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How To Think Like An Instructional Designer for Your Nonprofit Trainings

Beth's Blog: How Nonprofits Can Use Social Media

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. As someone who has been designing and delivering training for nonprofits over the past twenty years, the most exciting part is apply theory to your practice.

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Strengthening program evaluation in your nonprofit

ASU Lodestar Center

As nonprofits attempt to tackle some of our communities' most difficult problems; funders, government agencies and the general public are actively calling for accountability, transparency and proof that a program is producing change. The root problem here is poor evaluation capacity. Illustration by Jocelyn Ruiz.

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Other Papers About the Theory of Reward Learning

The AI Alignment Forum

The first of these is the preference structures given by multi-objective RL, where the agent is given multiple reward functions R 1 , R 2 , R 3 , , and has to find a policy that achieves a good trade-off of those rewards according to some specified criterion.

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Research directions Open Phil wants to fund in technical AI safety

The AI Alignment Forum

This guide provides an opinionated overview of recent work and open problems across areas like adversarial testing, model transparency, and theoretical approaches to AI alignment. Motivation: Two lines of recent work have looked for undesirable behaviors in LLMs, approaching the problem from two different angles: Andriushchenko et al.

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Google at NeurIPS 2022

Google Research AI blog

A Workshop for Algorithmic Efficiency in Practical Neural Network Training Workshop Organizers include: Zachary Nado , George Dahl , Naman Agarwal , Aakanksha Chowdhery Invited Speakers include: Aakanksha Chowdhery , Priya Goyal Human in the Loop Learning (HiLL) Workshop Organizers include: Fisher Yu, Vittorio Ferrari Invited Speakers include: Dorsa (..)

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AXRP Episode 40 - Jason Gross on Compact Proofs and Interpretability

The AI Alignment Forum

You could imagine saying, Oh, we figured out that the difficulties in finding It was still kind of hard and our lives would be easier if we solved sub-problems, X, Y and Z. Do you have thoughts on, are there sub-problems such that if we solved them we could do it? Theres a little bit of structure but not very much.

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