The Impetus for LLEX

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GPT-4 is a model that can do subsets of the capabilities that you get through a model like GPT-4. It's not like a magic wand, but there are certain app enterprise applications for these where they're very useful. A lot of those applications can be solved by training your own LLM on your data and serving it yourself. Using an AI assistant or GPTAI would be cool, but if I were to train one on all of my organization's past research, it would be more specific, more innovative for her.

In this episode, we discuss the impact of AI with Andriy Mulyar, co-founder of AtlasAI and creator of GPT4All. We explore how running local private language models can provide competitive benefits over using public models like ChatGPT without confidential data. Additionally, we delve into the importance of embeddings in AI and their potential impact on understanding data semantically. Finally, Andriy shares his thoughts on crypto/AI overlap, specifically focusing on data provenance as a key area for collaboration.

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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.