JavaScript and AI: Is JavaScript a Good Programming Language for Creating Artificial Intelligence?

Artificial intelligence and machine learning are two main technological trends of the year and, quite possibly, an entire decade. The prominence of AI has been underscored by the extensive coverage they have received in newspapers, radio, YouTube, and television, offering thousands and thousands of points of view regarding AI’s impact on our future, labor market, or unseen technology. However, neither AI’s extreme popularity nor the hype around the theme in the media aren’t the subject of this article. Today, we will delve into how AI works from the perspective of a software engineer.

As a part of a professional JavaScript development services company, we want to focus on what excites us the most and what we understand the best. Today, we will check whether building AI with JavaScript is a good idea and how one can use JavaScript for machine learning.

Introduction to AI in JS 

Let’s leave aside the headlines. AI has already impacted the world of any developer working with JavaScript, be it a React front-end architect or Next.js back-end specialist. Nowadays, AI helps developers write code, detect bugs, and optimize the performance of the development process, while AI-driven analytics simplifies decision-making. Artificial intelligence has become a decent tool — an assistant that significantly boosts developers’ efficiency. But what about the role of JavaScript in AI development?

You’re definitely no stranger to JS’s power for crafting dynamic and interactive web applications. With millions of developers already proficient in this language and thousands of frameworks and libraries, JavaScript has the potential to grow in almost any direction. The emergence of new libraries like TensorFlow.js and Brain.js  has made it easier than ever to use the capabilities of machine learning and neural networks directly within JavaScript. Nowadays, JavaScript has what to offer for building chatbots, sentiment analysis, image recognition, and other AI solutions. 

JavaScript’s role in AI development

JavaScript, traditionally associated with web development, is now carving a niche in the world of AI. While Python remains the dominant language of the sphere overall, JavaScript has found its own place. 

First of all, we should keep in mind an excellent level of web integration and accessibility. Developers can easily integrate AI capabilities into web applications. Building JavaScript AI allows easy deployment and accessibility across various devices, ensuring a broader reach for your AI solutions. Leveraging JavaScript opens the door to real-time AI-driven solutions, for instance, systems of personalized recommendations. 

At the same time, we should keep in mind that this popular language is not only about client-side development. Leveraging Node.js enables efficient back-end processing, making it a decent solution for complex tasks and some types of resource-intensive computations. Furthermore, when it comes to resource intensity, it should be said that JavaScript’s capability to handle asynchronous operations seamlessly aligns with the demands of AI development. The event-driven, non-blocking architecture allows for efficient handling of AI-associated tasks.

However, that’s more about the theory. What do we have in practice? Here are several examples where JavaScript AI can be used:

• Web-based AIs: Creating AI-powered features and user interfaces for websites and web-based applications;
• Real-time AIs: Enabling real-time AI experiences, such as chatbots and recommendation systems, directly within web browsers;
• Cross-platform AI solutions: Building AI solutions that work seamlessly across various operation systems and devices;
• JS for machine learning: A vast community has already created several powerful libraries and frameworks to develop and deploy machine learning models, which will be discussed a bit later;
• Asynchronous operations: Opportunity to handle tasks like data processing and model training efficiently using the architecture of JavaScript;
• Natural Language Processing: Implementing NLP algorithms and applications for text analysis and language understanding.

Comparing JavaScript with other AI programming languages

The first language that springs to mind when talking about AI development is, of course, Python. We have previously discussed some aspects of how these languages look compared to each other in our Python vs Node.js article. Speaking briefly about the subject, although JavaScript is traditionally considered the faster language with better performance (especially when it comes to the capabilities of Node.js), Python has typically been the preferred language for most AI-associated tasks, at least until we aren’t speaking about web development. The mature ecosystem with extensive libraries allows Python to be, in fact, the most popular language of AI development, while JavaScript lags behind in certain AI-specific capabilities. Python’s simplicity and readability make it suitable for complex AI tasks. However, as we will see later, most of the successful AI cases use both of these programming languages.

But Python isn’t the only language we should look at. Here are several key factors to understand the context of AI development with different languages:

Language features and ecosystem

Languages such as Python, R, and Julia are specifically designed with data science and AI in mind, offering extensive libraries and frameworks (e.g., TensorFlow and PyTorch for Python), while JavaScript may not be inherently suited for intensive AI tasks. However, new libraries appear, and JavaScript has what to offer (though not for all specific tasks).

Performance and scalability

Computational efficiency is of utmost importance for machine learning and artificial intelligence tasks. Languages like C++, C#, and Julia often outperform JavaScript in terms of raw computational power and memory management. Python, with its optimized libraries, also has robust performance. Although JavaScript’s performance improves continually, building AI in JS, especially when it comes to really huge models, is questionable.

