Founded Year

2018

Stage

Series B | Alive

Total Raised

$69M

Last Raised

$50M | 5 mos ago

About DataGen Technologies

DatGen Technologies is a data-as-a-service (DaaS) platform, creating customized, photorealistic, synthetic visual data to train neural networks. Has applications in virtual reality, augmented reality, drones, security, and autonomous vehicles.

DataGen Technologies Headquarter Location

Kalman 3

Tel Aviv-Yafo, 6101400,

Israel

DataGen Technologies's Product Videos

ESPs containing DataGen Technologies

The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.

EXECUTION STRENGTHMARKET STRENGTHLEADERHIGHFLIEROUTPERFORMERCHALLENGER
Emerging Tech / Artificial Intelligence

Companies here offer synthetic data platforms that generate hyper-realistic fake videos and images for training AI algorithms. Synthetic data is particularly useful in cases where real video and imaging data might be sparse or hard to obtain, e.g., training autonomous vehicles to navigate severe weather conditions.

DataGen Technologies named as Outperformer among 10 other companies, including Synthetaic, Mindtech, and Parallel Domain.

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DataGen Technologies's Products & Differentiation

See DataGen Technologies's products and how their products differentiate from alternatives and competitors

  • Datagen Platform

    The Datagen platform is a self-service synthetic data platform for visual AI applications, specifically for human and object data. The Datagen Platform uses proprietary virtual camera technology to ‘photograph’ real-world 3D data in photo-realistic simulations, thus creating hyper-realistic environments. Visual data of humans interacting with their environment is one of the most complex types of data to achieve for the training of CV machine learning models. From our work with customers, we know that human and object data are missing for many types of common engineering applications. And because this category of problem crosses so many different domains, the problem of acquiring high-quality labelled video data is immensely acute. Datagen has three product lines: Human- focused Human in context Objects in context

    Differentiation

    Datagen provides high quality, perfectly annotated data that can be used to train CV ML models for tasks related to humans in environments. The data include an accurate representation of the world a… 

Research containing DataGen Technologies

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CB Insights Intelligence Analysts have mentioned DataGen Technologies in 1 CB Insights research brief, most recently on Mar 25, 2022.

Expert Collections containing DataGen Technologies

Expert Collections are analyst-curated lists that highlight the companies you need to know in the most important technology spaces.

DataGen Technologies is included in 1 Expert Collection, including Artificial Intelligence.

A

Artificial Intelligence

9,093 items

This collection includes startups selling AI SaaS, using AI algorithms to develop their core products, and those developing hardware to support AI workloads.

Latest DataGen Technologies News

AI is using ‘fake’ data to learn to be less discriminatory and racist-Business Journal

