“Dictionaries, by their very nature, are merely tools for humans to temporarily fix meanings. However, the illusion of 'objectivity and neutrality' that their format conveys is the greatest...”

“"Claude is genuinely impressive, but the gap between 'looks right' and 'actually right' is bigger than I expected."”

“AI hype and the bubble that will follow are real, but it's also distorting our views of what the future could entail with current capabilities.”

“何をもって「うまく使えている」と言えるのか分からない”

“Does anyone else feel disillusioned with ChatGPT for a while very supportive and helpful now just being a jerk with bullsh*t answers”

“NL suggests a philosophy to design more expressive learning algorithms with more levels, resulting in higher-order in-context learning and potentially unlocking effective continual learning capabilities.”

“Your brain builds a simulation of what it *thinks* is out there and just uses your eyes to check if it's right.”

“The paper highlights that traditional models achieve inflated F1 scores due to label-persistence bias and fail on critical defect-transition cases. The proposed change-aware reasoning and multi-agent debate framework yields more balanced performance and improves sensitivity to defect introductions.”

“”

“If there isn’t something else out there and it’s really just LLM‘s then I’m not sure how the world can improve much with a confidently incorrect faster way to Google that tells you not to worry”

“The most criticized aspect is that the perception department has repeatedly changed leaders, but it is always unsatisfactory. Data collection work often spends a lot of money but fails to achieve results.”

“90% of AI entrepreneurs are running naked: What you thought was a moat is just an illusion.”

“”

“When OpenAI released a free web app called ChatGPT in late 2022, it changed the course of an entire industry—and several world economies.”

“Language models process text (*already* compressed human knowledge) using the same mechanism we use to learn from raw data.”

“The paper focuses on the impact of tacit biases in LLMs on negotiation performance.”

“Graded strength of comparative illusions is explained by Bayesian inference”

“The research is based on ArXiv.”

“The article is likely discussing LLM alignment, which refers to the problem of ensuring that LLMs behave in accordance with human values and intentions.”

“LLMs might also be a trap: they look like products and they look magic, but they aren’t.”

“Sanmi describes how evaluating model performance using nonlinear metrics can lead to the illusion that the model is rapidly gaining new capabilities, whereas linear metrics show smooth improvement as expected, casting doubt on the significance of emergence.”

“”

“The episode explores the idea that our perception of reality is a user interface designed by evolution.”

“The article likely discusses the inherent trade-offs between model complexity, performance, and interpretability in machine learning.”

“Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines.”

“The article likely critiques the naive application of machine learning.”