTorch enables image and text processing.
- Keras – furnishes a coding interface for mathematical operations performed at a high level.
- The Theano library allows you to define, optimize, and assess mathematical expressions.
Python is also considered to be one of the most user-friendly languages to learn and employ.
Lisp
Since its debut in the 1960s, Lisp has been leveraged to accomplish significant milestones in scientific research, specifically in the domain of natural language analysis, theorem proving, and attempting Artificial Intelligence (AI) challenges. Initially created as a potent mathematical notation for programming, nowadays, AI programmers exploit Lisp widely.
Lisp is the second oldest programming language still used today and considered integral for the successful implementation of any Artificial Intelligence (AI) endeavour. It offers a multitude of capabilities that are indispensable for the development lifecycle.
- Rapid prototyping.
- Creating on-the-go.
- Waste collection is required by legislation.
- Data structure-based programs can be executed.
- Computer code can be perceived as a form of data and edited similarly.
- Utilizes recursion as a control mechanism, as opposed to iteration.
- Significant capabilities for symbolic data processing.
- The iterative process of “reading,” “evaluating,” and “printing” can streamline the creation of interactive programs.
As a result of John McCarthy’s substantial impact on the Artificial Intelligence domain, numerous of his notions have been executed throughout the years.
Java
Java is recognised as a crucial language for the advancement of AI. Its extensive use in mobile application development is a significant contributing factor. Given the ubiquity of AI in mobile applications, it is a suitable option.
Besides its compatibility with TensorFlow, Java also provides a variety of libraries and frameworks concentrating on AI, including:
- Deep Java Library is a library developed by Amazon that enables the execution of deep learning tactics.
- Kubeflow – facilitates the management of Machine Learning stacks on Kubernetes.
- OpenNLP is a natural language processing tool constructed on Machine Learning. (source)
- The library for Machine Learning in Java comprises several diverse algorithms. (source)
- Developing neural networks is easy with Neuroph.
Java presents efficient debugging techniques, an intuitive syntax that permits graphical data presentation, and a fusion of WORA and Object-Oriented design principles, among other characteristics.
C++
C++ has been in existence for quite some time and still retains its significance in the AI domain. Its adaptability plays a significant role in this, as it is appropriate for jobs that demand substantial system resources. C++ is a low-level language that enables effective management of AI models in production. Despite not being the most favoured language among AI developers, there is no denying that several machine learning and deep learning libraries are built in C++.
Furthermore, C++’s effectiveness and productivity are unrivalled since it automatically translates user-generated code into machine-readable code.
R
Although R might not be the ideal language for Artificial Intelligence (AI), it is more efficient than Python for extensive numerical computations. Additionally, R is a convenient language for AI due to its support for functional programming, vector computing, and object orientation.
Several AI-focussed packages are also accessible in R.
- gmodels – provides an extensive collection of model fitting tools.
- When we say “TM,” we are referring to a framework for text mining software applications.
- RODBC, which is an ODBC bridge.
- The OneR package provides access to the One Rule Machine Learning classifier.
Julia
Julia is a relatively new programming language that concentrates on efficient computing in scientific and technical areas. It also offers several AI-related capabilities.
- Number storing formats that are frequently utilised.
- Levels of precision that are entirely subjective.
- Algorithmic resilience.
- Comprehending one’s code through the utilisation of tuples and dictionaries.
- Integration with a package manager.
- The type system is flexible.
- Being capable of operating in parallel and distributed computing environments.
- The capability to utilise macros and metaprogramming.
- Successive dispatches are permitted.
- Incorporated C function support.
Julia can be utilised along with packages such as TensorFlow.jl, MLBase.jl and MXNet.jl.
Conclusion
If your organisation wishes to implement AI, it is advisable to integrate one or more of these programming languages as early as possible. With a proficient development team, AI can offer significant advantages to your enterprise.
