In this episode, Michael Fischer, the founder of DB DAO, discusses the role of databases in Web3 and how they can be used improve scientific research and tokenize scientific articles.

Fischer also shares his own experience with medical conditions and his thoughts on the healthcare industry. Fischer has a PhD in computer science from Stanford University, where he studied natural language processing and AI.. He is co-author of the book "Regulating AI" and leads the DeSci NYC community.

00:00:00 - Exploring Blockchain Technology: A Stanford Graduate's Journey

00:00:38 - Introduction to Health Unchained Episode 109 with Michael Fischer

00:06:14 - Alternative Peer Review System Using Tokens

00:08:46 - Building A Community Around DeSci

00:13:43 - Building An Open Database Team For Data Preservation

00:19:40 - Exploring Decentralized Database Governance And Data Ecosystems

00:20:26 - Creating A Mission For A Database: Aligning People Towards A Common Goal

00:21:48 - Creating A Governance Structure For Data Collection And Rejection

00:26:06 - Exploring Corporate Governance Structures In Multi-sig Protocols

00:32:51 - Interoperability Of Web Three With Web Two Applications For User Benefits

00:35:39 - Scouts Earn Rewards For Contributing Data To Database

00:38:53 - The Benefits And Risks Of AI-powered Content Curation

00:42:07 - Data Generation And Web Three Tooling

00:48:01 - Using Zero Knowledge Technology To Create A Web Three Database For Data Science Queries

00:49:35 - Reducing Reliance With ZK And DLT Protocols

00:50:38 - Health Unchained News Corner

00:51:46 - GPT-3: AI-enabled Chat Applications Reaching New Heights

00:54:54 - Effects Of Injury And Medical Conditions On Health Care Industry

00:58:49 - Structuring Data To Improve Patient Communities

Google Research and DeepMind have created MedPaLM, an open-source large language model that can answer medical questions. It combines HealthSearchQA, a new dataset of medical questions, with six existing datasets covering professional medical exams, research, and consumer queries. The model was developed on PaLM (Pathways Learning Model), a 540 billion parameter large language model, and is intended for use by both medical professionals and non-professionals. The developers of MedPaLM are considering using blockchain technology to secure the data input and generated by the model. They hope that the tool will be successfully used in practice with real patients.