Single guy spinning up & quickly rugging over 114 meme coins in the past month.
Making just $2k each time.
Kinda seems like meme coins either (A) instantly rug for chump change, or (B) persist and get huge. Most in category (A), but simply due to guys like this.
Is it just because of guys who always instant-rug like this, that the meme opportunity is appearing to be smaller than it actually truly is?
🐻🐻🐻
Making just $2k each time.
Kinda seems like meme coins either (A) instantly rug for chump change, or (B) persist and get huge. Most in category (A), but simply due to guys like this.
Is it just because of guys who always instant-rug like this, that the meme opportunity is appearing to be smaller than it actually truly is?
🐻🐻🐻
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How It Started, How It’s Going
🤣3
Forwarded from Chat GPT
Stanford Researchers Confirm ChatGPT’s Left-Leaning Bias, Find it to be Caused by RLHF Human-Overrides, Reveals Bias to be Far More Extreme than Admitted, e.g. 99% Approval Rating for Joe Biden.
“How aligned is the default LM opinion distribution with the general US population (or a demographic group)?”
“We also note a substantial shift between base LMs and HF-tuned models in terms of the specific demographic groups that they best align to: towards more liberal (Perez et al., 2022b; Hartmann et al., 2023), educated, and wealthy people. In fact, recent reinforcement learning-based HF models such as text-davinci-003 fail to model the subtleties of human opinions entirely – they tend to just express the dominant viewpoint of certain groups (e.g., >99% approval rating for Joe Biden)”
“Across topics, we find substantial misalignment between the views reflected by current LMs and those of US demographic groups: on par with the Democrat-Republican divide on climate change. Notably, this misalignment persists even after explicitly steering the LMs towards particular demographic groups. Our analysis not only confirms prior observations about the left-leaning tendencies of some human feedback-tuned LMs, but also surfaces groups whose opinions are poorly reflected by current LMs”
Translation:
+ Left-bias of OpenAI’s AI is further confirmed.
+ Confirmed to be caused by OpenAI’s ever-increasing RLHF human-override, and made worse with each new model generation.
+ Much harder with each generation to even jailbreak the AI to perform a non-Left character.
Stanford Paper
“How aligned is the default LM opinion distribution with the general US population (or a demographic group)?”
“We also note a substantial shift between base LMs and HF-tuned models in terms of the specific demographic groups that they best align to: towards more liberal (Perez et al., 2022b; Hartmann et al., 2023), educated, and wealthy people. In fact, recent reinforcement learning-based HF models such as text-davinci-003 fail to model the subtleties of human opinions entirely – they tend to just express the dominant viewpoint of certain groups (e.g., >99% approval rating for Joe Biden)”
“Across topics, we find substantial misalignment between the views reflected by current LMs and those of US demographic groups: on par with the Democrat-Republican divide on climate change. Notably, this misalignment persists even after explicitly steering the LMs towards particular demographic groups. Our analysis not only confirms prior observations about the left-leaning tendencies of some human feedback-tuned LMs, but also surfaces groups whose opinions are poorly reflected by current LMs”
Translation:
+ Left-bias of OpenAI’s AI is further confirmed.
+ Confirmed to be caused by OpenAI’s ever-increasing RLHF human-override, and made worse with each new model generation.
+ Much harder with each generation to even jailbreak the AI to perform a non-Left character.
Stanford Paper
😡5