61: Fluffy Ones That Don't Hurt Each Other

Spinnerproof

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5y ago 1:55:00

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Front matter
- We already recorded this.
- Battle in the ‘Burgh
  - The whole tournament on YouTube
    - Plus whiteboards and shenanigens
  - How to watch a live event
‘Fixing’ the Battlebots meta
- Spinners bad
- Weight bonuses for non-spinners?
  - Are weight bonuses effective?
  - How do you decide how much is enough?
    - Walker/shuffler bonuses
      - Shuffler bonus for UK Beetles dropped from 50% to 33% in June 2019
        
        Battlebots used to acknowledge that unsticks happen
      - SOW went 7-0 as a Heavyweight shuffler, and 2-4 as a Superheavyweight
- Removing Battlebots’ extra barriers to non-spinner success
  - Arena hazards
    - Less intrusive pit designs:
      - Robot Ruckus’ walled-off corner pit
      - The ‘side pocket’
  - Judging criteria
    - Robogames judging (ctrl+f ‘flip over’ to find relevant section)
      - Say what you want about Robogames, this is a pretty good set of judging rules
A general discussion about AI and Machine Learning
- A conversation driven by the sordid tale of Apple’s ‘sexist’ credit card
- Defining AI, Machine Learning, and AGI (for the purposes of this discussion)
  - AI: A decision-making system designed by a human. Can be debugged.
  - Machine Learning: A system which produces a decision-making system. Built and then trained, can’t really be debugged.
  - Artificial General Intelligence (AGI): Sci-fi version of AI which is, or appears to be, a human-level intelligence. Don’t worry about it. Yet.
- Current uses of AI/ML
  - Image/video processing
    - Google’s image compression
    - YouTube’s video compression
  - Social media
    - How it all works
    - The wonderful, wholesome, entirely benevolent Netflix algorithm
    - Sam’s experiences with Reddit
    - Promoting controversy/outrage
    - Bad technology vs technology being used by bad people
      - Setting ‘goals’ for ML systems
  - Finance (and other stuff)
    - How bias can develop within an ML system
      - The Apple case
      - How bias is picked up
      - Why bias might not be detected
  - Medicine
    - Cancer detection
    - What happens when the machine causes a human death?
      - The impact of one life lost vs x lives saved
    - Potential bad implementations of the technology
  - Crime ‘prevention’
    - Predicting reoffending
    - Opportunities for bias presented by data which would likely be used for training
- How to be better

90 episodes

#Tech #spinnerproof@gmail.com #Ryan Bratley #Sam Hutchins