Can You Build AI With JavaScript?

JavaScript may not be the language that comes to mind when most people think of artificial intelligence (AI). Still, it’s one of the most widely used languages, and there’s no doubt about its numerous benefits, including speed, scalability, and the accessibility of applications built in JavaScript. With all its strengths, using JavaScript for AI seems like a great idea, but can you build AI with JavaScript?

You can build AI with Javascript. Thanks to libraries like Tensorflow.js, Brain.js and Keras.js, you can implement a wide range of AI use cases in websites, from image classification and pose estimation to regression and natural language processing.

In this article, I’ll discuss the major AI capabilities you can implement with JavaScript, along with the most useful libraries. I’ll also review AI tools that have been made using JavaScript and look at the advantages of building AI with JavaScript.

JavaScript Libraries You Can Use to Build AI

TensorFlow.Js – For New or Pre-existing Machine Learning Models

This JavaScript software library allows developers to build and use machine learning models directly in the browser. They can also run the models in a Node.js environment outside the browser.

Some of the AI capabilities you can implement using TensorFlow.Js include:

Object detection.
Natural language processing.
Image classification.

Being able to run AI models in the browser is a game-changer. You can run models in static HTML documents without having to set up a database or server. For example, developers can implement the following AI use cases entirely on the client side using TensorFlow.js:

Activity monitoring, which allows the client side to establish usage patterns and flag unusual behavior.
AI adversaries in gaming. Users can face AI adversaries even when the browser is offline.
Automatic picture manipulation based on a predefined rule set.
Content recommendation. A machine learning model in the browser determines what a user likes and serves more relevant content.

This library also gives developers the option of training a custom model or using pre-trained models.

A bonus of TensorFlow.js is that it allows you to use any AI model built with the wider TensorFlow library.

Neuro.Js – Great for Chat-Bots and Assistants

This framework allows developers to develop machine learning models and deploy them either via the browser or in Node.js.

Among the functionalities it supports include:

Real-time classification.
Online learning.
Multi-label classification.

Brain.Js – For Straightforward Implementation of Neural Networks

This GPU-accelerated library allows developers to train neural networks and is simple and user-friendly. It’s a high-level library that allows users to work with neural networks in browsers even without advanced neural network expertise.

Here are some AI capabilities that Brain.js enables:

Regression.
Classification.
Prediction.

It also enables the following types of neural networks:

Recurrent.
Feedforward.
Long short-term memory.

Ml5.Js – A Comprehensive Library With Pre-Trained Algorithms

This library facilitates the training of custom models using transfer learning and gives access to pre-trained machine-learning models. It can handle GPU-accelerated mathematical operations, features support for cross-validation, and is suitable for both basic and mission-critical AI models.

While it doesn’t have external dependencies, you can add your dependencies when using it with Node.js.

Some of the AI capabilities it enables are:

Style transfer.
Audio classification.
Pose estimation.
Text generation.
Music composition.

You can use it for the following features:

Array manipulation.
Feature extraction.
Clustering.
Supervised and unsupervised learning.
Regression algorithms.
Random number generation.
Linear models.
Artificial neural networks.

WebDNN – For Fast Execution of Deep Neural Networks on Browsers

This framework achieves speed by compressing model data and optimizing the model. It also utilizes GPU acceleration to ensure zero-overhead execution. It’s estimated that these measures result in execution speeds that are 200 times faster.

To ensure that it gets the most out of computer hardware, WebDNN uses:

Next-gen JavaScript API.
WebGPU to facilitate GPU acceleration.
WebAssembly to optimize CPU execution by converting code into lightweight modules that can be executed faster.

Synaptic – Great for Training and Comparing the Performance of Neural Networks

This library allows neural networks to be implemented either in the browser or via node.js and built-in architectures like liquid state machines and multi-layer perceptrons.

Its advantages include:

Thanks to its architecture-free algorithm, it can train any first- or second-order neural network.
It can import or export networks to JSON to allow gate connections and connections between networks.

Keras.Js – Allows Models to Be Trained on any Backend

Keras is a favorite among developers, and Keras.js allows you to implement deep learning and neural networks on browsers using JavaScript. Some of the heavyweights that use Keras models include Netflix, Uber, and Yelp.

If you want to take advantage of GPU acceleration, you’ll have to run the models in the browser. Running them in Node.js means using CPU mode.

Keras is high-level and usually abstracts the underlying framework to simplify the configuration of neural networks.

Some of the advantages you can expect include:

Flexibility and user-friendliness.
It’s easy to transition from research to deployment.
A smaller and more readable codebase.
Fast models that are easy to deploy.

DeepForge – For User-Friendly AI Models

DeepForge emphasizes collaboration and the reproducibility of research and simplicity and allows the development of AI models with user-friendly interfaces.

One of its unique features is that it supports the training of models on remote machines.

Examples of AI Tools That Have Been Built Using JavaSript

A Client-Side Application That Displays AI Art

This web page uses JavaScript to display AI-generated art.

AI models can hallucinate to produce unique AI art. This hallucination was first discovered when researchers were trying to improve image recognition. Instead of asking AI models to classify images, they asked them to show what they saw. This often resulted in strange images that didn’t match what the model was supposed to see.

To produce AI art, this hallucination can be enhanced by exaggerating the effect of one layer in the image identification neural network. Playing around with the weights in the network alters the art generated.

Google’s Teachable Machine

Google’s Teachable Machine is one of the most user-friendly and effective no-code AI platforms. It allows the use of pre-built models for the following AI use cases:

Pose estimation.
Image identification.
Sound recognition.

Users upload data and train the model, then use it for a wide range of applications. The AI is implemented using the TensorFlow.js framework.

Other AI applications powered by TensorFlow.js include:

LipSync by YouTube. It measures how well users lip sync to a song.
Emoji Scavenger Hunt, which allows users to use their phone to identify emojis in the real world.
Performance RNN, which involves a real-time piano performance by an AI.
Webcam Controller, which allows users to play Pac-Man based on images trained in the browser.

Advantages of Building AI With JavaScript

JavaScript has been growing rapidly and is now used in areas that we couldn’t have imagined. It may not be the first choice when it comes to AI, but it has certain advantages:

Enhanced privacy and security. JavaScript AI applications can run in the browser without sending any user data to the server.
AI applications in JavaScript are faster than Python applications. JavaScript uses node.js and allows techniques like GPU acceleration.
There’s a lot of support for JavaScript as it’s widely used.
It costs less, especially as usage grows. The server fees are relatively low.
JavaScript applications are better for real-time dynamic interaction on websites.
Better scalability as a result of the event-based model used by node.js.

The Takeaway

The question, “Can you build AI with Javascript?” may seem unnecessary to Python enthusiasts who believe anything AI should be implemented in Python. But JavaScript not only matches some of the advantages of Python in AI but also offers additional benefits, including scalability and speed.

With all the libraries that exist to support the development of AI using JavaScript, this trend is only likely to grow.