Learning from ChatGPT

#3182 Editorial Image Generation #3182  Content Removed It ain't right, but neither am I. Forgive me Lord. From < https://chatgpt.com/ >  ChatGPT 4o

#3181 New-Paradigm Telescope #3181 Discuss alternative, new paradigm ways to collect big light for astronomic imaging Below is a survey of “big-light” collection concepts that step outside the classic monolithic glass mirror/lens paradigm. I grouped them by the fundamental physical idea they exploit, then noted the state-of-the-art, what makes each approach a genuine leap, and the main technical hurdles. 1. In-space-manufactured liquid or fluidic optics Concept How it grabs more photons Why it’s new Status & hurdles FLUTE (Fluidic Telescope) Liquid spreads inside a rim, naturally forming a perfectly smooth mirror tens–hundreds m across. Removes launch-shroud size & mass limits; aperture can scale 10–100× Webb. NIAC Phase I/II; needs micro-g fluid control, cryogenic coatings, in-orbit curing. NASA Opportunity: Pair with robotic servicing to refill or refigure the surface, giving a “self-healing” primary. 2. Ultra-lightweight membrane / diffractive apertures Examples: D...

#3180 Machine Learning for Correcting a Low-Budget Astronomical Mirror #3180 I have a low-quality (i.e. cheap) 16-inch diameter mirror that I want to use for a Newtonian reflector telescope. How can I apply machine learning by using the mirror to image a square grid then compare that image to the original grid? Below is a practical, end-to-end workflow that treats your inexpensive 16-inch mirror as a black-box optical element, measures the aberrations it introduces with a square-grid target, and then trains a machine-learning model that can either (a) numerically “clean up” every photograph you take through the telescope or (b) drive an active-optics polishing / deformation routine if you later add actuators. 1 Optical test bench Item Why Tips Back-illuminated square grid (e.g. an LCD tablet showing a black/white checker or a printed transparency on a light panel) Provides thousands of precisely known feature points Use a grid whose cell size is ≤3 mm so that many cells are visible...

#3179 Ask Chat: How Can I Get Consistent, Deterministic Results from my LLM? #3179 How can I realistically get consistent, deterministic results from my LLM? “Deterministic” means every run of the same prompt returns the identical token sequence. With today’s LLM stacks you can get very close, but there are three different randomness layers you have to lock down: Layer Where the randomness creeps in What you can realistically do Decoder / sampling Temperature, top-p, top-k, nucleus/beam sampling Force a greedy decode: temperature = 0 , top_p = 1 , top_k = 1 (or simply omit the samplers entirely). This removes algorithmic randomness. GitHub Seeder / RNG state Each call seeds its random-number generator Pass an explicit, fixed seed. OpenAI exposes a seed field in ChatCompletion (beta) and returns a system_fingerprint so you can verify the backend hasn’t changed. OpenAI Cookbook OpenAI Platform Implementation / hardware Non-deterministic kernels, multithreading, model updates • Clo...