Computer Vision, Natural Languague Processing, Custom R&D
E-commerce or classified sites often contain large category hierarchies of their goods. When a user wants to submit a new item, our AI-based system can suggest up to N categories (usually 1 to 5). Smart category prediction can speed up this process by 70%, saving users precious time.
The base of this technology is a machine learning system that predicts the leaf node category based on a combination of image and/or text. Our proprietary algorithm then chooses optimal nodes from the hierarchy tree to minimize the number of user’s clicks through the hierarchy to get to the correct category.
We have built the system for detection and counting of palm oil trees. This system used aerial photographs of palm tree fields to count the number of palms, detect palm age and health status. Every tree had to be categorized in one of five age categories and one of three health categories. We used object detection model fine-tuned on labeled images of palm tree fields.
Whether you build a professional application for managing free car parking spaces or a consumer mobile app for learning a new language, object detection, and tracking system can raise it to a new level. Object detection systems can replace expensive sensors or provide new information that was previously non-measurable. For consumer apps, it delivers a much more engaging user experience.
On the left you can see an example of our Visual Translator Mobile Application - Babbly.
Many of today's CMS systems are using tags to describe, sort and categorize articles or inventory items. Finding appropriate tags can be a tedious job, and tag quality can often suffer because of it, reducing the SEO and marketing potential of a site.
Our solution suggests tags based on text, image, or combination of both to optimize different metrics. It can also be used to manage the existing tag database by enabling faster tag merging and removal, thus improving tag quality.
A new era of supersonic flight might be just around the corner, but there are three challenges to overcome when it comes to flying faster than the speed of sound.
Those are the three Es of aviation: engineering, environment and economics.
Concorde, the aeronautical marvel that made its last flight 16 years ago this week, only conquered the first of those three travel challenges.
The world's slinkiest airliner could transport passengers across the Atlantic in less than half the time taken by other commercial aircraft, but it still had ecological shortcomings and high operating costs.
Machine Learning and especially Deep Learning can improve and speed up the simulations of linear and non-linear dynamic systems. Their advantages over classical numerical methods are:
Predicting the weather or simulating fluids are just some of the tasks where our models beat classical methods. Deep learning algorithms often used here are 3D convolutional neural networks and generative adversarial networks (GANs).
Meet our highly experienced team who just loves to build AI and design its surrounding to incorporate it in your business. Find out for your self how much you can benefit from our fair and open approach.Contact Us