Learning from ChatGPT

#4617 Google TabFM -- Machine Learning from Tabular Data Google TabFM: The AI Breakthrough That's About to Replace Traditional ML https://youtu.be/XwYPRLMLcNs #4617  Tell me about TabFM TabFM (Tabular Foundation Model) is an open-weights zero-shot foundation model for tabular data developed by Google Research . Inspired by the success of time-series models like Google’s TimesFM and tabular research like TabPFN and TabICL , TabFM reframes classical tabular machine learning (classification and regression) as an In-Context Learning (ICL) task. Instead of training model weights from scratch or running extensive hyperparameter tuning on a new dataset, you pass your labeled training rows and target test rows together in a single prompt—generating predictions in a single forward pass. Key Architecture & How It Works TabFM uses a hybrid architecture designed to solve the heavy computational overhead of running transformers on two-dimensional grid structures: Column Attention...

#4616 Central Heterochromia ("Eye Rings") #4616  Some people have a very specific pattern in their eyes that catches anybody's attention. What is it? When people talk about a striking, attention-grabbing pattern in someone's eyes, they are usually referring to heterochromia or highly defined anatomical features within the iris structure. Because the iris is essentially a complex web of muscles, connective tissue, and pigment, it can form unique structural layouts. The specific variations that most instantly catch the human eye include: 1. Central Heterochromia ("Eye Rings") This occurs when the iris has two completely distinct colors arranged in concentric rings. Typically, there is a vibrant burst of gold, amber, or brown immediately surrounding the pupil, which then sharply transitions into a completely different color—like piercing blue or green—toward the outer edge. This creates a high-contrast, target-like effect that draws immediate focus. 2. Se...

#4615 The Hybrid Multi-Agent Systems Innovation Engine #4615  Describe a sequence of prompts that, for a specified domain: 1) find new opportunities to provide innovative solutions, 2) analyze the opportunities, 3) propose improvements, 4) describe implementation of the solutions. For example only, domain=EV batteries, opportunity=EV Battery capacity, health, efficiency, and chemistry Here is a structured, 4-step prompt sequence designed to take you from raw domain analysis to a concrete implementation roadmap. For maximum impact, you can run these prompts sequentially in your AI workspace, plugging in your chosen domain at the start. Phase 1: Opportunity Discovery Goal: Uncover hidden friction points, inefficiencies, and emerging trends within the target domain to find areas ripe for innovation. Prompt 1: > "Act as a leading innovation strategist and industry analyst. I want to explore the domain of [Insert Domain, e.g., EV batteries] , specifically focusing on [Insert F...