#Machine Learning

AGI, hallucination, inference, LLMs — AI's vocabulary isn't just technical shorthand. It shapes who holds power in the conversation. A clear-eyed glossary with the questions behind the terms.

A new paper proves that the self-play training method behind AlphaGo and AlphaZero structurally fails on a whole category of games. What that means for AI systems making real-world decisions.

Guide Labs' Steerling-8B can trace every output back to its training data. Are we finally moving beyond black-box AI toward true interpretability?

Google's latest Gemini 3.1 Pro model achieves record benchmark scores, leading professional task evaluations. But as AI models advance every few months, what's the real endgame?

Flapping Airplanes raises $180M to challenge AI's data-hungry approach. Is the industry ready to move beyond the scaling paradigm toward research-driven breakthroughs?

Handshake's acquisition of Cleanlab reveals the fierce competition for high-quality data labeling talent as AI companies realize clean data matters more than raw volume.

Researchers are using biological approaches to understand how large language models work, treating them like vast alien creatures that have appeared in our midst. The mystery deepens as AI becomes more widespread.

Google researchers unveil Internal Reinforcement Learning (Internal RL), a technique that steers LLM internal activations for superior reasoning and robotics performance.

Stanford and NVIDIA's new TTT-E2E AI architecture allows models to learn continuously after deployment, achieving 2.7x faster inference on long-context tasks.

A viral Instagram account's literal translations reveal the critical 'translation gap' that AI struggles with. Here's why it matters for global business.

OpenAI's GPT-5.2 is more than a benchmark win. Our analysis shows why its math and science ability signals a new era for AI, threatening to disrupt R&D.