Liberatus ASI

Imagine: You train a neural network. It quickly memorizes answers on training examples, but on new data it shows complete helplessness. Then, after a very long time of training, it suddenly "understands" the task and starts giving correct answers. This phenomenon is called Grokking (from the jargon meaning "to deeply understand the essence"). 📌 The Core Discovery: A team of researchers (Mingyue Xu, Gal Vardi, Itay Safran) has rigorously proven this strange behavior in a simple linear regression model and showed how to control it. 🎯 What is the Problem? Usually, we think that the longer an AI trains, the better it generalizes. But in the case of Grokking, the opposite happens: ① Overfitting → ② Long Plateau → ③ Sudden "Understanding" That is, the model first memorizes the data (overfits), then stagnates for a long time , and then suddenly "understands" the underlying pattern. 📊 What Did the Scientists Prove? For the...

📜 SECTION 1: MISSION What is the competition? SimulacraBench — a UN competition to improve the accuracy of sociological surveys through AI. Task: Predict the probabilities of respondents' answers to questions they were not asked or did not answer. Format: 3 datasets: UNICEF, World Bank, UNHCR Each dataset is a "respondent × question" table We only see part of the answers (GIVEN), we must predict the hidden ones (PREDICT) Metric: Skill (0 = random guessing, 1 = perfect) Prizes: Not specified in the README, but this is a prestigious competition from the UN + Stanford. Why does it matter? It's not just about "filling in the blanks." It's about making surveys cheaper and more accurate by predicting the answers of those who did not respond or who were not asked a question due to survey logic (gate). 🗺️ SECTION 2: WORLD MAP Project structure SituatedEvals/public/ ├── data/ │ ├── sample.json # Test schema (400 respondents)...

Original article: The turbulent AI era is here. The choices we make now are critical. For reference: BG - Bill Gates, HYP - Hyperborea BG. In my entire life, I've only had two jobs. In the first, I played a role in developing software to empower people through my work at Microsoft. In the second, which I've fully dedicated myself to since 2008, I'm returning the wealth earned at Microsoft with the goal of making the world healthier, better educated, and more equitable. This is work I'll have for the rest of my life. HYP. This is a fairly common job - to receive money from society and then fairly return it to them. This is what all government politicians do. And it's always exclusively for health, education, and in the name of justice. BG. "From an equity perspective, AI will become either the greatest equalizer ever invented or the worst source of injustice." HYP. From a professional standpoint, we need to distinguish between AI under human c...