Chapter
Contracts Over Data with Decentralized Oracles
1:26:59 - 1:39:47 (12:48)
The use of contracts over data, with decentralized oracles and an impatial computational agent, could improve data privacy while still allowing for the exchange of value between parties. By placing private data on the decentralized Oracle network, trust issues can be settled in an autonomous and reliable way.
Clips
Hybrid smart contracts can enhance confidentiality by placing private computations in the off-chain decentralized Oracle network with the help of trusted execution environments, providing additional privacy even to the nodes carrying out the computations.
1:26:59 - 1:32:57 (05:58)
Summary
Hybrid smart contracts can enhance confidentiality by placing private computations in the off-chain decentralized Oracle network with the help of trusted execution environments, providing additional privacy even to the nodes carrying out the computations.
ChapterContracts Over Data with Decentralized Oracles
Episode#181 – Sergey Nazarov: Chainlink, Smart Contracts, and Oracle Networks
PodcastLex Fridman Podcast
The transition to hybrid smart contract forms can benefit from a privacy preserving environment that codifies the use of data and enables the exchange of value.
1:32:57 - 1:36:19 (03:22)
Summary
The transition to hybrid smart contract forms can benefit from a privacy preserving environment that codifies the use of data and enables the exchange of value. Machine learning systems are inherently data hungry, making privacy an important aspect of contracts over machine learning systems use of different data.
ChapterContracts Over Data with Decentralized Oracles
Episode#181 – Sergey Nazarov: Chainlink, Smart Contracts, and Oracle Networks
PodcastLex Fridman Podcast
An autonomous agent and on-chain smart contract with an Oracle network can be used to assess data quality, signal from algorithms, and determine pricing for data in a trustless manner.
1:36:19 - 1:39:47 (03:27)
Summary
An autonomous agent and on-chain smart contract with an Oracle network can be used to assess data quality, signal from algorithms, and determine pricing for data in a trustless manner. This can solve the trust issue in data marketplaces and allow for more efficient use of data in machine learning models for financial markets and other purposes.