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Six Tips for Evaluating Your Nonprofit Training Session

Beth's Blog: How Nonprofits Can Use Social Media

There are two different methods to evaluate your training. Alternately, you may feel so good about it and say my job is done. Use Learning Theory. This includes documenting your session, reviewing your decks and exercises, analyzing your instructional design, and figuring out how to improve it.

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The Theoretical Reward Learning Research Agenda: Introduction and Motivation

The AI Alignment Forum

Concretely, this research agenda involves answering questions such as: What is the right method for expressing goals and instructions to AI systems? The next question is whether or not a given reward learning method is guaranteed to converge to a reward function that is sufficiently accurate in this sense.

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How To Think Like An Instructional Designer for Your Nonprofit Trainings

Beth's Blog: How Nonprofits Can Use Social Media

Designing and delivering a training to a nonprofit audience is not about extreme content delivery or putting together a PowerPoint and answering questions. If you want to get results, you need to think about instructional design and learning theory. And, there is no shortage of learning theories and research.

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Twittering and Forgetting

Beth's Blog: How Nonprofits Can Use Social Media

The title of this post is a play on a book I read The Book of Learning and Forgetting by Frank Smith in 1998 when I was working with arts educators on integrating technology into their lesson plans. I would recommend technology resources and they would share books about learning. and tag it. Many times I forget.

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Research directions Open Phil wants to fund in technical AI safety

The AI Alignment Forum

Were interested in more research on this, and other stress tests of todays state-of-the-art alignment methods. Alternatives to adversarial training : Adversarial training (and the rest of todays best alignment techniques) have failed to create LLM agents that reliably avoid misaligned goals.

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Other Papers About the Theory of Reward Learning

The AI Alignment Forum

We also managed to leverage these results to produce a new method for conservative optimisation, that tells you how much (and in what way) you can optimise a proxy reward, based on the quality of that proxy (as measured by a STARC metric ), in order to be guaranteed that the true reward doesnt decrease (and thereby prevent the Goodhart drop).

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Guest Post: Community and Civic Engagement in Museum Programs

Museum 2.0

The purpose of my thesis was two-fold: To research and analyze community and civic engagement practices, methods, theories and examples in other museum programs. This can be accomplished through a variety of feedback methods conducted both inside and outside the museum.

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