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Six Tips for Evaluating Your Nonprofit Training Session

Beth's Blog: How Nonprofits Can Use Social Media

Alternately, you may feel so good about it and say my job is done. Use Learning Theory. To guide evaluation, there are several learning theories, including The Four Levels of Evaluation, also referred to as the Kirkpatrick Evaluation Model , was created by Donald Kirkpatrick, Ph.D. 3. The Evaluation Survey.

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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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Other Papers About the Theory of Reward Learning

The AI Alignment Forum

Alternatively, see the main paper. The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret In this paper , we look at what happens when a learnt reward function is optimised. This means that it essentially makes the analysis in this paper more realistic, and more closely applicable to humans.

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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. To apply the results of my analysis to produce a community-driven program design specifically for implementation at the Santa Cruz Museum of Art & History (the MAH).

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Research directions Open Phil wants to fund in technical AI safety

The AI Alignment Forum

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. Alternative approaches to mitigating AI risks These research areas lie outside the scope of the clusters above. Sheshadri et al., and Zeng et al.

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AXRP Episode 40 - Jason Gross on Compact Proofs and Interpretability

The AI Alignment Forum

And you do some neat little tricks, but its like- Jason Gross (00:04:34): Interval propagation and case analysis. And a technical note: it needs to be in some first-order system or alternatively, you need to measure proof checking time as opposed to proof length. Daniel Filan (00:04:36): Yeah.

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