Everything posted by Genady
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Today I Learned
Today I learned that after a moving body collides non-centrally and elastically with a body of equal mass which is at rest, they move in mutually orthogonal directions. It is quite obvious in the hindsight, but I was not aware of this.
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Is AI making us luddites?
Is it AI to blame? The movie Idiocracy has been made long before AI.
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[Math] [Set] De Morgan's Law, Symmetric difference with 3 sets
OK. Now I know what the triangle means. It's the operation XOR (exclusive OR). Let's make a diagram with [math]C\subset B[/math]: Now, let's paint the set [math]A {\Delta }B[/math]: and the set [math]A {\Delta }C[/math]: As you see, in this case [math]A {\Delta }B \neq A {\Delta }C[/math].
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[Math] [Set] De Morgan's Law, Symmetric difference with 3 sets
I don't know what that triangle sign means. Some set operation, I guess, but which? Union, intersection, subtraction...?
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[Math] [Set] De Morgan's Law, Symmetric difference with 3 sets
Consider an example. Let set W be all women and set B be all black persons. The intersection of W and B contains all black women. If a person x doesn't belong to this intersection, then x is not a black woman. x can be a black person but not a woman, a woman but not a black person, or neither a black person nor a woman.
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Science Basics
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[Math] [Set] De Morgan's Law, Symmetric difference with 3 sets
No, it does not imply that x doesn't belong to any set. It implies that there is at least one set such that x doesn't belong to it. IOW, it does not imply that [math]x \notin A_1 \wedge x \notin A_2 \wedge x \notin A_3[/math]. It implies that [math]x \notin A_1 \vee x \notin A_2 \vee x \notin A_3[/math]. Yet IOW, it does not imply that [math]\forall i , x \notin A_i[/math]. It implies that [math]\exists i , x \notin A_i[/math].
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[Math] [Set] De Morgan's Law, Symmetric difference with 3 sets
What specifically don't you understand? The OP is too noisy. I am having problem understanding it.
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[Math] [Set] De Morgan's Law, Symmetric difference with 3 sets
What is your question?
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Is AI making us luddites?
I like that LLM can "talk." It might force humans to think more and to talk less.
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when building powersets why don't we make combinations with null set ?
You've just proved by contradiction that it is not a set: if it is a set then it is an element of itself, but it follows from Zermelo-Fraenkel axioms that no set can be element of itself. See, e.g., Set is Not Element of Itself - ProofWiki
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when building powersets why don't we make combinations with null set ?
This is not a set either. Zermelo-Fraenkel
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when building powersets why don't we make combinations with null set ?
"Everything" is not a set. Just from curiosity, I've just checked the "classics", "A Book of Abstract Algebra" by Charles C. Pinter. The word member appears there 28 times. The word element, 500.
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when building powersets why don't we make combinations with null set ?
Moreover, subsets of a set are never elements of that set. They are heavily confused.
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when building powersets why don't we make combinations with null set ?
You call them members; we call them elements.
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when building powersets why don't we make combinations with null set ?
Which places?
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What is the World coming to ?
Yes, good. I've posted this to @studiot :
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[Eletrochem] Help me understand molar conducitvity
Which part: [math]\frac {1} {1000 \ m^{-3}} = \frac {m^3}{1000}[/math] [math]=\frac {100 \times 100 \times 100 \ cm^3}{1000} [/math] [math]= 1000 \ cm^3[/math] ?
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d’Alembert Operator - Geometric Intuition
An arbitrary function f(x-ct) is a wave in the sense that the entire set of the function values rigidly moves along x by ct when the time advances by t.
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What is the World coming to ?
Maybe this helps: How to disable every AI feature on Microsoft Edge for Windows 11 - Pureinfotech
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[Eletrochem] Help me understand molar conducitvity
1/(1000 m-3) = 1/1000 m3 = 1/1000 x 100 x 100 x 100 cm3 = 1000 cm3
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What is the World coming to ?
I've learned things like this: Though the size of the computation for training can be large, the trained neural network can be quite small. In our MNIST example, training the network involves a reasonable amount of computational power to find the optimal values of the parameters. But the network only has 11,935 parameters. It is relatively small. This observation tells us chips containing trained neural networks can be small and cheap. It will be easy to install them into everyday devices. Bernhardt, Chris. Beautiful Math (p. 185). MIT Press. 2024.
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What is the World coming to ?
Not everyone and not all the time needs to heat their house. I never need it. And nobody else in my country.
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What is the World coming to ?
They require this huge power for training, but not for answering queries. (I skip the LLM responses just as I skip commercial ads.)
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What is the World coming to ?
They will read the paper.