Scope of tasks

Regarding the scope of tasks that can be accomplished, JavaScript shows itself better here than in previous comparisons. TensorFlow.js, for instance, is used for machine learning in JS directly in the browser, enabling speech recognition, natural language processing, and other features. With Node.js, the language expands its capabilities to chatbots, recommendation systems, and data analysis workflows. The integration of AI into web applications, IoT devices, and serverless architectures are areas of particular success for JavaScript. However, training large-scale deep learning models with JavaScript may not be the most efficient choice compared to more specialized AI programming languages.

Cross-platform and integration capabilities

One of the biggest advantages of JavaScript is its ubiquity in web applications and its cross-platform nature. This can be beneficial for AI applications that need to be integrated with web interfaces or need real-time interaction with web services. Other languages might require additional layers for integrations that can be done natively with JavaScript.

Real-world applications of JavaScript in artificial intelligence

Python is generally preferred for most AI and ML tasks due to its versatility and wide support. However, JavaScript could be a better choice for AI-driven web applications or when incorporating AI features into existing JavaScript-based applications. Here are several examples.

Let’s take a closer look at ChatGPT, probably the most famous sort of generative AI, as of today. By the way, we have written a short guide on the generative AIs, where we compared ChatGPT vs. Bard vs. BingAI; don’t hesitate to check it. Based on the public data, OpenAI, a founder of ChatGPT, mainly used four languages: Python, JavaScript, C/C++, and CUDA. Python is the primary language for developing machine learning models — it was used for data preprocessing, model training, and testing. In this case, JavaScript was not directly involved in the core machine learning processes; however, it was used to build web interfaces as well as third-party APIs and applications that interact with ChatGPT.

Google’s Teachable Machine

Google’s JavaScript AI case lies in the fact they used TensorFlow.js in its Teachable Machine project. This web tool allows users to create machine learning models directly in their web browsers using a webcam and showcasing real-time images. Also, GTM has speech recognition capabilities.

Facebook Messenger bots

Facebook JavaScript artificial intelligence use case consists of Node.js and chatbots. These AI-driven bots can understand user queries, provide automated customer support, and handle transactions, all within the Messenger user experience and interface utilizing the Node.js opportunities.

Other cases

Try to find some examples of AI built with JavaScript by yourself. You will definitely find cases of JS-based artificial intelligence, such as crafting a face recognition model just because the developer is bored on the lockdown, or Land Lines by Google that has a complicated pattern recognition system being the JavaScript+HTML only project (which can be easily checked on GitHub) or many other examples.

JavaScript and machine learning

Machine learning and artificial intelligence: you can’t get one without the other. Diving into the technical nuances of machine learning in JS, we should mention the set of tools and libraries the language provides us to work with ML.

TensorFlow.js

As the JS-based incarnation of the popular TensorFlow library (in fact, the most popular deep learning framework in the world), TensorFlow.js is the Alpha and Omega of ML in JavaScript. This library allows for the definition, training, and deployment of machine learning models directly in the browser or Node.js. TensorFlow.js supports advanced features like transfer learning, where a pre-trained model is fine-tuned for new tasks, custom model development, model conversion from Python’s TensorFlow version, support for advanced neural network architectures, etc.

ML5.js

Built on top of TensorFlow.js, ML5.js is designed to make machine learning more approachable for a wider audience. It provides a high-level API, making integrating machine learning key features such as image classification, natural language processing, and pose estimation into web applications as easy as ever. 

Brain.js

Focusing on neural networks, Brain.js offers a streamlined approach to implementing neural network-based AI in web applications. It is particularly noted for its simplicity and ease of use, making it an ideal choice for projects that require basic capabilities, such as pattern recognition and predictive modeling.

Natural

Also, we should add Natural as a powerful tool for natural language processing (NLP). Natural offers functionalities like text classification and sentiment analysis. It’s designed to work seamlessly with JavaScript’s string processing capabilities, facilitating the development of AI-driven chatbots and customer service tools.

JS artificial intelligence: A developer’s perspective

From the developer’s perspective, JavaScript artificial intelligence fusion typically seems twofold. It opens up ample opportunities but, at the same time, presents a set of unique challenges and considerations. Here are some of the thesis that can be often heard among the developers’ community:

• Skepticism towards optimization and speed of JS: A lot of developers express skepticism regarding the optimization and speed of JavaScript in AI-based applications, particularly for those requiring complex and computationally intensive tasks. The most frequent argument here is the concern about the performance of JS itself. Its raw performance, even with such powerful development tools as WebAssembly and TensorFlow.js, may not yet match the efficiency of Python or C/C++, especially for intricate tasks for machine learning and data-intensive operations.
• Enthusiasm for accessibility and versatility: At the same time, many developers are likely to be enthused by the accessibility provided by JavaScript. JavaScript is one of the most widely used programming languages in the world, and the ability to integrate AI into this familiar environment means that a vast number of developers can now experiment with and deploy some of AI’s key features without the steep learning curve.
• A consensus regarding the role of JavaScript: There is no real doubt that JavaScript will still remain the main tool for developing web-based interfaces in AI-powered applications. Regarding JavaScript as the primary language of the whole development process, especially data-intensive tasks for machine learning, some developers perceive it as a complete waste of time, while for some developers, it is as room for experimentation.

