Pundi AI and Zeno Collaborate to Bridge Physical AI and On-chain Provenance for Robotics

Pundi AI has partnered with ZenO to revolutionize training in Physical AI systems. With more information from the real world and from first person human experience to power robotics and autonomous machines as we move into the age of Artificial Intelligence being able to learn from something other than just text & code, the collaboration will combine ZenO’s datasets with an on-chain record of where that data came from, to create a marketplace for acquiring realistic, verifiable data that connects digital intelligence (AI) and real-world physical environments.

The Missing Link – Why Physical AI Needs Reality

To enable robots to function anywhere other than a laboratory, they need to be able to comprehend human movement, the space around those movements, and how to make decisions by considering context. Most used datasets are missing an essential component – the “first-person” viewpoint of the world. This is important information if we want to build robots that can function as humans do in a city with crowds or manipulate very fragile items as humans do.

This cooperation uses real-world human experiences to build machine-readable workload data sets. “Physical AI needs to directly experience the world,” the release said. Wearable technologies like smart glasses will be used to collect sensory-based data from the user’s eyes and hearing as a living entity. Ego-centric data is best for training autonomous systems since it reflects how biological entities and autonomous systems experience their environments.

Ensuring Data Integrity Through On-chain Provenance

A prominent aspect of this partnership is the focus on on-chain provenance. As AI hallucinations and deepfakes become commonplace, the integrity of the training data is critical. With data provenance being recorded on a blockchain, Pundi AI creates a system in which the data in their marketplace can be confirmed, tracked and immutable.

By decentralizing how we manage data, developers can trace back the source and history of the datasets they buy and therefore will be less likely to train models for robotics on data that has been altered or biased. The result is safer and more reliable autonomous AI. This also fits into a wider trend of using blockchain to secure supply chains across different areas in the Web3 world, including sports data integration and creative assets.

Empowering the Pundi AI Data Marketplace

As an upcoming AI-to-earn market, the Pundi AI Data Marketplace will act as a central source of real-world data, enabling AI training providers to reward users for their contributions. This will allow Pundi AI to build a more democratic model of AI development and its broader use.

ZenO will enhance the usefulness of the marketplace by providing an additional avenue for data acquisition via “Physical AI” datasets, as opposed to relying only on digital means. McKinsey estimates that there will be an explosive increase in demand for high-quality, specialized datasets created by physical automation as generative AI moves toward physical automation. Pundi AI’s infrastructure provides an efficient means to share and distribute this specialized data, allowing both startups and large tech companies access to the same resources needed to improve their robotics algorithms.

Conclusion

At the crossroads of Web3 and Artificial Intelligence, The Partnership marks a significant development. The two will create first-person experience for humans, as seen through their eyes, using blockchain to secure the rarity of digital assets, as well as to preserve the historical fidelity of those robots in development. Robotics and physical AI will touch the physical world more each day. Opportunities like Pundi AI’s platform will let AI-powered gadgets in the physical world be trained in the best, most accurate way to reflect their realities.