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Executive Summary The rapid advancement of artificial intelligence (AI) has elevated the challenge of ensuring these systems operate in accordance with human intentions from a theoretical concern to a critical Read More …
Section 1: The Alignment Imperative: Defining the Problem of Intent The rapid proliferation of artificial intelligence (AI) into nearly every facet of modern society has made the question of its Read More …
Section 1: The Dawn of Meta-Cognition: From Reactive Systems to Reflective Agents The field of artificial intelligence is undergoing a profound architectural shift, moving away from systems that merely react Read More …
Section 1: The New Data Paradigm: An Introduction to Synthetic Data Generation The development of large language models (LLMs) has been fundamentally constrained by a singular resource: high-quality training data. Read More …
Part I: The Reverse Engineering Paradigm As artificial intelligence systems, particularly deep neural networks, achieve superhuman performance and become integrated into high-stakes domains, the imperative to understand their internal decision-making Read More …
1. Executive Summary This report provides a detailed analysis of the evolving landscape of AI alignment, with a focus on two foundational methodologies: Reinforcement Learning from Human Feedback (RLHF) and Read More …
The Alignment Imperative: Defining the Core Challenge in Artificial Intelligence Safety Defining AI Alignment and its Place Within AI Safety In the field of artificial intelligence (AI), the concept of Read More …