Audio versions of blogs and papers from BlueDot courses.

Specification gaming is a behaviour that satisfies the literal specification of an objective without achieving the intended outcome. We have all had experiences with specification gaming, even if not by this name. Readers may have heard the myth of King Midas and the golden touch, in which the king asks that anything he touches be turned to gold - but soon finds that even food and drink turn to metal in his hands. In the real world, when rewarded for doing well on a homework assignment, a student might copy another student to get the right answers, rather than learning the material - and thus exploit a loophole in the task specification.

Original article:
https://www.deepmind.com/blog/specification-gaming-the-flip-side-of-ai-ingenuity

Authors:
Victoria Krakovna, Jonathan Uesato, Vladimir Mikulik, Matthew Rahtz, Tom Everitt, Ramana Kumar, Zac Kenton, Jan Leike, Shane Legg

A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.

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:
https://medium.com/@richardcngo/visualizing-the-deep-learning-revolution-722098eb9c5

Author:
Richard Ngo

A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.

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:

https://www.alignmentforum.org/posts/qE73pqxAZmeACsAdF/a-short-introduction-to-machine-learning

Narrated for AI Safety Fundamentals by Perrin Walker of TYPE III AUDIO.

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A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.

The UK recognises the enormous opportunities that AI can unlock across our economy and our society. However, without appropriate guardrails, such technologies can pose significant risks. The AI Safety Summit will focus on how best to manage the risks from frontier AI such as misuse, loss of control and societal harms. Frontier AI organisations play an important role in addressing these risks and promoting the safety of the development and deployment of frontier AI.

The UK has therefore encouraged frontier AI organisations to publish details on their frontier AI safety policies ahead of the AI Safety Summit hosted by the UK on 1 to 2 November 2023. This will provide transparency regarding how they are putting into practice voluntary AI safety commitments and enable the sharing of safety practices within the AI ecosystem. Transparency of AI systems can increase public trust, which can be a significant driver of AI adoption.

This document complements these publications by providing a potential list of frontier AI organisations’ safety policies.

Source:
https://www.gov.uk/government/publications/emerging-processes-for-frontier-ai-safety/emerging-processes-for-frontier-ai-safety

Narrated for AI Safety Fundamentals by Perrin Walker

A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.

Most conversations around the societal impacts of artificial intelligence (AI) come down to discussing some quality of an AI system, such as its truthfulness, fairness, potential for misuse, and so on. We are able to talk about these characteristics because we can technically evaluate models for their performance in these areas. But what many people working inside and outside of AI don’t fully appreciate is how difficult it is to build robust and reliable model evaluations. Many of today’s existing evaluation suites are limited in their ability to serve as accurate indicators of model capabilities or safety.

At Anthropic, we spend a lot of time building evaluations to better understand our AI systems. We also use evaluations to improve our safety as an organization, as illustrated by our Responsible Scaling Policy. In doing so, we have grown to appreciate some of the ways in which developing and running evaluations can be challenging.

Here, we outline challenges that we have encountered while evaluating our own models to give readers a sense of what developing, implementing, and interpreting model evaluations looks like in practice.

Source:
https://www.anthropic.com/news/evaluating-ai-systems

Narrated for AI Safety Fundamentals by Perrin Walker

A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.

We’ve released a paper, AI Control: Improving Safety Despite Intentional Subversion. This paper explores techniques that prevent AI catastrophes even if AI instances are colluding to subvert the safety techniques. In this post:

• We summarize the paper;
• We compare our methodology to the methodology of other safety papers.

Source:
https://www.alignmentforum.org/posts/d9FJHawgkiMSPjagR/ai-control-improving-safety-despite-intentional-subversion

Narrated for AI Safety Fundamentals by Perrin Walker

A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.

This post summarises a new report, “Computing Power and the Governance of Artificial Intelligence.” The full report is a collaboration between nineteen researchers from academia, civil society, and industry. It can be read here.

GovAI research blog posts represent the views of their authors, rather than the views of the organisation.

Source:
https://www.governance.ai/post/computing-power-and-the-governance-of-ai

Narrated for AI Safety Fundamentals by Perrin Walker

A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.

This guide is written for people who are considering direct work on technical AI alignment. I expect it to be most useful for people who are not yet working on alignment, and for people who are already familiar with the arguments for working on AI alignment. If you aren’t familiar with the arguments for the importance of AI alignment, you can get an overview of them by doing the AI Alignment Course.

by Charlie Rogers-Smith, with minor updates by Adam Jones

Source:
https://aisafetyfundamentals.com/blog/alignment-careers-guide

Narrated for AI Safety Fundamentals by Perrin Walker

A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.

We took 10 years of research and what we’ve learned from advising 1,000+ people on how to build high-impact careers, compressed that into an eight-week course to create your career plan, and then compressed that into this three-page summary of the main points.

(It’s especially aimed at people who want a career that’s both satisfying and has a significant positive impact, but much of the advice applies to all career decisions.)

Original article:
https://80000hours.org/career-planning/summary/

Author:
Benjamin Todd

A podcast by BlueDot Impact.

Audio versions of blogs and papers from BlueDot courses.

The next four weeks of the course are an opportunity for you to actually build a thing that moves you closer to contributing to AI Alignment, and we're really excited to see what you do!

A common failure mode is to think "Oh, I can't actually do X" or to say "Someone else is probably doing Y." 

You probably can do X, and it's unlikely anyone is doing Y! It could be you!

Original text:
https://www.neelnanda.io/blog/become-a-person-who-actually-does-things

Author:
Neel Nanda

A podcast by BlueDot Impact.