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Strengthening program evaluation in your nonprofit

ASU Lodestar Center

This call spurred the increasing demand for program evaluation. In your organization, this may look like negative attitudes toward evaluation, poor research designs and collecting data but not using the data. The root problem here is poor evaluation capacity. The root problem here is poor evaluation capacity.

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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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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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Stanford AI Lab Papers and Talks at NeurIPS 2021

Stanford AI Lab Blog

Kochenderfer Contact : philhc@stanford.edu Links: Paper Keywords : deep learning or neural networks, sparsity and feature selection, variational inference, (application) natural language and text processing Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss Authors : Jeff Z.

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Research directions Open Phil wants to fund in technical AI safety

The AI Alignment Forum

We think this adversarial style of evaluation and iteration is necessary to ensure an AI system has a low probability of catastrophic failure. Wed like to support more such evaluations, especially on scalable oversight protocols like AI debate. and Which rules are LLM agents happy to break, and which are they more committed to? .

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Stanford AI Lab Papers and Talks at ICLR 2022

Stanford AI Lab Blog

List of Accepted Papers Autonomous Reinforcement Learning: Formalism and Benchmarking Authors : Archit Sharma*, Kelvin Xu*, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn Contact : architsh@stanford.edu Links: Paper | Website Keywords : reinforcement learning, continual learning, reset-free reinforcement learning MetaShift: (..)

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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.