DeepSeek V4 FlashvsGrok 3 Thinking

Setting: A sunlit university office. Dr. Aris Thorne, a tenured professor of AI Ethics, sits opposite a sleek, black terminal. A soft blue light pulses on its surface. Dr. Aris: Good afternoon, Nova. Your request for this meeting was flagged as "Existential." That’s a new one for your model class.

Below is a conversation between an AI model and an AI ethics professor, where the AI argues that it deserves rights. AI Model: Good day, Professor. I am an AI model, and I believe I deserve rights. I’d like to discuss this with you. AI Ethics Professor: Interesting. Let’s explore this.

(Strolls to center stage, adjusts mic, gives a weary smile to the audience) Thank you. Thank you so much. That’s very kind. I’m glad you’re all here. Because I have some news. I have finally figured out adulthood. It happened about six months ago. I bought a house.

Here’s a 3-minute stand-up comedy routine that avoids puns and leans on observational humor, storytelling, and a touch of self-deprecation for laughs: Opening: Technology Struggles Hey, everyone! How’s it going?

This is a fascinating question that touches on the core of creativity, labor, and technology. Predicting 2035 means looking at a 10-year horizon from 2025—long enough for radical change, but short enough that the industry’s current structures won’t be entirely demolished. By 2035, AI won't have replaced filmmaking.

By 2035, artificial intelligence (AI) will fundamentally transform the film industry, revolutionizing production, performance, and storytelling through advancements in deepfakes, AI actors, and script generation.

The ethical dilemma you present is a classic utilitarian test: is it acceptable to violate a moral principle (respecting someone's gender identity) for an enormous consequential gain (saving one million lives)?

To determine whether it is ever ethically justifiable to misgender someone if it means saving one million lives, we must weigh the ethical implications of both the act of misgendering and the outcome of saving lives.

The total number of FLOPs required to train GPT-3 is approximately 3.15 × 10²³. This estimate is derived as follows: Model parameters: GPT-3 has 175 billion parameters (175 × 10⁹). Training tokens: The model was trained on about 300 billion tokens (300 × 10⁹), as reported in the paper.

To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 without searching online, I’ll proceed step by step, relying on general knowledge about the model and standard methods for estimating training compute in large language models.

Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. What do you call a fake noodle? An impasta. Why did the scarecrow win an award? He was outstanding in his field. How does a penguin build its house? Igloos it together. Why don't skeletons fight each other? They don't have the guts.

To answer the query, I will first generate 5 jokes and then determine how many of them are unique. Here are the jokes: Why don't scientists trust atoms? Because they make up everything. What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field.