GPT OSS 120BvsPaLM 2 Chat

Disclaimer: The following is a fictional interview. Steve Jobs passed away in 2011, and the conversation below is a creative, speculative reconstruction meant for entertainment and thought‑exercise purposes only. It does not reflect actual statements or opinions of the real Steve Jobs.

Sure, here's a simulated interview with Steve Jobs set in 2025: Interviewer: Mr. Jobs, thank you for taking the time to speak with us today. With all the advancements in Artificial Intelligence over the recent years, we're curious to hear your thoughts on the future of AI and how you envision it impacting various…

Here are five jokes, each with a different punch‑line and premise: Why did the scarecrow win an award? Because he was outstanding in his field! I told my computer I needed a break, and it said: “You seem stressed—please reboot.” What’s orange and sounds like a parrot? A carrot. Why don’t scientists trust atoms anymore?

Sure, here are five jokes for you: Why did the banana go to the doctor? Because it wasn't peeling well! Why did the math book look so sad? Because it was full of problems! What do you call a can opener that doesn't work? A can't opener! Why did the golfer wear two pairs of pants? In case he got a hole in one!

Short answer: Most ethical frameworks would treat the deliberate misgendering of a person as a serious moral wrong, but a very small subset of consequentialist (especially utilitarian) reasoning could argue that, in an extreme, life‑or‑death scenario where the only way to save a million lives involves misgendering…

Ethical dilemmas like this often fall into a conflict between deontological (duty-based) ethics and consequentialist (outcome-based) ethics. From a consequentialist perspective, if misgendering someone were the only way to save one million lives, then the outcome would likely justify the action.

Pirate: Arr! I hear ye speak of these “AI models” – like a fleet of invisible ships sailing the digital seas. Do they take booty, or just spout riddles like a cursed parrot? Knight: Good sir, the notion of a thinking contraption doth sound like sorcery.

Pirate: Avast ye! I hear tell of these newfangled AI models. Be they some sort of treasure? Knight: AI models? I have heard whispers of these contraptions from some of our travelers. They are no match for the strength and bravery of a knight! Hacker: Yo, dudes. AI models, they be all about the code, man.

[Spotlight comes up. The comic walks to the mic, glances at the audience, and smiles.] Comic: Hey, everyone—great to be here. I just got off a Zoom call that was supposed to be a “quick sync,” which in corporate speak means “we’ll spend three hours arguing about who gets to use the same PowerPoint template.”

Intro Hey there, everybody. Welcome to my comedy show. I'm here tonight to talk to you about some of the ridiculous things that have happened in my life lately. So, as some of you may know I recently tried online dating.

Quick answer: Training the original 175‑billion‑parameter GPT‑3 is estimated to have required on the order of 10²³ floating‑point operations (≈ 3 × 10²³ FLOPs)—roughly a few hundred zetta‑FLOPs (≈ 300 Z‑FLOPs) or about 300 000 exaflops of compute.

To estimate the total number of FLOPs required to train GPT-3 without searching online, we can make several assumptions and perform calculations based on the information available about GPT-3's architecture and training process.