Synthetic Data for Better Computer Vision

Yashar Behzadi on how virtual images of real things can train AI systems.

Computers learn to see by having objects labeled. This is typically done by humans and is slow and error prone.

Synthesis AI generates perfectly labeled digital images to train computer vision algorithms. 

The mission at Synthesis AI has been the same since their initial launch: to enable more capable and ethical AI. Their unique platform couples generative AI with cinematic CGI pipelines to enable the on-demand generation of photorealistic, diverse and perfectly labeled images and videos. Synthesis AI gives machine learning practitioners more tools, greater accuracy, and finer control over their data for developing, training and tuning computer vision models.

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Computers teaching other computers to see

Synthesis AI is pioneering synthetic data technology to build more capable AI to define the new computer vision AI tech stack. Synthesis solves the engineering aspect of creating synthetic data to improve CV perception algorithms. The company builds workflows which easily integrate with a CV team to navigate the longtail data set, edge cases, privacy and bias.

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Machine to Machine Learning



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"Now you can explicitly define your data sets so that you don't have class imbalance, misrepresentation, or under-representation of particular demographics, which ultimately leads to AI bias. And of course, you're doing this in a completely privacy-compliant way, so you're not only solving technical issues, but you're ultimately enabling more ethical systems to be built as well. Great.

As an industry and as a society, I think we've all really now focused on the fact that consumer privacy has to be maintained. You can't just scrape data or use data, and there have been a lot of people in the press that have done this and built systems. And I think increasing regulation and increasing consumer scrutiny and pressure is going to push people off that. So the leading companies want to do this the right way.

Live AMA with Yashar Behzadi, Founder of Synthesis AI, on the role of synthetic data for more efficient, robust and ethical model developments.

“The biggest obstacle for creating solutions was having access to high quality, labeled data. It became clear that the human annotation paradigm was not going to be sufficient.”

And the other side of that is AI bias. There's also been a lot of press and focus, rightfully so, on systems that have inherent bias, and this is because of the class imbalance that exists in the data sets.

So if I'm a company and I have a certain demographic of users, and I'm using that data to train my systems, well, by design, I'm potentially excluding certain demographics. And that leads to potentially some really negative consequences if your system doesn't perform well for certain skin tones, for instance, or certain ethnicities, or variations. Using synthetic data allows you to explicitly define your distributions, so you want equal balance across these areas. So we're working a lot with leading technology companies to ensure more fair AI."


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Delve deeper into the world of Machine-to-Machine Learning through our other inspiring articles or discover why we're also passionate about Biological Machines and Human Machine Interaction.


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