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Why Movement Is the Killer Learning App for Nonprofits

Beth's Blog: How Nonprofits Can Use Social Media

As a trainer and facilitator who works with nonprofit organizations and staffers, you have to be obsessed with learning theory to design and deliver effective instruction, have productive meetings, or embark on your own self-directed learning path. Internal: These theories take into account our minds and bodies.

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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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Timaeus in 2024

The AI Alignment Forum

Published on February 20, 2025 11:54 PM GMT TLDR: We made substantial progress in 2024: We published a series of papers that verify key predictions of Singular Learning Theory (SLT) [ 1 , 2 , 3 , 4 , 5 , 6 ]. The learning process picks out the algorithms a model learns and thus how it generalizes. in funding for 2025.

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Google at ICLR 2023

Google Research AI blog

If you’re registered for ICLR 2023, we hope you’ll visit the Google booth to learn more about the exciting work we’re doing across topics spanning representation and reinforcement learning, theory and optimization, social impact, safety and privacy, and applications from generative AI to speech and robotics.

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Strengthening program evaluation in your nonprofit

ASU Lodestar Center

What level of interest and incentives to learn and implement evaluation are present? What does the decision making and organizational structure look like? Does the environment support change, accountability, and communication? Identify champions and people to work with. Implement ECB strategies.

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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

Were interested in research that investigates this phenomenon empirically: under what conditions does it occur in practice? But there are many ways to design the game and define red or blue team victory conditions within this high-level structure. How problematic is it? Wen et al. ,