“The author explores the potential for individuals to become 'scapegoats,' taking responsibility without understanding the AI's actions, highlighting a critical point for discussion.”

“"I’d suggest some kind of token retention when you’re not using it... maybe something like 20% of what you don’t use in a day is credited as extra tokens for this month."”

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“This company has been unfairly labeled an 'AI loser,' a situation that should soon change.”

“The paper is available on ArXiv, suggesting peer review is not yet complete but the research is publicly accessible.”

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“ML researchers support openness in peer review but are concerned about resubmission bias.”

“The study investigates gender bias within emotion recognition capabilities of LLMs.”

“The article's title itself serves as the quote, directly conveying the judge's strong sentiment.”

“The summary indicates the core issue: students are facing false accusations. The article likely explores the reasons behind this, such as the detectors' inability to accurately distinguish between human and AI-generated text, or biases in the training data.”

“Data quality is paramount for building trustworthy AI.”

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“The article's premise is that deep learning on electronic medical records is doomed to fail.”

“Fortunately the boys have a surefire solution for who should lead the party into 2024, if the Dems are brave enough to take their advice.”

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