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
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