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

Find Solutions by Simulating Darwinian Evolution​

• Each candidate solution is defined by its properties (chromosomes)​
• A fitness function is used to determine which solutions "survive"
  • A simulation may be used as the fitness function
• Surviving solutions may mutate and evolve other solutions​

• Optimal solution may never be found
  • i.e. If a optimality requires a large combination of elements
• Optimum may be found but not recognized
  • If conditions make that solution non-optimal at that moment
    • i.e. If a key feature is not used during the simulation
  • Minimize the liklihood by increasing simulation size

• A chromosome represents all of the possible decisions that can be made

• Each gene represents a decision

  • Ideally a gene fully describes a single game state
  • If no decision is possible for a state, there is no gene for that state

• An allele is an option for that decision

  • If there are 3 options for a given state there will be 3 possible alleles for that gene

• Implement in your preferred language

  • C# implementation is on GitHub

• Augment to include what place the player is in

  • i.e. Might make different decisions if started 1^st vs 6^th

• Augment to include opponent locations

  • i.e. Might make different decisions if opponents are close to the end

• Define the DNA of another game

  • Recommendation - stick with simpler games to start
  • Avoid games like chess with massive complexity

Design a system to find words/phrases that are far apart (semantically different) in the embedding space?

• Assumptions

  • No db of embeddings available to search
  • Limited understanding of the features of the embedding space
    • i.e. changing language changes nearly every dimension

• What type of models might work?

  • Can we do it logically?
  • Can we brute-force it?
  • Are there hybrid approaches?

• What are the features of each model?

• How do we execute the model(s)?

• Categorical

  • Part of Speech (noun, verb, etc)
  • Register (formal, slang, technical, etc)
  • Morphology (root, compound, phrase)
  • Animacy (inanimate, animate, agentic, collaborative)
  • Scientific Discipline (biology, physical, life/health, etc)

• Continuous

  • Polarity (negative -> positive)
  • Idiomaticity (literal -> idiomatic)
  • Concreteness (abstract -> concrete)

• Other Options

  • Language

We need to find what should change in order to identify a word distant from the current one

• Part of Speech: Noun -> Adverb
• Discipline: Software Engineering -> Philosophy
• Polarity: Neutral -> Highly Positive

You are a simulation of a great linguist. You classify words/phrases within the following categories:

• Part of Speech (noun, verb, etc)
• Register (formal, slang, technical, etc)
• Scientific Discipline (biology, physical, life/health, etc)
• Morphology (root, compound, phrase)
• Animacy (inanimate, animate, agentic, collaborative)
• Polarity (highly negative, negative, neutral, positive, highly positive)
• Idiomaticity (highly literal, literal, idiomatic, highly idiomatic)
• Concreteness (highly abstract, abstract, concrete, highly concrete)

Your job is to identify words/phrases that meet the requested characteristics. You respond only with the requested word or phrase in lower case.

Give me a word or phrase with the following characteristics:

• Primary part of speech is verb
• Register is technical
• Morphology is phrase
• Animacy is collaborative
• Scientific Discipline is biology
• Polarity is highly negative
• Idiomaticity idiomatic
• Concreteness highly concrete

and is not in the following list: "prime genius", "engineer a cooperative symbiosis".

Be sure to only respond with the selected word or phrase, no ceremony.

Once we can encode the traits of a word/phrase/idea, what other fitness functions could we apply?

• The DNA defines the traits of the specific organism (candidate)
• The Fitness Function defines the direction of evolution

We discussed 2 problems:

• Game strategy - what's the best move given game state?
• Semantic Distance - what word should I test next?

What do they have in common? What makes them good candidates for genetic algorithms?

• Genetic Algorithms "Learn" by simulating Darwinian Evolution
• The "DNA" represents the decisions that can be made
  • One chromosome per condition (game state)
  • One gene per option (choices from a state)
• Several parameters control how solutions evolve
  • Simulations per Generation - how often the solutions change
    • Fewer simulations make it more likely a less-fit solution will survive over a more-fit solution
  • Error (misspelling) rate - how often each chromosome changes between generations
    • Higher rates cause greater variations from generation to generation
  • Selection strategy - Which solutions survive and mutate
    • Survive intact - Remain unchanged to next generation
    • Survive mutated - Modified versions survive to next generations
    • Survive recombined - Crossover between two solutions survives

Appendix