the strange reality of strict calvinism is that it makes a mockery of God's claims to hate, abhort, or even be theoretically opposed to evil
Because we all know that God's response to the Flood was, "I REALLY liked that, it was AWESOME, it satisfied my will and purposes to obliterate my creation!"
1) If God knew how things would go, exercising his sovereignty in that way is only a little different
2) Evil begins BEFORE man's fall! Satan fell first! We know little about this, but locating first-evil in *human* free will is an error.
The key point is simply that the origin of evil is certainly *before* "original sin."
This is formally equivalent to the question, "Could the Father discontinue the existence of the Son?" since both are questions about if God has sovereignty over His own attributes.
Who can say! Not even the Son was told the day or the hour! But if God is taking His time there must be a reason. We can't reason about what that may be, as God has not revealed it.
.... love me all my Presby family and friends (literally both sides of the family and many of my best friends!)....
.... and your theology is interesting...
.... but very, very, very wrong.
More from Lyman Stone 石來民
So a few days back I was tweeting about SSRIs. The big question with these drugs is: why do controlled trials routinely show such small effects when practitioners and patients report life-changingly-large effects?
So first off, at this point the evidence is pretty clear that SSRIs and other anti-anxiety/anti-depression drugs truly don't do very much. Their average effects are beneath clinical significance, as I tweeted about here:
Basically, the problem these drugs face is that while they actually see relatively LARGE effects.... but that placebos in those trials ALSO see large effects (and most untreated depression improves within a year anyways).
So basically you have this problem where:
1. The condition tends to improve on its own in a majority of cases
2. Placebo effects for the condition are unusually large
Which means the large crude effects of SSRIs get swamped.
So that raises two new questions.
1. (Not my focus here) Are we treating these conditions appropriately given their untreated prognosis is usually (though certainly not always!!) "goes away in a few months"?
2. Why are placebo effects so unusually large?
So first off, at this point the evidence is pretty clear that SSRIs and other anti-anxiety/anti-depression drugs truly don't do very much. Their average effects are beneath clinical significance, as I tweeted about here:
What's the best recent empirical assessment of SSRI/SNRI effectiveness which deals with heterogeneity and long-term effects in a plausible way?
— Lyman Stone \u77f3\u4f86\u6c11 (@lymanstoneky) December 4, 2020
Basically, the problem these drugs face is that while they actually see relatively LARGE effects.... but that placebos in those trials ALSO see large effects (and most untreated depression improves within a year anyways).
So basically you have this problem where:
1. The condition tends to improve on its own in a majority of cases
2. Placebo effects for the condition are unusually large
Which means the large crude effects of SSRIs get swamped.
So that raises two new questions.
1. (Not my focus here) Are we treating these conditions appropriately given their untreated prognosis is usually (though certainly not always!!) "goes away in a few months"?
2. Why are placebo effects so unusually large?
More from For later read
Nice to discover Judea Pearl ask a fundamental question. What's an 'inductive bias'?
I crucial step on the road towards AGI is a richer vocabulary for reasoning about inductive biases.
explores the apparent impedance mismatch between inductive biases and causal reasoning. But isn't the logical thinking required for good causal reasoning also not an inductive bias?
An inductive bias is what C.S. Peirce would call a habit. It is a habit of reasoning. Logical thinking is like a Platonic solid of the many kinds of heuristics that are discovered.
The kind of black and white logic that is found in digital computers is critical to the emergence of today's information economy. This of course is not the same logic that drives the general intelligence that lives in the same economy.
Help! What precisely is "inductive bias"? Some ML researchers are in the opinion that the machine learning category of \u2018inductive biases\u2019 can allow us to build a causal understanding of the world. My Ladder of Causation says: "This is mathematically impossible". Who is right? 1/
— Judea Pearl (@yudapearl) February 14, 2021
I crucial step on the road towards AGI is a richer vocabulary for reasoning about inductive biases.
explores the apparent impedance mismatch between inductive biases and causal reasoning. But isn't the logical thinking required for good causal reasoning also not an inductive bias?
An inductive bias is what C.S. Peirce would call a habit. It is a habit of reasoning. Logical thinking is like a Platonic solid of the many kinds of heuristics that are discovered.
The kind of black and white logic that is found in digital computers is critical to the emergence of today's information economy. This of course is not the same logic that drives the general intelligence that lives in the same economy.
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Nano Course On Python For Trading
==========================
Module 1
Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...
... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit https://t.co/EZt0agsdlV
This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!
In Module 1 of this Nano course, we will learn about :
# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)
# Using Google Colab
Intro link is here on YT: https://t.co/MqMSDBaQri
Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb
You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want
# Importing Libraries
Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.
==========================
Module 1
Python makes it very easy to analyze and visualize time series data when you’re a beginner. It's easier when you don't have to install python on your PC (that's why it's a nano course, you'll learn python...
... on the go). You will not be required to install python in your PC but you will be using an amazing python editor, Google Colab Visit https://t.co/EZt0agsdlV
This course is for anyone out there who is confused, frustrated, and just wants this python/finance thing to work!
In Module 1 of this Nano course, we will learn about :
# Using Google Colab
# Importing libraries
# Making a Random Time Series of Black Field Research Stock (fictional)
# Using Google Colab
Intro link is here on YT: https://t.co/MqMSDBaQri
Create a new Notebook at https://t.co/EZt0agsdlV and name it AnythingOfYourChoice.ipynb
You got your notebook ready and now the game is on!
You can add code in these cells and add as many cells as you want
# Importing Libraries
Imports are pretty standard, with a few exceptions.
For the most part, you can import your libraries by running the import.
Type this in the first cell you see. You need not worry about what each of these does, we will understand it later.
