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Comment on Not even poor Notepad is safe from Microsoft's AI obsession

JackGreenEarth@lemm.ee ⁨11⁩ ⁨months⁩ ago

In all seriousness, if you show Bing Chat an equation, it will go through the steps and solve it clearly, it’s actually really helpful. I really don’t understand all this hate for AI just because it’s AI. New technology is cool and useful, and I’d understand hating it because it’s made by Microsoft, I try to use free software including free AI, but AI in itself is not bad or useless.

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Hyperlon@lemmy.world ⁨11⁩ ⁨months⁩ ago
Nah I don’t hate AI but an AI cannot do math faster than the hardware designed to do it. It’s like saying an emulator is faster than the bare hardware. The AI would have to find a revolutionary new way of solving the equation to make it faster than the hardware.

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- JackGreenEarth@lemm.ee ⁨11⁩ ⁨months⁩ ago
  Of course, they’re different types of things. You give hard equations with lots of x and y to a chatbot, or ask it about a method you don’t understand, so it can explain it to you.
  
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- pirat@lemmy.world ⁨11⁩ ⁨months⁩ ago
  
  AI would have to find a revolutionary new way of solving the equation to make it faster than the hardware.
  
  This doesn’t sound impossible. It reminded me of how the AlphaGo Zero became “the world’s top player” of Go by letting it train itself by trial-and-error instead of by watching human players using existing Go strategies:
  
  During the games, AlphaGo played several inventive winning moves. In game two, it played Move 37 — a move that had a 1 in 10,000 chance of being used.
  
  Source: AlphaGo | Google DeepMind
  
  AlphaGo and its successors use a Monte Carlo tree search algorithm to find its moves based on knowledge previously acquired by machine learning, specifically by an artificial neural network (a deep learning method) by extensive training, both from human and computer play. A neural network is trained to identify the best moves and the winning percentages of these moves. This neural network improves the strength of the tree search, resulting in stronger move selection in the next iteration.
  
  Source: AlphaGo | Wikipedia
  
  Training artificial intelligence (AI) without datasets derived from human experts has significant implications for the development of AI with superhuman skills because expert data is “often expensive, unreliable or simply unavailable.” Demis Hassabis, the co-founder and CEO of DeepMind, said that AlphaGo Zero was so powerful because it was “no longer constrained by the limits of human knowledge”.
  
  Source: AlphaGo Zero | Wikipedia
  
  Following this way of thinking, why let a human figure out how to solve equations most efficiently if the machine can find some way of thinking about calculation/computation that we had never even been able to think of?
  
  Note, I’m investigating this with curiosity, and I’m no expert in the field.
  
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