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TASBot plays Brain Age by micro500, xy2_ in 20:06 - Awesome Games Done Quick 2016 - Part 154

TASBot plays Brain Age by micro500, xy2_ in 20:06 - Awesome Games Done Quick 2016 - Part 154 on HackerTube: recommended by 1 Hacker News commenter across 1 month, 2019–2022.

Games Done Quick

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A tool-assisted run of Brain Age by TASBot, shown at Awesome Games Done Quick 2016. The bot gives deliberately deranged answers while exploiting the game's error-prone recognition.

Why Hacker News recommends it

HN points to it as a funny, fun-to-watch example of what TAS can do to a game's input handling.

people recommended it
1
separate months
1
strong recommendations
0
discussed on HN
2019–2022
“It's stupidly funny.”
— PetitPrince, Jul 2019, in “Zelda speedrunners are attacking the memory allocator”

What HN commenters said

3 Hacker News comments link this video. Each one links back to its thread.

PetitPrinceJul 2019Recommends

> e.g the increasingly deranged "correct" answers to TASbot brain age. Video link: ▶ this video It's stupidly funny.

in “Zelda speedrunners are attacking the memory allocator” · View on HN

schroedingJul 2022Cites it

Arbitrary Code Execution TAS like Triforce% or this Pokemon Yellow one[1] are more or less demos that target both the console and the "host game". I would say even some non-ACE ones like this Brainage TAS[2] are demos :D [1] [link] [2] ▶ this video

in “Games Done Quick “Ocarina of Time” beta showcase” · View on HN

Cycl0psSep 2021Cites it

By the sound of it this is a problem that neither entities nor their moniedness can solve. I'm sure these companies watch each other, and when one steps on a metaphorical rake the others are likely taking notes on how to avoid it on their future attempt. And yet rakes are still being stepped on. When you have an automated system that has irregular behavior to a given input, we call that a bug. Bugs exist in all software, not always unique, but always present. This software is no different than any other. It will have errors. Because the software is categorizing faces, its errors will result in miscategorizing them. The only relevant questions to this are how frequent these errors are and how disparate they are across racial lines. Another reference: this one is a Tool-Assisted Speedrun of a game that relies on basic image recognition software. While not entirely related, it does show how error-prone these algorithms can be. It's also fun to watch. ▶ this video

in “Facebook apology as AI labels black men 'primates'” · View on HN

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