Within the coming decades, artificial general intelligence (AGI) may surpass human capabilities at a wide range of important tasks. We outline a case for expecting that, without substantial effort to prevent it, AGIs could learn to pursue goals which are undesirable (i.e. misaligned) from a human perspective. We argue that if AGIs are trained in ways similar to today's most capable models, they could learn to act deceptively to receive higher reward, learn internally-represented goals which generalize beyond their training distributions, and pursue those goals using power-seeking strategies. We outline how the deployment of misaligned AGIs might irreversibly undermine human control over the world, and briefly review research directions aimed at preventing this outcome. Original article: Authors: Richard Ngo, Lawrence Chan, Sören Mindermann --- This article is featured on the AI Safety Fundamentals: Alignment course curriculum. Narrated by TYPE III AUDIO on behalf of BlueDot Impact .

People often ask me for career advice related to AGI safety. This post summarizes the advice I most commonly give. I’ve split it into three sections: general mindset, alignment research and governance work. For each of the latter two, I start with high-level advice aimed primarily at students and those early in their careers, then dig into more details of the field. See also this post I wrote two years ago, containing a bunch of fairly general career advice. ## General mindset In order to have a big impact on the world you need to find a big lever. This document assumes that you think, as I do, that AGI safety is the biggest such lever. There are many ways to pull on that lever, though—from research and engineering to operations and field-building to politics and communications. I encourage you to choose between these based primarily on your personal fit—a combination of what you're really good at and what you really enjoy. In my opinion the difference between being a great versus a mediocre fit swamps other differences in the impactfulness of most pairs of AGI-safety-related jobs. Original article: Narrated for the Effective Altruism Forum by TYPE III AUDIO .

Despite the current popularity of machine learning, I haven’t found any short introductions to it which quite match the way I prefer to introduce people to the field. So here’s my own. Compared with other introductions, I’ve focused less on explaining each concept in detail, and more on explaining how they relate to other important concepts in AI, especially in diagram form. If you’re new to machine learning, you shouldn’t expect to fully understand most of the concepts explained here just after reading this post - the goal is instead to provide a broad framework which will contextualise more detailed explanations you’ll receive from elsewhere. I’m aware that high-level taxonomies can be controversial, and also that it’s easy to fall into the illusion of transparency when trying to introduce a field; so suggestions for improvements are very welcome! The key ideas are contained in this summary diagram: First, some quick clarifications: None of the boxes are meant to be comprehensive; we could add more items to any of them. So you should picture each list ending with “and others”. The distinction between tasks and techniques is not a firm or standard categorisation; it’s just the best way I’ve found so far to lay things out. The summary is explicitly from an AI-centric perspective. For example, statistical modeling and optimization are fields in their own right; but for our current purposes we can think of them as machine learning techniques. Original text: Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO .

The field of AI has undergone a revolution over the last decade, driven by the success of deep learning techniques. This post aims to convey three ideas using a series of illustrative examples: There have been huge jumps in the capabilities of AIs over the last decade, to the point where it’s becoming hard to specify tasks that AIs can’t do. This progress has been primarily driven by scaling up a handful of relatively simple algorithms (rather than by developing a more principled or scientific understanding of deep learning). Very few people predicted that progress would be anywhere near this fast; but many of those who did also predict that we might face existential risk from AGI in the coming decades. I’ll focus on four domains: vision, games, language-based tasks, and science. The first two have more limited real-world applications, but provide particularly graphic and intuitive examples of the pace of progress. Original article: Author: Richard Ngo Narrated for the AI Safety Fundamentals Course by TYPE III AUDIO .

Gradient hacking is a hypothesized phenomenon where: A model has knowledge about possible training trajectories which isn’t being used by its training algorithms when choosing updates (such as knowledge about non-local features of its loss landscape which aren’t taken into account by local optimization algorithms). The model uses that knowledge to influence its medium-term training trajectory, even if the effects wash out in the long term. Below I give some potential examples of gradient hacking, divided into those which exploit RL credit assignment and those which exploit gradient descent itself. My concern is that models might use techniques like these either to influence which goals they develop, or to fool our interpretability techniques. Even if those effects don’t last in the long term, they might last until the model is smart enough to misbehave in other ways (e.g.  specification gaming , or  reward tampering ), or until it’s deployed in the real world—especially in the RL examples, since convergence to a global optimum seems unrealistic (and ill-defined) for RL policies trained on real-world data. However, since gradient hacking isn’t very well-understood right now, both the definition above and the examples below should only be considered preliminary. Source: Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO . --- Share feedback on this narration .

This report explores the core case for why the development of artificial general intelligence (AGI) might pose an existential threat to humanity. It stems from my dissatisfaction with existing arguments on this topic: early work is less relevant in the context of modern machine learning, while more recent work is scattered and brief. This report aims to fill that gap by providing a detailed investigation into the potential risk from AGI misbehaviour, grounded by our current knowledge of machine learning, and highlighting important uncertain ties. It identifies four key premises, evaluates existing arguments about them, and outlines some novel considerations for each. Source: Narrated for AI Safety Fundamentals by TYPE III AUDIO . --- Share feedback on this narration .