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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.
” 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 LearningTheory.
As a trainer and facilitator who works with nonprofit organizations and staffers, you have to be obsessed with learningtheory to design and deliver effective instruction, have productive meetings, or embark on your own self-directed learning path.
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.
Drawing from adult learningtheories, 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.
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.
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.
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. Robust unlearning: One idea for reducing AI risks is to remove models' knowledge of potentially dangerous topics, such as cybersecurity exploits or virology.
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