DiscoverEye On A.I.Edo Liberty: Solving ChatGPT Hallucinations With Vector Embeddings
Edo Liberty: Solving ChatGPT Hallucinations With Vector Embeddings

Edo Liberty: Solving ChatGPT Hallucinations With Vector Embeddings

Update: 2023-03-30
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Welcome to the latest episode of our podcast featuring Edo Liberty, an AI expert and former creator of SageMaker at Amazon’s AI labs. In this episode, Edo discusses how his team at Pinecone.io is tackling the problem of hallucinations in large language models like ChatGPT.

Edo’s approach involves using vector embeddings to create a long-term memory database for large language models. By converting authoritative and trusted information into vectors, and loading them into the database, the system provides a reliable source of information for large language models to draw from, reducing the likelihood of inaccurate responses.

Throughout the episode, Edo explains the technical details of his approach and shares some of the potential applications for this technology, including AI systems that rely on language processing.

Edo also discusses the future of AI and how this technology could revolutionise the way we interact with computers and machines. With his insights and expertise in the field, this episode is a must-listen for anyone interested in the latest developments in AI and language processing.

We have a new sponsor this week: NetSuite by Oracle, a cloud-based enterprise resource planning software to help businesses of any size manage their financials, operations, and customer relationships in a single platform. They've just rolled out a terrific offer: you can defer payments for a full NetSuite implementation for six months. That's no payment and no interest for six months, and you can take advantage of this special financing offer today at netsuite.com/EYEONAI 

Craig Smith Twitter: https://twitter.com/craigss
Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

 

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Edo Liberty: Solving ChatGPT Hallucinations With Vector Embeddings

Edo Liberty: Solving ChatGPT Hallucinations With Vector Embeddings

Craig Smith