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What is Latent AI?

We simplify the complexities of AI processing at the edge. The Latent AI Efficient Inference Platform (LEIP) facilitates adaptive AI at edge by optimizing computational resources, energy usage, and memory requirements without necessitating changes to current AI/ML systems or frameworks. LEIP functions as a completely integrated modular workflow designed for the construction, evaluation, and deployment of edge AI neural networks. Latent AI envisions a dynamic and sustainable future powered by artificial intelligence. Our objective is to unlock the immense potential of AI that is not only efficient but also practical and beneficial. We expedite the market readiness with a Robust, Repeatable, and Reproducible workflow specifically for edge AI applications. Additionally, we assist companies in evolving into AI-driven entities, enhancing their products and services in the process. This transformation empowers them to leverage the full capabilities of AI technology for greater innovation.

What is ConvNetJS?

ConvNetJS is a JavaScript library crafted for the purpose of training deep learning models, particularly neural networks, right within your web browser. You can initiate the training process with just a simple tab open, eliminating the need for any software installations, compilers, or GPU resources, making it incredibly user-friendly. The library empowers users to construct and deploy neural networks utilizing JavaScript and was originally created by @karpathy; however, it has been significantly improved thanks to contributions from the community, which are highly welcomed. For those seeking a straightforward method to access the library without diving into development intricacies, a minified version can be downloaded via the link to convnet-min.js. Alternatively, users have the option to acquire the latest iteration from GitHub, where you would typically look for the file build/convnet-min.js, which comprises the entire library. To kick things off, you just need to set up a basic index.html file in a chosen folder and ensure that build/convnet-min.js is placed in the same directory, allowing you to start exploring deep learning within your browser seamlessly. This easy-to-follow approach opens the door for anyone, regardless of their level of technical expertise, to interact with neural networks with minimal effort and maximum enjoyment.

Media

Media

Integrations Supported

Qwen3-Omni

Integrations Supported

Qwen3-Omni

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Latent AI

Date Founded

2018

Company Location

United States

Company Website

latentai.com

Company Facts

Organization Name

ConvNetJS

Company Website

cs.stanford.edu/people/karpathy/convnetjs/

Categories and Features

Artificial Intelligence

Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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