The DeFi Download train AI to avoid rugpulls! Democratising AI's predictive power in complex systems.

March 18, 2022 Radix DLT
The DeFi Download train AI to avoid rugpulls! Democratising AI's predictive power in complex systems.
Show Notes

In this episode of the DeFi Download, Piers Ridyard interviews Humayun Sheikh, co-founder, and CEO of They discuss the role of in democratizing AI, as well as machine learning and automation tools in blockchain and DeFi platforms. is an artificial intelligence and machine learning based blockchain platform populated with intelligent digital twins, who learn and deliver solutions that aid decision-making. Originally based on Ethereum, quickly shifted to Cosmos while retaining its interoperability as an interchain protocol., as a decentralized and open platform, intends to foster innovative ideas from the mobility, infrastructure, and energy sectors to develop inclusive and immersive multimodal systems powered by Autonomous Agents. 

With a background as a founding investor in DeepMind, Humayun Sheikh is enthusiastic about the future of the distributed economy. 

[0:49] How was Humayun’s experience with the launch of in 2017-2018?

[1:54] What was the platform's original vision? How has it evolved over time to become what it is today?

[6:03] What exactly does the term "democratizing AI" mean?

[7:38] What are some instances of questions that a machine learning algorithm might be asked in the open data environment of a public ledger? What is the difference between an individual attempting to design a model that produces strong predictive outcomes and using something like, which simplifies these challenging tasks and allows individuals to have democratized access to these systems in the first place?

[14:39] What role might blockchain play in AI automation?

[19:56] Data storage on IPFS, data verification processes, and model upgrading rules

[22:20] An example of a user who utilizes to withdraw liquidity from a liquidity pool when a model predicts a rug pull.

[26:26] The token (FET) and the kind of projects seeks to join their ecosystem

[28:58] How does plan to bootstrap the marketplace? What are their thoughts on matching consumers willing to pay for outcome predictions with experts who know how to build predictive models? How does the Fetch team extrapolate what the market currently requires and then decide which models to bootstrap first?

[33:34] The current state of the framework, as well as the agents that can be deployed at this time

[34:58] Upcoming projects: ecosystem funding for projects, hackathons, and Cosmos IBC integration 

Further resources