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Mindtech

Train Smart Retail AIs 50x Faster With Mindtech’s New Synthetic Data Application Pack

Smart Retail application pack enables developers to rapidly train AIs with synthetic data for next-gen use-cases Current retail customer use cases include enhancing customer safety, spill and object detection, monitoring stock levels, customer in-store engagement and traffic flow Mindtech will be exhibiting at NRF on booth # 1064 JANUARY 17, 2022 – NEW YORK – […]

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Synthetic Data Platform Mindtech Releases New Application Pack for Training Smart Home AIs at CES

Enables developers to rapidly train AIs with synthetic data for next-gen Smart Home use-cases Customer AI applications include home security, home automation and home safety CES, Las Vegas, 5 January – Mindtech Global, developer of the world’s leading end-to-end synthetic data creation platform for training AI vision systems – has released a new application pack for companies developing Smart Home solutions. The smart

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Synthetic Data Company Mindtech Releases New Application Pack for Training Smart City AIs

Turn-key solution for companies developing Smart City AI vision systems Enables developers to rapidly train AIs with synthetic data on novel use-cases including pedestrian and traffic management Sheffield, UK, 11 November – Mindtech Global, developer of the world’s leading end-to-end synthetic data creation platform for training AI vision systems – has released a new application

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Mindtech Releases New Features for Chameleon – the Synthetic Data Creation Platform for Training AI Vision Systems

Sheffield, 11 August 2021, Mindtech Global announces the latest update to their end-to-end platform for the creation and curation of synthetic images for training AI vision systems. This release brings an enhanced platform user experience, simplifying the creation of complex scenarios, with comprehensive new data discovery, bias and diversity management tools. New automated real-world behavioral

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Mindtech Raises $3.25m to Accelerate Growth of Synthetic Data Training Platform for AI Vision Systems

Sheffield, UK 23rd July 2021 Mindtech Global, developer of the world’s leading end-to-end synthetic data creation platform for training AI vision systems – today announced closing a $3.25 million funding round. The round was led by NPIF – Mercia Equity Finance, which is managed by Mercia and is part of the Northern Powerhouse Investment Fund

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Mindtech Global Limited Strengthens Executive Team with Addition of Justin Bronder from Microsoft

LONDON, May 5, 2020 – Justin Bronder has joined Mindtech Global Limited as VP-AI Strategy. Prior to joining Mindtech, Justin worked at Microsoft, where he led a team of computer scientists that developed machine learning solutions using AI in the fields of reinforcement learning, generative adversarial networks, and object detection as well as using classical

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Mindtech Global Demonstration of Synthetic Data Creation for Neural Network Training

Peter McGuinness, Vice President of AI and Services at Mindtech Global, delivers a product demonstration at the September 2019 Vision Industry and Technology Forum. Specifically, McGuinness demonstrates the company’s Chameleon AI toolset for creating, managing and generating synthetic data for neural network training.

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mindtech-simulator

Mindtech Introduce Chameleon Synthetic Data Generator and AI Tools for Effective Training of AI-Based Solutions

LONDON, SEPT. 17 – Mindtech Global Ltd, a UK based start-up, has announced the availability of Mindtech Chameleon Simulator, creating synthetic vision datasets for training neural networks, and Mindtech Chameleon AI Tools, providing end to end data management for deep learning systems. Synthetic Datasets Effective training of neural networks for visual processing requires very large

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“Can Simulation Solve the Training Data Problem?,” a Presentation from Mindtech

Peter McGuinness, Vice President of AI and Services at Mindtech, presents the "Can Simulation Solve the Training Data Problem?" tutorial at the May 2019 Embedded Vision Summit. While there has been rapid progress in the adoption of neural networks and the evolution of neural network structures, the problem of training data remains. Even companies with

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