Jun 28, 2022

Last week Microsoft said it would stop selling software that guesses a person’s mood by looking at their face. The reason: It could be discriminatory. Computer vision software, which is used in self-driving cars and facial recognition, has long had issues with errors that come at the expense of women and people of color. Microsoft’s decision to halt the system entirely is one way of dealing with the problem. But there’s another, novel approach that tech firms are exploring: training AI on “synthetic” images to make it less biased. The idea is a bit like training pilots. Instead of practicing in unpredictable, real-world conditions, most will spend hundreds of hours using flight simulators designed to cover a broad array of different scenarios they could experience in the air. A similar approach is being taken to train AI, which relies on carefully labelled data to work properly. Until recently, the software used to recognize people has been trained on thousands or millions of images of real people, but that can be time-consuming, invasive, and neglectful of large swathes of the population. Now many AI makers are using fake or “synthetic” images to train computers on a broader array of people, skin tones, ages or other features, essentially flipping the notion that fake data is bad. In fact, if used properly it’ll not only make software more trustworthy, but completely transform the economics of data as the “new oil.” In 2015, Simi Lindgren came up with the idea for a website called Yuty to sell beauty products for all skin types. She wanted to use AI to recommend skin care products by analyzing selfies, but training a system to do that accurately was difficult. A popular database of 70,000 licensed faces from Flickr, for instance, wasn’t diverse or inclusive enough. It showed facial hair on men, but not on women, and she says there weren’t enough melanin-rich — that is, darker-skinned — women to accurately detect their various skin conditions like acne or fine lines. She tried crowdsourcing and got just under 1,000 photos of faces from her network of friends and family. But even that wasn’t enough. Lindgren’s team then decided to create their own data to plug the gap. The answer was something called GANs. General adversarial networks or GANs are a type of neural network designed in 2014 by Ian Goodfellow, an AI researcher now at Alphabet’s DeepMind. The system works by trying to fool itself, and then humans, with new faces. You can try testing your ability to tell the difference between a fake face and a real one on this website set up by academics at the University of Washington, using a type of GAN. Lindgren used the method to create hundreds of thousands of photorealistic images and says she ended up with “a balanced dataset of diverse people, with diverse skin tones and diverse concerns.” Currently, about 80 per cent of the faces in Yuty’s database aren’t of real people but synthetic images which are labelled and checked by humans(3), she says, who help assess her platform’s growing accuracy. Lindgren is not alone in her approach. More than 50 startups currently generate synthetic data as a service, according to StartUs Insights, a market intelligence firm. Microsoft has experimented with it and Google is working with artificially-generated medical histories to help predict insurance fraud. Amazon.com Inc. said in January that it was using synthetic data to train Alexa to overcome privacy concerns. Remember when Big Tech platforms found themselves in hot water a few years ago for hiring contractors to listen in on random customers, to train their AI systems? ‘Fake’ data can help solve it. The trend is becoming so pervasive that Gartner estimates 60 per cent of all data used to train AI will be synthetic by 2024, and it will completely overshadow real data for AI training by 2030. The market for making synthetic images and videos is roughly divided into companies that use GANs, and those that design 3D graphics from scratch. Datagen Technologies, based in Tel Aviv, Israel, does the latter. Its CGI-style animations train car systems to detect sleepiness. Carmakers have historically trained their sensors by filming actors pretending to fall asleep at the wheel, says Gil Elbaz, co-founder of synthetic data startup Datagen, but that still leads to a limited set of examples. Fake data isn’t just being used to train vision recognition systems, but also predictive software, like the kinds banks use to decide who should get a loan. For example, to help design algorithms that distribute loans more fairly to minority groups, Fairgen makes databases of artificial people from minority groups with average credit scores that are closer to those from other groups. One bank in the UK is currently Fairgen’s data to hone its loan software. Cohen says manipulating the data that algorithms are trained on can help with positive discrimination and “recalibrating society.” Strange as it may sound, the growth of fake data is a step in the right direction, and not just because it avoids using people’s personal data. It could also disrupt the dynamics of selling data. Synthetic data also won’t eliminate bias completely, though, says Julien Cornebise, an honorary associate professor of computer science at University College London. “Bias is not only in the data. It’s in the people who develop these tools with their own cultural assumptions,” he says. “That’s the case for everything man-made.” Fake is the new real If used properly it’ll not only make software more trustworthy, but completely transform the economics of data Fake data isn’t just being used to train vision recognition systems, but also predictive software According to estimates, 60 per cent of all data used to train AI will be synthetic by 2024, and it will completely overshadow real data for AI training by 2030

DataGen Technologies Web Traffic

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DataGen Technologies Rank

  • When was DataGen Technologies founded?

    DataGen Technologies was founded in 2018.

  • Where is DataGen Technologies's headquarters?

    DataGen Technologies's headquarters is located at Kalman 3, Tel Aviv-Yafo.

  • What is DataGen Technologies's latest funding round?

    DataGen Technologies's latest funding round is Series B.

  • How much did DataGen Technologies raise?

    DataGen Technologies raised a total of $69M.

  • Who are the investors of DataGen Technologies?

    Investors of DataGen Technologies include TLV Partners, Viola Ventures, Spider Capital Partners, Anthony Goldbloom, Michael J. Black and 6 more.

  • Who are DataGen Technologies's competitors?

    Competitors of DataGen Technologies include Deci AI, Neurolabs, Synthesis AI, Synthetaic, Mindtech and 12 more.

  • What products does DataGen Technologies offer?

    DataGen Technologies's products include Datagen Platform.

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