JavaScript in AI: Challenges and Solutions 

Most of the skepticism toward the role of this powerful language in AI development stems from challenges inherent in JavaScript. Let’s see some of the difficulties concerning the community of developers the most and some solutions trying to address them.

Computational efficiency in machine learning

Challenge: Probably the main challenge in using JS for AI is its limited computational efficiency, especially for processing-intensive tasks such as training deep learning models.

Solution: Integrating WebAssembly with JavaScript addresses this issue by enabling near-native performance (at least, it significantly improves). This combination allows developers to run pre-compiled boilerplate code efficiently in the browser, enhancing the execution speed of basic AI algorithms and making JS a bit better in terms of performance.

Handling other resource-intensive tasks

Challenge: JavaScript traditionally faces challenges in other aspects of AI/ML development, such as image processing or real-time data analysis.

Solution: Utilizing WebGL for GPU acceleration has the potential to be a game changer. It leverages the power of the client’s graphics processing unit, allowing for more efficient handling of computationally dependent tasks. This API significantly improves the performance of AI applications in browsers. Also, different kinds of tasks are covered by various libraries tailored explicitly to certain types of tasks.

Optimizing development time

Challenge: Developing AI models from scratch in JavaScript is time-consuming, as the game is not worth the candle.

Solution: Although JavaScript definitely has fewer tools than Python, available libraries like TensorFlow.js offer a powerful solution. However, yes, with JavaScript, you still probably need to write more lines of code than with C/C++ or Python.

Challenges of offline functionality

Challenge: Ensuring AI-driven applications remain fully functional and responsive even without an Internet connection has always been a challenge in JavaScript environments. 

Solution: This problem was widely discussed outside of the AI context. When it comes to mostly web-based AI tools, developers can try developing progressive web apps (PWA). PWAs can help with offline functionalities for key features, caching important data and models in the browser. This means AI-driven interactions, like language processing or personalized content, can still function without an active Internet connection. However, yes, that still looks like a crutch, considering the nature of the language.

JavaScript and artificial intelligence: The global impact

Integrating JavaScript in developing web-based AI applications has played a significant role globally. As the dominant language of the web, JavaScript’s foray into AI has transformed how AI functionalities are delivered and experienced online. By embedding AI directly into web browsers, JavaScript enables a more dynamic and interactive user experience, allowing for real-time personalization, analytics, and automation. The global impact is seen in the widespread adoption of these technologies, from e-commerce to streaming platforms, making AI features more accessible. In fact, AI is already a part of our daily routine now, largely due to the impact of JavaScript.

However, frankly speaking, JavaScript isn’t revolutionary in the AI realm when it comes to AI development itself. While it has made significant strides in enabling AI on the web, languages like Python still dominate the field of artificial intelligence and machine learning in terms of performance, community support, and overall versatility of the language. JavaScript’s primary contribution lies in its ability to bring AI to a vast amount of web applications rather than pioneering new AI methodologies or algorithms. Its role is more about accessibility and integration, making AI functionalities available on the web, rather than being the leading force in AI innovation and development.

Conclusion: The future of JavaScript in AI

Despite all the inherent limitations and suspiciousness of developers, the future of JavaScript in AI looks promising, particularly in web-based applications. As web technologies continue to evolve, JavaScript is poised to play a key role in making AI even more accessible and more interactive. With gradual advancements in performance and new powerful libraries, JavaScript’s capability to handle complex AI tasks is likely to improve, bridging the gap between user-friendly interfaces and powerful AI functionalities. 

However, when it comes to JavaScript’s future in AI, it still hinges on increasing computational efficiency and adding new libraries and frameworks. There will be a need for continuous improvement in how JavaScript handles intensive tasks in order to maintain its growing relevance in the AI domain. It can possibly be done through deeper integration with browser capabilities. Additionally, the further development of libraries and frameworks specifically tailored for AI tasks will be crucial for JS. Otherwise, it will still remain a decent tool for building web interfaces but quite a challenging experience for crafting a sizable universal AI model from scratch.

get a custom solution for your project

contact our expert

JavaScript and AI FAQs