1. Liquid Victor : Media tracking and aggregation [used to assemble this presentation]
2. Prehensile Pony-Tail : A static site generator built in c#
3. TestHelperExtensions : A set of extension methods helpful when building unit tests
4. Conference Scheduler : A conference schedule optimizer
5. IntentBot : A microservices framework for creating conversational bots on top of Bot Framework
6. LiquidNun : Library of abstractions and implementations for loosely-coupled applications
7. Toastmasters Agenda : A c# library and website for generating agenda's for Toastmasters meetings
8. ProtoBuf Data Mapper : A c# library for mapping and transforming ProtoBuf messages

• Chat Completions
  • ChatGPT gets most of its power here
• Embeddings
  • Enable additional features that can be used with Chat Completions
  • Especially useful operationally

• What are embeddings?
• What do they represent?
• How do we compare/contrast them?
• How can we use them operationally?

Grouping data into categories based on features of each item

• Can be used for:
  • Identifying which known group a new item belongs to
  • Grouping items with shared properties together (clustering)
  • Normalization of input/output

Determines if the input is an affirmative or negative response to a question

• "I'm a canine lover" is an affirmative response to "Are dogs your favorite pets?"
• "Nobody does" is a negative response to "Do you like Javascript?"

Determines the emotional tone of a response

• "I love speaking at great conferences like this" => Enthusiasm
• "I had to miss so many great conferences due to covid" => Regret

• Combines the benefits of retrieval-based and generative models
  • Identify and retrieve relevant information
  • Agument context of the generative models
  • Generative responses based on the augmented context
• Potential uses include
  • Explore large sets of documentation conversationally
  • Generate recommendations and insights based on retrieved relevant information
  • Summarization of articles in light of known relevant information

• When all possible biases have been accounted for
• When all sensitive data has been removed, tokenized or encrypted
• When the stochastic nature of responses has been accounted for
  • A wrong answer is no worse than no answer
  • Outputs have been fully constrained
  • A human is in-the-loop to fix the inevitable errors

• Arrays of 1536 floating-point values
• Structured numeric data that represents unstructured text
• Representations of the semantics and context of the source text
• Vectors that support standard mathematical operations