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How To Think Like An Instructional Designer for Your Nonprofit Trainings

Beth's Blog: How Nonprofits Can Use Social Media

So, expect to see regular reflections on good instructional design and delivery for any topic, but especially digital technology and social media related. 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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Six Tips for Evaluating Your Nonprofit Training Session

Beth's Blog: How Nonprofits Can Use Social Media

” While a participant survey is an important piece of your evaluation, it is critical to incorporate a holistic reflection of your workshop. This includes documenting your session, reviewing your decks and exercises, analyzing your instructional design, and figuring out how to improve it. Use Learning Theory.

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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.

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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? Which specification learning algorithms are guaranteed to converge to a good specification? This is called a behavioural model.

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Strengthening program evaluation in your nonprofit

ASU Lodestar Center

Drawing from adult learning theories, ECB utilizes a variety of strategies such as: Vehicle of instruction: face-to-face meetings, teleconferences, classroom style learning, web-based mechanisms, manuals, etc. The evaluator takes on many roles: facilitator, technical expert, and sometimes a shoulder to cry.

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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. Smith, Scott W.

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Other Papers About the Theory of Reward Learning

The AI Alignment Forum

The third and final class of tasks I look at in this paper is a new category of objectives that I refer to as modal objectives, where the agent is given an instruction expressed not just in terms of what does happen along a given trajectory, but also in terms of what could happen. In the paper, we show that the answer to this question is no.