DEVELOPER BLOG

SKY ENGINE AI’s Software Solutions Fuel Developments in Computer Vision – Gartner Report

The number of human-related applications of computer vision and AI is growing by the day. From medicine to retail to manufacturing and security, AI-powered solutions are becoming more prevalent and will soon be present in nearly all aspects of our lives. In all this flux one constant remains – the need for high-quality data for training AI models. Face analysis-related use cases intensify and complicate this need even more, for example, face recognition, gaze estimation, face segmentation, facial expressions analysis, etc. We challenge this reality with our approach to synthetic data for training computer vision AI models.

SKY ENGINE AI and DRKVRS Partnership – 3D Generative AI for Games Development.

SKY ENGINE AI has raised its Series A round to help tech companies improve computer vision with Synthetic Data Cloud for AI developers.

Transfer learning is a computer vision approach that involves building a new model on top of a prior one. The goal is to enable the new model to learn characteristics from the existing one, allowing the new model to be trained to its purpose faster and with less data.

SKY ENGINE AI has been selected to deliver its world-leading Synthetic Data Cloud for Driver Monitoring System (DMS) software to boost car models capabilities with a major European car manufacturer – Renault Group.

SKY ENGINE AI has been selected by major European defence and military vendor to deliver its world-leading Synthetic Data Cloud software platform for Vision AI and Generative AI. New and legacy vision AI defence systems of the vendor will be powered by synthetic data solutions provided by SKY ENGINE AI.

Zero-shot learning allows a model to complete a job without receiving any training examples by inferring what might be in an image using auxiliary information such as text descriptions.

Transformers, initially described in a Google paper titled "Attention Is All You Need" in 2017, use a self-attention mechanism to tackle sequence-to-sequence problems such as language translation and text production. According to the abstract for the article, the transformer, which is simpler in construction than its predecessor, can do away "with recurrence and convolutions entirely."

Overfitting occurs when a model fits perfectly to its training data. When the machine learning model you trained overfits to training data rather than understanding new and unknown data, the model's quality degrades.

Mask R-CNN, or Mask Region-based Convolutional Neural Network, is an extension of the Faster R-CNN object detection method, which is used in computer vision for both object recognition and instance segmentation.

CVPR – Computer Vision and Pattern Recognition, the world's leading computer vision conference was filled with researchers and professionals discussing recent accomplishments in the area and looking ahead to the future of the discipline and AI in general.

The use of several hidden layers in machine learning (referred to as "deep learning") has outperformed traditional strategies for solving a variety of problems, notably in pattern recognition and object detection. The Convolutional Neural Network (CNN) is a prominent deep learning architecture that is widely used for classification tasks.

Large-scale datasets have become the foundation for training accurate and resilient machine learning algorithms. However, as datasets become larger and more complex, training on the full dataset can become computationally expensive and time-consuming. This is where dataset distillation comes in, providing a method for reducing the processing needs of the training process while retaining critical information.

To prevent overfitting and to correctly evaluate your model, divide your data into train, validation, and test batches.

StyleGAN is a Generative Adversarial Network (GAN) that is used to generate pictures. StyleGAN-T, the most recent product in the StyleGAN series, was released in January 2023. After models based on architectures such as diffusion took the globe by storm in 2023 and early 2023, this model has reintroduced GAN into the picture generation competition.

SKY ENGINE AI announces new win for Synthetic Data Cloud from US top medical devices manufacturer to enable simulation of synthetic data for defect analysis and AI models training. The new win, for one of the major medical devices vendor worldwide, will allow improved PCB (printed circuit board) waffers defects detection powered by SKY ENGINE AI synthetic data and vision AI.

EfficientNet, which was introduced in 2019 by a team of Google AI researchers, quickly became a go-to architecture for many difficult tasks, including object identification, picture segmentation, and even language processing. Its effectiveness arises from its ability to strike a compromise between two crucial deep learning factors: computational efficiency and model performance.

Learning curves are a common diagnostic tool in machine learning for algorithms that learn progressively from a training dataset. After each update during training, the model may be tested on the training dataset and a hold out validation dataset, and graphs of the measured performance can be constructed to display learning curves.

The terms "supervised learning" and "unsupervised learning" will appear often in conversations about data science, machine learning, and other related topics. The ability to discern between supervised and unsupervised learning is basic information that will come up repeatedly in a data science career.

The goal of hyperparameter tuning is to fine-tune the hyperparameters so that the machine can build a robust model that performs well on unknown data. Effective hyperparameter adjustment, in conjunction with excellent feature engineering, may considerably improve model performance.

SKY ENGINE AI Synthetic Data Cloud for Vision AI has been once again selected by an existing customer to enhance the development of the car factory of the future. The new order, for one of the top Japanese car manufacturer, will enable state-of-the-art robotics solutions and logistics powered by computer vision.

An autoencoder is a type of artificial neural network that is used to learn data encodings unsupervised. The autoencoder must examine the input and create a function capable of transforming a specific instance of that data into a meaningful representation.

