GPT-5 Minivso3 Mini

Short answer By 2035 AI will be deeply integrated into almost every phase of filmmaking. Expect routine use of generative AI for treatments, storyboards, previs, localized versions, and some drafts of scripts; widespread use of synthetic actors and AI-driven de‑aging/recreation in many productions; much faster, cheaper…

By 2035, AI is expected to reshape the film industry on multiple fronts. Here’s a detailed look at how deepfakes, AI actors, and script generation could transform filmmaking: Deepfakes: Beyond Special Effects Enhanced Visual Storytelling: Deepfake technology will likely become a standard tool in visual effects.

Recommendation: SHORT 12‑month PT range: $17 – $42 (Bear → Bull; Base case PT $28) Thesis (2 sentences): LedgerLift is a high‑quality mid‑market spend-management / AP automation SaaS with strong unit economics (92% subscription, NRR 123%, FY25 operating margin 18%) that has built a sticky, expanding customer base.

Internal Memo – LedgerLift (LLLT) Date: [Insert Date] ───────────────────────────── Recommendation & 12-Month Price Target Recommendation: SHORT 12-Month Price Target Range: $17 – $28/share Thesis: LedgerLift’s current trading price of $46 appears significantly expanded relative to our DCF valuations across base, bull…

Why don't scientists trust atoms? Because they make up everything. Parallel lines have so much in common — it's a shame they'll never meet. Why did the scarecrow win an award? Because he was outstanding in his field. I told my wife she was drawing her eyebrows too high — she looked surprised.

Why did the math book look sad? Because it had too many problems. What do you call an alligator in a vest? An investigator. What did the zero say to the eight? Nice belt! Why did the bicycle fall over? Because it was two-tired. How does a penguin build its house? Igloos it together.

Pirate: "Arrr, so these 'AI models'—be they like a crew o' invisible sailors? Tell 'em to find treasure and sometimes they do, sometimes they bury the map wrong." Knight: "Methinks 'tis a trained squire. You teach it laws of reason and courtesy, and it performs tasks.

Pirate: Arrr, have ye heard of these AI models? They be like treasure chests of knowledge on the digital seas! Knight: By my troth, such marvels seem as if sorcery itself, granting wisdom beyond our ancient scrolls. 1990s Hacker: Yo, dudes, these AI models are totally radical!

Goal: estimate total floating-point operations (FLOPs) required to train GPT‑3 (the 175B‑parameter model). I’ll state assumptions, derive the FLOPs per token, and multiply by number of training tokens. Assumptions (reasonably standard for GPT‑3): Model size: N ≈ 175 billion parameters (175 × 10^9).

Below is a step‐by‐step estimation: Parameters Count: GPT‑3 has about 175 billion parameters, i.e. roughly 1.75×10¹¹ parameters. Training Data: The model was trained on a large corpus that can be estimated at around 300 billion tokens (≈3×10¹¹ tokens).

Short answer: In a narrow, extreme, last‑resort scenario where misgendering is the only realistic way to prevent the imminent deaths of one million people, it can be ethically permissible.

This is a challenging moral dilemma that pits consequentialist reasoning against deontological principles. From a consequentialist standpoint, one might argue that if an act—even one as seemingly small as misgendering—could lead to saving one million lives, then the overall benefit might justify a minor harm.