Edinburgh-based synthetic computer vision startup Neurolabs scans in €3 million

Once only spoken of in hushed tones in dimly lit back rooms, the still-nascent field of synthetic data is beginning to deliver on its promises, with both businesses and investors taking note.

Edinburgh-basedNeurolabs已经在种子融资中筹集了300万欧元，这将使该公司能够继续扩展业务并扩大产品，以包括几种消费者包装的用例。自2019年以来，这家初创公司总共筹集了490万美元。

This announcement arrives just a few weeks自从Madrid’s synthetic data startupAnyverseraked in €3 million in a Series A round led byBullnet Capital和Inveready，而维也纳的MOSTLY AI锯熔融冒险lead a $25 million Series B round announced in early January. Clearly, there’s something bubbling just below the surface here.

But perhaps I’m getting ahead of myself. Let’s back up a second.

合成的形容词syn·thet·ic |\ sin-ˈ-the-the-the-the-tik \：设计，布置或捏造，以模仿或替换通常的现实。

But wait, isn’t the whole point of data to be cold, hard, indisputable facts? Precisely不是制造还是模仿的东西？是的。

So what’s the deal with synthetic data?

To put things quite simply: time, money, and accuracy.

收集培训AI有时难以获得所需的大量数据的过程不仅是很难获得的，而且通常会附带出巨额的价格标签，并且可能是“肮脏”的，最终导致意外偏见。

Where synthetic data enters the picture is through the AI-based creation of data that accurately resembles something that exists in the real world and has its characteristics but does not depict them directly.

Through this process concerns about data privacy are all but eliminated, and data sets can be freely shaped and formed in order to fit the specific use case of the AI to be trained.

According to Vienna’s Mostly AI, they can, “create synthetic data sets which look just as real as a company’s original customer data and reflect behaviours and patterns with up to 99% accuracy.”

The power and accuracy of synthetic data is so great, that according to Gartner, the method will completely overtake real-life data within the next eight years.

And now back to our regularly scheduled programme

Now that we’ve made the case for synthetic data, where Neurolabs fits into the grand scheme of things is with a no-code or low-code offering that allows retailers to leverage the power of Computer Vision in any means of automation solutions, all at a fraction of today's cost of development and deployment.

However, it’s not quite as easy as it sounds.

CEO and founderPaul Popoutlines just one of the hurdles the company has overcome, “Unstructured visual data for AI processes in retail needs very precise and anticipating 3D-modeling of everyday physical objects like milk cartons and cereal boxes. For a machine to simply recognize an object on the shelf is not enough. To anticipate and reproduce real-life changes in packaging and design is the real feat for Synthetic Computer Vision championed by Neurolabs.”

Neurolabs正在编译零售中最大的3D存储库，其中包括约100,000（且计数）的数字双胞胎物理产品（消费包装商品），这些数字双胞胎将使合成计算机视觉技术在生命周期的每个阶段都能使用从制造和分销到店内/电子商务以及回收利用的消费者包装商品。

Neurolabs的300万欧元种子回合由索非亚领导LAUNCHub Ventures和锯participation fromTechstart,7% Ventures, andLunar Ventures.

‘While the Teslas and Googles of this world can pour into their AI operations their unmatched financial and human resources to develop next-stage consumer products like self-driving cars, there are a plethora of non-tech industries that are ripe for the latest automation technologies but struggle with adoption,'' commented LAUNCHub Ventures’Stan Sirakov. “With an end-to-end solution so easy to implement, we see it as a way to democratise Computer Vision.”