SKY ENGINE AI Synthetic Data Cloud for Vision AI announces new win for its synthetic data cloud to enable simulation of synthetic data and AI models training. The new win, for one of the top agriculture and crop protection manufacturer worldwide, will allow crop protection improvement powered by synthetic data and vision AI.

The development of neural networks is an active subject of study, as academics and businesses attempt to find more efficient ways to handle complicated problems using machine learning.

Summary conference report after I/ITSEC 2022 – The world's largest modeling, simulation & training event.

Deep neural networks have grown in popularity for a variety of applications ranging from recognising items in images using object detection models to creating language using GPT models. Deep learning models, on the other hand, are frequently huge and computationally costly, making them challenging to deploy on resource-constrained devices like mobile phones or embedded systems. Knowledge distillation solves this issue by condensing a huge, complicated neural network into a smaller, simpler one while retaining its performance.

The virtuous cycle of data needs to be expanded by new modalities including synthetic data to further enhance product development and customers willingness to share more data.

In this article, we are introducing exemplary driver and occupant monitoring case created in the SKY ENGINE AI Synthetic Data Cloud that is training Vision AI models with the RGB and IR cameras to not only recognize the driver, but also to detect driver's activities and vigilance level. Such solutions can effectively lead to increased safety and easier usage to the next generation of driver assistance capabilities and their accuracy can be further boosted when the AI models are created using synthetic data and virtual environments created in the SKY ENGINE AI cloud.

SKY ENGINE AI has been selected by an existing customer to deliver its world-leading digital twins and synthetic data to further boost the development of the car factory of the future. The new order, for one of the top Japanese car manufacturer, will enable digital twins and synthetic data for robotics solutions based on the Computer Vision AI systems.

SKY ENGINE AI featured in the Forrester Market Research article.

Read the full story on Forrester.

SKY ENGINE AI featured in the Venture Beat article.

Read the full story on Venture Beat.

SKY ENGINE AI joins the Metaverse Standards Forum to foster the development of open standards for the metaverse and drive interoperability.

SKY ENGINE AI announces a new deal win with another US major merchandising company. The new order, for one of the largest US merchandisers, will enable synthetic data and solutions delivery for in-store items tracking using vision AI technology.

SKY ENGINE AI featured in the Open Data Science article.

Read the full story on Open Data Science.

SKY ENGINE AI joins Microsoft for Startups Founders Hub to further accelerate broad adoption of synthetic data for AI models training in the metaverse.

SKY ENGINE AI featured in the PS News Australia article.

Read the full story on PS News Australia.

SKY ENGINE AI has been invited o the NVIDIA AI Accelerated program announced at GTC 2022 to boost performance and reliability of AI applications.

Here, we will focus on the applications of the SKY ENGINE AI platform to development of AI models for worker safety monitoring, on-site inspection, audit, and infrastructure management in the construction sector to reduce operating costs and optimize the efficiency of these jobs. 

In this article, you'll discover how to think about your machine learning models from a data-centric standpoint, stressing the relevance and value of data in the AI models creation process.

In this article, you will discover how SKY ENGINE AI’s platform with synthetic data generation tools and AI models training in virtual environment enable designing of computer vision systems for warehousing whereas mitigating bottlenecks and issues with inventorying accuracy.

In team-based sports, building correct playing strategy before the championship season is a key to success for any professional coach and club owner. While coaches strive at providing best tips and point out mistakes during the game, they still are incapable of noticing every detail and behavioral patterns of both teams while rewatching the matches. For being able to collect such data, analyze it and make inference about team behavior sophisticated AI algorithms can be used.

Synthetic data plays increasingly important role in computer vision, especially the data generated from computer simulations provides competitive alternative to real-world data and gains momentum in multiverse of use cases to create accurate AI models.

In AI and computer vision, data acquisition is costly and time-consuming and human-based labeling can be error-prone. The accuracy of the models is also affected by insufficient and poorly balanced data and the prolonged time required to improve the deep learning models. It always requires the reacquisition of data in the real world.

SKY ENGINE AI (UK) announces partnership with an American multinational technology corporation – Microsoft and becomes its official supplier for AI research and development in computer vision.

SKY ENGINE AI (UK) announces partnership with renowned technology pioneer NVIDIA (US) to fast track the end-to-end workflow for AI and computer vision developers.

SKY ENGINE AI featured in the NVIDIA Developers Blog article.

Read the full story on NVIDIA Blog

London, UK and Santa Clara, US — April 8th, 2021 — SKY ENGINE AI, a UK-based data science technology company that develops innovative software used for artificial intelligence and computer vision, today announced it has joined the NVIDIA Metropolis Partner Program.

SKY ENGINE AI (UK) receives ultra wide band radar simulator design and AI models training win from one of the APAC’s largest OEMs. 

SKY ENGINE AI, Land Robots and NIAB East Malling Research announce strategic partnership around AI, computer vision and robotics to shape the future of precise agriculture