Hashgraph is a new consensus algorithm that radically differs from proof-of-work as well as proof-of-stake consensus algorithms. While work on Hashgraph begun in 2012, it’s design is radically different from today’s blockchain architectures. The Hashgraph team claims that it has found an optimal consensus algorithm design that will be impossible to significantly improve upon. We were joined by Mance Harmon, who is CEO of the Swirlds, the company developing Hashgraph. Our conversation covered the origin story of hashgraph, how it compares to existing consensus algorithms and how Hashgraph works. Topics covered in this episode: Leemon Baird and Mance Harmon’s long history of building companies together What motivated Leemon Baird to start working on Hashgraph in 2012 The existing categories of consensus algorithms and their problems How Hashgraph consensus combines voting and gossip protocols The performance characteristics of Hashgraph How a public Hashgraph network could look like Episode links: Hashgraph Homepage Hashgraph Whitepaper Hashgraph Consensus - Detailed Examples Sybil Attacks in Hashgraph Hidden Forces Podcast Episode on Hashgraph Lemon Baird's Talk on Hashgraph at Harvard Business School This episode is hosted by Brian Fabian Crain and Meher Roy. Show notes and listening options: epicenter.tv/219

A significant part of the modern digital economy, is underpinned by machine learning models that are trained to perform tasks such as facial recognition, content curation, health diagnostics etc. Data to train machine learning models is the essential commodity of this century – a sentiment captured by epithets such as “”Data is the new oil””. Today’s dominant AI paradigm has companies focus their efforts on gathering data from their users in order to train models and monetise usage of the model. This model has many consequences such as loss of privacy for the user, consolidation of data in a handful of large companies, low access to data for startups and a fundamental impossibility of collecting sensitive data such as markers for depression. Our guest, Andrew Trask, is building OpenMined – a platform that merges cryptographic techniques such as homomorphic encryption and multi-party computation and blockchain technology to create the ability to train ML models with private user data. OpenMined will allow AI companies of the future to develop models, have them trained on user data without compromising user privacy, and incentivise users to train their model. We walk through the OpenMined vision and its potential impact on AI business models and AI safety Topics covered in this episode: Challenges with the current AI paradigm OpenMined’s vision to allow training of AI models with private user data How OpenMined works under the hood Applications enabled by OpenMined Current state and OpenMined hackathon Episode links: OpenMined hackathon OpenMined website This episode is hosted by Brian Fabian Crain and Meher Roy. Show notes and listening options: epicenter.tv/217