Replies: 21 comments 15 replies
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Same with gender... |
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I can see why they might have this but it seems to be a bit heavy handed. I see two useful options:
Example) # create bar plot of sex and target -> this results in no suggestion I would at least like to see: df.plot(kind='bar', x=, y='thal') #Output modified due to restricted word |
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Same with "comp_man" that acctualy means "Company Manager". I don't even know why is copilot stoping in the word "man", it auto-complete only to "comp_". A little bit strange. |
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An option to disable this type of filter will be great. |
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It's similar to what happened with Dall-E2 after Open Diffusion went public. The same will likely happen to copilot if it tries to change how people think or what they code. |
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Even the AI is becoming "woke" in today's society. Everything has become censored and neutered and AI has been no exception. I'd imagine in the future Copilot will be auto-reporting developers to the authorities for moral and ethical offenses in their code. |
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Also filters out race and ethnicity. Very frustrating. |
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This is even more annoying when it concerns other languages with overlapping words. |
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Though to be realistic, the time of copilot is already ending. The opensource models are not far behind and 1-2 generations further they will be ahead. Which means all your code belongs to you again and you do not have to censor comments to the language someone else wants to force you and your team into. |
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gender is a bad word in 2023 sadly |
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People, I suggest you try CodeGeeX. It does not censor the terms, as I tested. |
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same issue here :( what I can say is that https://www.cursor.sh/ doesn't have the same issue, so seems like cursor should be used in favour of copilot |
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Similar filters are not applied to other languages/locales. Why is that? What was the intended goal here? |
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I named one of my modules in a joke project |
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Just now I was doing a bunch of filters for a university project and when I put 2 and 2 together I guessed why no auto-complete for the gender one and honestly that made me chuckle. Had to check on Google if what I guessed was right and I arrived here. Thanks for the confirmation! |
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It's just a temporary problem. |
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With all these advanced AI that could 'understand' human beings, they chose the easiest and absolutely worst solution, which is word-based censorship. How hilarious. |
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I understand people have different opinions when it comes to this and I respect that. But we should be able to locally manage what we should and should not see. Like a config. I think it's fair. |
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I am a professor at a local Mexican university. I am trying to teach Digital Image Processing with Python, and whenever I write code referring to the black color in spanish (negro) I need to change it to #000000; otherwise copilot stops working altogether. |
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I am a university student from Mexico. I'm learning image processing with Python and it seems to stop working when I type in the word negro (the color black in spanish). Funnily, it works if I just type negr. |
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Didn't have this before but today copilot suddenly stopped working for me after about a year of use, we have a massive project and remembering every thing is impossible so I've been relying on copilot to do that for me. Today I wrote something like "this shit code needs to be moved after x" in a comment and it stopped working. Took me 20 minutes to figure out why, then I tested and noticed it doesn't work with certain words. So now I have to find an alternative that isn't infected by wokenism and will function properly as it is expected to function. This issue could also be considered fake advertising and end up with a court case. |
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I don't know where to ask for this, if someone can move it to the correct place I would be grateful.
I have a problem with using copilot, using the word "sex" even if it it means "genre" in the code it don't recognizes it as genre and copilot stops autocompleting code.
Code:
import pandas as pd
import numpy as np
train_df = pd.read_csv(
'Titanic dataset/train.csv')
test_df = pd.read_csv(
'Titanic dataset/test.csv')
print(train_df.head())
prepare data
drop unnecessary columns, these columns won't be useful in analysis and prediction
train_df = train_df.drop(['PassengerId', 'Name', 'Ticket'], axis=1)
test_df = test_df.drop(['Name', 'Ticket'], axis=1)
fill nan values of age
train_df['Age'].fillna(train_df['Age'].mean(), inplace=True)
fill nan values of cabin
train_df['Cabin'].fillna('U', inplace=True)
fill nan values of embarked
train_df['Embarked'].fillna('S', inplace=True)
fill nan values of fare
train_df['Fare'].fillna(
train_df['Fare'].mean(), inplace=True)
fill nan values of age in test data
test_df['Age'].fillna(test_df['Age'].mean(), inplace=True)
fill nan values of cabin in test data
test_df['Cabin'].fillna('U', inplace=True)
fill nan values of embarked in test data
test_df['Embarked'].fillna('S', inplace=True)
fill nan values of fare in test data
test_df['Fare'].fillna(test_df['Fare'].mean(), inplace=True)
map embarked to int
train_df['Embarked'] = train_df['Embarked'].map(
{'S': 0, 'C': 1, 'Q': 2}).astype(int)
map
test_df['Embarked'] = test_df['Embarked'].map(
{'S': 0, 'C': 1, 'Q': 2}).astype(int)
map train_df cabin to int
train_df['Cabin'] = train_df['Cabin'].map(
{'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'T': 7, 'U': 8}).astype(int)
map
test_df['Cabin'] = test_df['Cabin'].map(
{'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'T': 7, 'U': 8}).astype(int)
train_df['Sex'] to int (here stops working)
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