The Amazing Ways Snowflake Uses Generative AI For Synthetic Data

NVIDIA’s new AI model quickly generates objects and characters for virtual worlds

This is even more true in games than in any other field, because games generate tons of valuable data across all their different modalities. No longer do game developers create a game and ship it and move on. You can now constantly run updates, analyze user activities, understand what works, offer personalized and live activities, and add new meta game layers. In other words, you can constantly improve and optimize your game. Third, this sector has an unfair number of elite product minds. Games founders and their teams are faster than any other companies we see, and they understand user psychology and delight better than anyone.

As well as offering access to AI-generated synthetic data, Snowflake has created a number of tools based on generative AI for its customers to use. The leader ends up behaving differently than the followers. I don’t know, I’m not inside the company, I can’t really tell. What I do know is there’s going to be a big ecosystem of AI models, and it’s not clear to me how an AI model stays differentiated as they all asymptote toward the same quality and it just becomes a price game.

Using InsightFaceSwap (Picsi.AI) to insert yourself into any Midjourney image you generate.

The model should be able to whip up shapes quickly too. The company notes that GET3D can generate around 20 objects per second using a single GPU. From a small set of images, you can make a 3D model! This has benefits over standard photogrammetry, which requires an enormous library of images to generate something (you need to have footage of every angle). However, we did promise at the start that NeRFs were fast, which until recently, was not the case. Previously, NeRFs took a very long time to learn how to transform your set of images into something 3D.

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We expect Generative Tech to analyze our performance, generate advice, or incorporate tools that allow us to hone our craft. Many of these applications might make some people feel uncomfortable at first. But they will challenge us to grow with the help of an AI collaborator. These companies generate rough drafts or completed projects, and incorporate traditional SaaS tools to help perfect those drafts. Over time, we expect these companies to move toward creating finished products, but moving from zero to one is the first big step.


Consumers could capture footage themselves using smartphones, then upload this data to the cloud where algorithms would learn to copy it and insert it into games. It would make it easier to create avatars that look just like players, for example. All this generative tech is not going to be built just on the Open AI GPT-3 model; that was just the first one. The open source community has now replicated a lot of their work, and they’re probably eight months behind, six months behind, in terms of quality.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

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Uniqueness and scalability have historically been incompatible concepts. Truly unique things can’t exist en masse without losing their bespoke qualities. The generative engine is capable of providing a new output for every new user or every problem, at Yakov Livshits scale. This is a very limited approach to personalization because it is based on calling existing data. We have been trying to retrofit people’s preferences into our existing offerings, rather than generating new things that are best suited to them.

NVIDIA says it took just two days to feed around 1 million images into GET3D using A100 Tensor Core GPUs. “This data shows just how quickly generative AI usage has taken off in less than a year,” Clara Shih, CEO of Salesforce AI, said in a statement. “In my career, I’ve never seen a technology get adopted this fast. For those who use the new technology, a third use it every day, Eliyahu says, while the rest use it weekly or more. Most are looking to automate work tasks, while about a third use it for fun, and a third use generative AI for learning about topics that interest them.

Instead, I’ll focus on the videos I made, and little parts of the journey I stumbled on. In the past, facial recognition algorithms have been criticized and even banned due to concerns over biases in the datasets used to train them. This has led to differences in their ability to identify people of different ethnic backgrounds and accusations that they could be unfair or prejudiced. Snowflake is one of the world’s biggest “data-as-a-service” companies that, in addition to their analytics services, also offers a data marketplace covering thousands of topics, including healthcare, finance and retail. Think about a dataset comprising thousands of human faces, for example – as used to train facial recognition algorithms.

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Are they at risk of being replaced by this technology? As you mentioned,  the team working on “AI Dungeon” is smaller. That’s good for the company but potentially bad for developers who might have worked on the game otherwise. This is not possible today but is a capability users would love. Today’s large game developers are not crazy about this, because a players’ investment in virtual goods that can only be used in one game creates lock up. With web3 ownership, any developer can decide to accept virtual goods from other games.

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