comment: >
  
  Okay, quick downvote for implying us loud talkers are dumb! (/s)
context: This is a friendly reminder to read our rules.
  Remember, /r/Showerthoughts is for showerthoughts, not "thoughts had in the shower!"
  (For an explanation of what a "showerthought" is, please read this page.)
  Rule-breaking posts may result in bans. >
  
  I got into an argument with some guy on r/justrolledintheshop about a bit of a myth regarding brakes.
  I sent him several videos of an automotive engineer/enthusiast explaining exactly why it was a myth, including directly addressing some of the exact assumptions the guy was making, with the math and physics to back it up.
  He sent me screenshots of him asking chatGPT about it and getting vaguely myth affirming responses. He said that it was more conclusive evidence than "some stranger on a video that may as well be a flat earther making wrong assumptions" because it "used all the information on the internet to arrive at an answer, so he wasn't going to waste time watching them.
  Of course a lot of the information online regarding this topic is people believing the myth and companies happily selling them things based on it. >
  
  I’m curious to know what the myth was >
  
  That bigger brake rotors will stop a car faster. >
  
  That’s just for better temp dispersion and brake longevity, right? >
  
  Bingo. It's actually useful for track use where there is repeated high speed braking and smaller rotors would quickly overheat. Or in heavy duty towing in mountainous terrain.
  But on street driven cars, they won't do anything to stop you faster, as the limiting factor isn't the brakes, it's the tires, and larger rotors don't actually impart functionally greater stopping power even if the tires were grippier.
  And you can stop from freeway speed as fast as possible multiple times before any modern brake system would start to overheat. >
  
  So the problem with AI is it will only be as smart as the average person. >
  
  AI and machine learning has proven itself only useful at highly specialized tasks with highly trained operators. For example detecting cancers in MRIs or organizing legos or things where there is a definitive pass fail.
  Everything else, AI sucks at, because its always limited by the trainers capabilities. >
  
  Though a profound thought, this isn’t quite accurate.
  When training AI, it’s very important to consider the sentiment and emotion of the text you’re training it with. When training with, I don’t know, some angry Reddit posts, you would weight the training material as “Angry” or at a high on the 1-100 anger scale. From there, you would add less angry posts, and give it more context on why it was angry.
  In your example, by loud, I interpret that to mean that the training material is present across multiple mediums, in an urgent tone. You would train the AI by letting it know the tone, while also clarifying repeated information isn’t always correct. 
  With that context, the AI would weight any “loud” ideas as repetitive and urgent, which may not be an accurate output, so it would generate an output with stronger confidence. >
  
  I was thinking about what conclusion an AI might draw if you trained them on the entirety of the internet and the wide spread missinformation that is present there.
  The propensity for sites to copy and share articles with each other as sources could seriously influence AI training significantly, especially if that information is incorrect in some way.
  I meant loud information as there being lots of it in the training data rather than the tone of the information. It just sounded better for the post. >
  
  I think r/GukkiSpace read the post and responded appropriately that it's up to the programmer to train the AI that loudwr isn't necessarily better. >
  
  But how can the programmer train it to differentiate? How does it know which answer is right. It can't. The AI can't conduct an experiment to prove it, which is how all knowledge is determined.
  Ergo... most AI is overblown bullshit. It is the very definition of selling snake oil to high bidders. >
  
  Provide peer reviewed verified sources proving that statement please. >
  
  The Unreasonable Effectivness of Data, E. Wigner (Google), 2009, Cited by 1829 other papers at time of writing.
  In general: Large data sample of data regarding population will be biased towards people who generate more data - "shout the loudest". When teaching AI using say, the internet, most data will come from people who post on the internet, which is not representative of the whole population.
  Certain mechanisms can be developed to lower that bias, however there is no good way to completely reduce it, nor there is a way to get the data regarding the underrepresented part of the population (i.e. those who don't post on the intertubez).
  This has the effect that AI is primarly trained on the loudest shouters of the population, as there is simply no data available for the quiet portion of the population.
  Hope that answers your doubts. If you have more questions - I am an AI researcher and I'll be happy to answer any you might have >
  
  People don't seem to understand that "ai" is mostly copying human behavior when we talk about language and information curating. And/or a little bit of feedback from humans based on a sample size of results. 
  If people are misinformed, then the ai will be misinformed. Who is right if there is contradictory data. Whatever is more common in dataset or in the mind of the feedback people. 
  It is really simple. But people seem to think it is magic >
  
  Hm, well that is not exactly true. It is always a matter of how training was conducted. There are ways utilised by current AI to deal with contradictory information. Fuzzy reasoning and rough sets are common tools to deal with human based data, and these contain mechanisms for estabilishing contradictions. 
  Another thing is that certain AI are trained purely from noise. Consider the Generative Adversarial Networks (GAN) architecture, which has two neural networks:
  Network 1: The Discriminator: its job is to distinguish whether an image it sees is real or generated.
  Network 2: The Generator: Creates random images, beggining with random gaussian noise.
  In each turn of the learning the Generator creates images, and these get fed to the discriminator together with real images. The discriminator gets punished or rewarded based on how well it did and the generator rewarded for each image it generated which managed to fool the discriminator.
  This creates a feedback loop.
  Is this only recreating data? Approximating it maybe? Or is this what the process of learning looks like?