2451: AI Methodology
Title text: We've learned that weird spacing and diacritics in the methodology description are apparently the key to good research; luckily, we've developed an AI tool to help us figure out where to add them.
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The joke in this comic is that the people are using AI without understanding how to. That classifier is trained on data that doesn't include the causes of the results, and then not testing it at all, producing a model that is both random and heavily overfitted. Such a model appears perfect but makes random predictions on new data. The flavor text is describing this happening, and how. For an introduction to machine learning, you can visit https://fast.ai/ .
This comic shows Cueball giving a presentation of some description. He is reassuring his audience of the validity of his research's methodology, which he says is "AI-based". There are many issues that can arise from an AI-based methodology, such as lingering influence from its training data or a bad algorithm reducing the quality of the investigation.
Cueball seeks to reassure his audience by quantifying the quality of his methodology. He does this by creating yet another AI to rank methodologies. This would not actually improve the confidence of any audience member, as any flaws of the methodology AI would likely be shared by the ranking AI, due to being created by the same team.
Furthermore, the ranking AI heavily favours the methodology of Cueball's AI, and may be biased. It shows a normal distribution, with a singular outlier to the far right with an arrow above. It can be inferred this data-point represents the AI's methodology. It is a significant outlier, and as such it is probably not an accurate representation of Cueball's AI. Alternatively, this could be taken as AI 'nepotism', where Cueball's methodology AI is more likely to select AI-based approaches over others. This type of algorithmic bias is mentioned in 2237: AI Hiring Algorithm.
The title text is likely a continuation of Cueball's dialogue, saying that when the classifying AI was shown good research methodology descriptions, the AI identified weird spacing and diacritics as the indicators of a good methodology. Cueball then used his AI to figure out where to put these into his own methodology description to improve his research report. Adding weird symbols into a text doesn't improve the quality of the text  and hence Cueball may be doing something very similar to p-hacking, where data is manipulated to decrease the p-number, which represents the likelihood the data is a fluke. P-hacking is mentioned in 882: Significant
|This transcript is incomplete. Please help editing it! Thanks.|
- [Cueball stands in front of a projection on a screen and points with a stick to a histogram with a bell curve to the left and one bar to the far right marked with an arrow]
- Cueball: Despite our great research results, some have questioned our AI-based methodology.
- Cueball: But we trained a classifier on a collection of good and bad methodology sections, and it says ours is fine.
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