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Listen to the careful and deliberate speech of qualified scientists and compare it to the advocates for any “data-driven” endeavor and you will understand who is more credible.

If you have no experience with a phenomenon, it is impossible to know if it is a problem.

“If it is contrary to my beliefs, it cannot be true.” This can have disastrous results.

“lack of transparency, corruption of messaging, and magnification of these distortions” are characteristics of communication in digital age... yeah... so... literacy is changing.

Students can be motivated and engaged, but not interested. Interested are motivated, engaged, interested.

“If you don’t like change, you’ll like irrelevance even less.” I’m getting t-shirts and bumper stickers with this message.

Everyone is born a genius. Society de-geniuses them." -Buckminster Fuller

“Preparing students for traditional careers and pathways is a disservice not only to their future but also to the future is society....” can’t argue with that. So that means learning to learning, adaptability, and broad skills must dominate

I just heard a textbook publisher offering to buy back materials… like goggles for chemistry courses. Seriously? We don’t have safety officers saying “no” to this?

Ultimately, the quest to define science highlights its complexity. It involves more than just confirming evidence; it's about designing severe tests, showing progress, and being open to criticism.

If we can't find a clear criterion, perhaps decisions about which theories get funding or are taught should depend not just on being "scientific," but on being good theories.

The demarcation problem has not found an adequate solution. Is something like creationism simply "lousy science" rather than "unscientific"? The distinction remains difficult.

Beyond Popper, other possible characteristics of pseudoscience include a lack of progress over time compared to rivals, or the absence of a clear mechanism for proposed effects. But these criteria have their own issues

What makes something scientific? Philosophers call it the problem of demarcation, finding a principled way to distinguish genuine sciences from pseudosciences. It's a surprisingly hard question!

Monitoring latency (the time from API request to response) is important for production LLM applications. Optimizing prompt design and token usage can help reduce latency.

Deploying GenAI applications to production requires managing challenges like latency, scalability, costs, quotas, and ensuring observability. Best practices address these areas.

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