ClarisTalk

In this episode of Claris Talk AI, hosts Cris Ippolite and Matt Navarre delve into the concept of vector databases and their importance in semantic search and chatbot functionality. They discuss the process of converting words into numbers through embeddings, using specifically trained language models called embeddings models. The hosts explain how these models extract features from words and plot them in a multi-dimensional space, where the distance between the plotted points indicates their similarity.

Cris and Matt also explore the potential for integrating semantic search capabilities into FileMaker databases, either by storing vectors in a separate database or potentially within FileMaker itself using container fields. They touch on the importance of re-ranking search results based on semantic distances and the possibilities this could bring to FileMaker developers.

The conversation also covers the rapid advancements in AI and the need for regular discussions to stay informed. Cris shares his experience using the Gemini 1.5 Pro model from Google, which offers a 1 million token context window, enabling users to analyze large amounts of data, such as court transcripts or even entire movies, for various purposes.

SHOW NOTES:
- Book recommendation: "AI for Good: Applications in Sustainability, Humanitarian Action and Health" by various PhDs
- Libby app for accessing audiobooks from local libraries
- Upcoming FileMaker conferences: Rome FileMaker Week (October 7-13), Engage EU, dot FMP Berlin, and Reconnect in Brisbane (September)
- Gemini 1.5 Pro model from Google, offering a 1 million token context window, now generally available
- Cris Ippolite will be participating in a Claris AI webinar on April 10

TIMESTAMPS:

00:00 Introduction and overview of the episode
01:28 The importance of semantic search and its relevance to Claris FileMaker developers
02:33 Matt Navarre's FRM Search Results tool and plans for a semantic search update
07:02 Lexical searches vs. semantic searches
10:25 The process of converting words into numbers through embeddings
16:12 Plotting words in a multi-dimensional space to determine similarity
20:23 Semantic search in chatbots and matching customer needs with business solutions
21:47 Storing vectors in a separate database or within FileMaker using container fields
26:51 Matt Navarre's love for JSON and its versatility in FileMaker development
29:38 Upcoming FileMaker conferences and guest appearances on the podcast
37:00 The rapid advancements in AI and the need for regular discussions to stay informed
43:51 Book recommendation: "AI for Good" and the importance of educating oneself about AI technology
48:48 A Wheel of Fortune contestant solves a puzzle with just one letter, stumping AI models
52:53 Gemini 1.5 Pro model from Google and its 1 million token context window
58:58 Analyzing movies and TV shows using the Gemini 1.5 Pro model for various purposes


Follow Cris:
   / @isolutionsai  
  / crisippolite  
  / isolutionsai  
https://www.isolutionsai.com

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