Once only spoken in hushed tones in darkened rooms, the still nascent field of synthetic data is beginning to deliver on its promise, with businesses and investors taking notice.
Based in Edinburgh Neurolabs raised €3 million in a seed funding round that will allow the company to continue to scale its operations and expand its offering to include multiple consumer packaged goods use cases. Since 2019, the startup has raised a total of $4.9 million.
This announcement comes just weeks after the start of synthetic data in Madrid EVERYWHERE raised €3m in a Serie A round led by Bullnet Capital and alreadywhile Vienna ESPECIALLY AI seen Merging companies lead a $25 million Series B round announced in early January. Clearly, there’s something bubbling just below the surface here.
But maybe I’m getting ahead. Let’s back up for a second.
synthetic adjective synthetic | sin-ˈthe-tik: designed, arranged, or fabricated for particular situations to imitate or replace usual realities.
But wait, isn’t the point of data being cold, hard, indisputable facts? Precisely do not something invented or imitated? Yes.
So what’s the deal with synthetic data?
To put it simply: time, money and precision.
Not only is the process of collecting the huge amount of data needed to train an AI sometimes difficult to obtain, but it often has a high price attached to it and can be “dirty”, ultimately leading to unintended biases.
Where synthetic data comes into play 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 directly describe them.
Through this process, data privacy concerns are virtually eliminated, and datasets can be freely shaped and trained to fit the specific use case of the AI to be trained.
According to Vienna’s Mostly AI, they can “create synthetic datasets that look every bit as real as a company’s original customer data and reflect behaviors 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 outperform real data within the next eight years.
And now back to our regular program
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 development and deployment cost.
However, it is not as easy as it seems.
CEO and Founder Paul Pop describes one of the hurdles the company has overcome: “Unstructured visual data for AI processes in retail requires highly accurate and anticipated 3D modeling of everyday physical objects such as milk cartons and cereal boxes. For a machine, it is not enough to recognize an object on a shelf. Anticipating and replicating real-world changes in packaging and design is the true feat of synthetic computer vision championed by Neurolabs.
Neurolabs is compiling the largest retail 3D repository of some 100,000 (and counting) digital twins of physical products (consumer packaged goods) that will enable computer vision technologies to be used at every stage of the consumer packaged goods life cycle, from manufacturing and distribution to store/e-commerce, and recycling.
“While the Teslas and Googles of this world can invest their unrivaled financial and human resources into their AI operations 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 to gain adoption,” commented LAUNCHub Ventures’ Stan Sirakov. “With an end-to-end solution so easy to implement, we see it as a way to democratize computer vision.”