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.
Introduction to AI in JS
- 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;
- Natural Language Processing: Implementing NLP algorithms and applications for text analysis and language understanding.
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
Performance and scalability
Scope of tasks
Cross-platform and integration capabilities
Google’s Teachable Machine
Facebook Messenger bots
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.
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.
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.
JS artificial intelligence: A developer’s perspective
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.
Handling other resource-intensive tasks
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
Challenges of offline functionality
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.
The most popular JS libraries for AI are TensorFlow.js for ML models, Brain.js for neural networks, and ML5.js, which provides an easier interface to TensorFlow.js for creative coding.