Decentralized vs Distributed AI Systems: Nuances and Future Trends

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The chapter discusses the distinction between decentralized and distributed AI systems, emphasizing the importance of functionality over decentralization obsession. It explores retaining IP in a decentralized web 3 industry, introduces zero-party data for IP monetization, and debates data ownership challenges. Additionally, it delves into data significance in AI models, specialized model building with small datasets, differentiation among tech companies, and predicting inflection points for decentralized AI and compute integration.

Recorded Live at ETH Denver 2024, this conversation explores the intersection of crypto and AI, discussing the need for decentralized AI models and the challenges of achieving this. The participants highlight the importance of keeping AI open and driven by people, rather than organizations. They discuss the potential of blockchain and Web3 technology to enable new business models and address the limitations of centralized AI companies. The conversation also touches on the role of incentives and tokens in driving innovation in the crypto AI space. The discussion concludes with an examination of BitTensor and the potential for future advancements in crypto AI. The conversation explores various themes related to the intersection of crypto and AI. It discusses the value of outside judgment in rating AI systems, the concept of a hive mind, and exciting use cases in the crypto and AI space. The projects AGI Guild and Morpheus are highlighted, along with the decentralized data warehouse Space and Time. The potential of utilizing unused data in DeFi protocols and the decentralized chat GPT platform MyShell.ai are also discussed. Concerns about OpenAI's dominance, the collaboration between Web2 and Web3, and the concept of zero party data are explored. The conversation also touches on the potential of data DAOs and crowdsourcing, the idea of minimum viable centralization, and the inflection point of decentralized AI and compute. Tangible innovation in ZKML and decentralized data, the importance of edge compute, and the need for decentralized inference are further examined.

Chapters

00:00 Introduction and Background

01:54 The Need for Crypto and AI

06:29 Challenges of Decentralizing AI

09:18 Building Better Models with Web3

13:31 Innovation and Value in Crypto AI

19:11 BitTensor and the Future of Crypto AI

24:18 The Value of Outside Judgment

25:14 The Concept of a Hive Mind

25:43 Exciting Use Cases at the Intersection of Crypto and AI

26:33 AGI Guild and Morpheus Projects

27:32 Space and Time: Decentralized Data Warehouse

29:26 Utilizing Unused Data in DeFi Protocols

29:53 Decentralized Chat GPT with MyShell.ai

30:48 Concerns about OpenAI's Dominance

34:00 Web2 and Web3 Collaboration

35:11 The Concept of Zero Party Data

37:30 The Potential of Data DAOs and Crowdsourcing

38:06 Minimum Viable Centralization

40:29 The Inflection Point of Decentralized AI and Compute

43:45 Tangible Innovation in ZKML and Decentralized Data

45:35 The Importance of Edge Compute

46:30 The Need for Decentralized Inference

Disclosures

Disclosures: This podcast is strictly informational and educational and is not investment advice or a solicitation to buy or sell any tokens or securities or to make any financial decisions. Do not trade or invest in any project, tokens, or securities based upon this podcast episode. The host and members at Delphi Ventures may personally own tokens or art that are mentioned on the podcast. Our current show features paid sponsorships which may be featured at the start, middle, and/or the end of the episode. These sponsorships are for informational purposes only and are not a solicitation to use any product, service or token. Delphi’s transparency page can be viewed ⁠⁠⁠⁠here⁠⁠⁠⁠.