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client: lesswrong
project_id: curated
feed_id: ai_safety
narrator: pw
qa: mds
qa_time: 0h45m
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This post is an attempt to gesture at a class of AI notkilleveryoneism (alignment) problem that seems to me to go largely unrecognized. E.g., it isn’t discussed (or at least I don't recognize it) in the recent plans written up by OpenAI (1,2), by DeepMind’s alignment team, or by Anthropic, and I know of no other acknowledgment of this issue by major labs.

You could think of this as a fragment of my answer to “Where do plans like OpenAI’s ‘Our Approach to Alignment Research’ fail?”, as discussed in Rob and Eliezer’s challenge for AGI organizations and readers. Note that it would only be a fragment of the reply; there's a lot more to say about why AI alignment is a particularly tricky task to task an AI with. (Some of which Eliezer gestures at in a follow-up to his interview on Bankless.)

Original article:
https://www.lesswrong.com/posts/XWwvwytieLtEWaFJX/deep-deceptiveness

Narrated for LessWrong by TYPE III AUDIO.

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client: agi_sf
project_id: core_readings
feed_id: agi_sf__alignment
narrator: pw
qa: mds
qa_time: 0h30m
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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

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This article is featured on the AGI Safety Fundamentals: Alignment course curriculum.

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client: agi_sf
project_id: core_readings
feed_id: agi_sf__alignment
narrator: pw
qa: mds
qa_time: 0h30m
---

It seems unlikely that humans are near the ceiling of possible intelligences, rather than simply being the first such intelligence that happened to evolve. Computers far outperform humans in many narrow niches (e.g. arithmetic, chess, memory size), and there is reason to believe that similar large improvements over human performance are possible for general reasoning, technology design, and other tasks of interest. As occasional AI critic Jack Schwartz (1987) wrote:

"If artificial intelligences can be created at all, there is little reason to believe that initial successes could not lead swiftly to the construction of artificial superintelligences able to explore significant mathematical, scientific, or engi-neering alternatives at a rate far exceeding human ability, or to generate plans and take action on them with equally overwhelming speed. Since man’s near-monopoly of all higher forms of intelligence has been one of the most basic facts of human existence throughout the past history of this planet, such developments would clearly create a new economics, a new sociology, and a new history."

Why might AI “lead swiftly” to machine superintelligence? Below we consider some reasons.

Original article:
https://drive.google.com/file/d/1QxMuScnYvyq-XmxYeqBRHKz7cZoOosHr/view

Authors:
Luke Muehlhauser, Anna Salamon

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This article is featured on the AGI Safety Fundamentals: Alignment course curriculum.

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qa_time: 0h30m
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AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

Original article:
https://arxiv.org/abs/2108.07258

Authors:
Bommasani et al.

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This article is featured on the AGI Safety Fundamentals: Alignment course curriculum.

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qa_time: 0h30m
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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

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This article is featured on the AGI Safety Fundamentals: Alignment course curriculum.

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qa_time: 0h15m
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According to the orthogonality thesis, intelligent agents may have an enormous range of possible final goals. Nevertheless, according to what we may term the “instrumental convergence” thesis, there are some instrumental goals likely to be pursued by almost any intelligent agent, because there are some objectives that are useful intermediaries to the achievement of almost any final goal. We can formulate this thesis as follows:

The instrumental convergence thesis:
"Several instrumental values can be identified which are convergent in the sense that their attainment would increase the chances of the agent’s goal being realized for a wide range of final goals and a wide range of situations, implying that these instrumental values are likely to be pursued by a broad spectrum of situated intelligent agents."

Original article:
https://drive.google.com/file/d/1KewDov1taegTzrqJ4uurmJ2CJ0Y72EU3/view

Author:
Nick Bostrom

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This article is featured on the AGI Safety Fundamentals: Alignment course curriculum.

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feed_id: agi_sf__alignment
narrator: pw
qa: mds
qa_time: 0h15m
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One approach to the AI control problem goes like this:

• Observe what the user of the system says and does.
• Infer the user’s preferences.
• Try to make the world better according to the user’s preference, perhaps while working alongside the user and asking clarifying questions.

This approach has the major advantage that we can begin empirical work today — we can actually build systems which observe user behavior, try to figure out what the user wants, and then help with that. There are many applications that people care about already, and we can set to work on making rich toy models.

It seems great to develop these capabilities in parallel with other AI progress, and to address whatever difficulties actually arise, as they arise. That is, in each domain where AI can act effectively, we’d like to ensure that AI can also act effectively in the service of goals inferred from users (and that this inference is good enough to support foreseeable applications).

This approach gives us a nice, concrete model of each difficulty we are trying to address. It also provides a relatively clear indicator of whether our ability to control AI lags behind our ability to build it. And by being technically interesting and economically meaningful now, it can help actually integrate AI control with AI practice.

Overall I think that this is a particularly promising angle on the AI safety problem.

Original article:
https://www.alignmentforum.org/posts/h9DesGT3WT9u2k7Hr/the-easy-goal-inference-problem-is-still-hard

Authors:
Paul Christiano

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This article is featured on the AGI Safety Fundamentals: Alignment course curriculum.

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client: ea_forum
project_id: curated
narrator: jc
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In my last essay, I looked at two stories (brute preference for systematic-ness, and money-pumps) about why ethical anti-realists should still be interested in ethics – two stories about why the “philosophy game” is worth playing, even if there are no objective normative truths, and you’re free to do whatever you want. I think some versions of these stories might well have a role to play; but I find that on their own, they don’t fully capture what feels alive to me about ethics. Here I try to say something that gets closer.

Original article:

https://joecarlsmith.com/2023/02/17/seeing-more-whole

Narrated by Joe Carlsmith and included on the Effective Altruism Forum by TYPE III AUDIO.

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client: ea_forum
project_id: summaries
narrator: cs
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Original article:
https://forum.effectivealtruism.org/posts/idpbfmPjHFCvzj46L/ea-and-lw-forum-weekly-summary-13th-19th-march-2023

This is part of a weekly series summarizing the top posts on the EA Forum — you can see the full collection here. The first post includes some details on purpose and methodology. Feedback, thoughts, and corrections are welcomed.

Narrated by Coleman Jackson Snell. Summaries written by Zoe Williams (Rethink Priorities).

Published by TYPE III AUDIO on behalf of the Effective Altruism Forum.

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client: cea
project_id: articles
narrator: pw
qa: mds
qa_time: 1h15m
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Effective altruism is a project that aims to find the best ways to help others, and put them into practice.

It’s both a research field, which aims to identify the world’s most pressing problems and the best solutions to them, and a practical community that aims to use those findings to do good.

This project matters because, while many attempts to do good fail, some are enormously effective. For instance, some charities help 100 or even 1,000 times as many people as others, when given the same amount of resources.

This means that by thinking carefully about the best ways to help, we can do far more to tackle the world’s biggest problems.

Original article:
https://www.effectivealtruism.org/articles/introduction-to-effective-altruism

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