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Using the ADDIE for designing your workshop, you arrive at the “E” or evaluation. ” While a participant survey is an important piece of your evaluation, it is critical to incorporate a holistic reflection of your workshop. There are two different methods to evaluate your training. Use LearningTheory.
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.
All of my work these days is focused on designing and delivering effective training for nonprofits -primarily on the topics of social media, strategy, networks, and measurement. 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.
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? .
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.
Some relevant criteria for evaluating a specification language include: How expressive is the language? A nave answer might be to measure their L 2 -distance. However, this is not the only option, and it is not self-evident that it is the right choice. Are there things it cannot express? How intuitive is it for humans to work with?
We also managed to leverage these results to produce a new method for conservative optimisation, that tells you how much (and in what way) you can optimise a proxy reward, based on the quality of that proxy (as measured by a STARC metric ), in order to be guaranteed that the true reward doesnt decrease (and thereby prevent the Goodhart drop).
Beth has over 30 years working in the nonprofit sector in technology, training, capacity building, evaluation, fundraising and marketing. Beth is an expert in facilitating online and offline peer learning, curriculum development based on traditional adult learningtheory and other instructional approaches.
Derrick Xin , Behrooz Ghorbani , Ankush Garg , Orhan Firat , Justin Gilmer Associating Objects and Their Effects in Video Through Coordination Games Erika Lu , Forrester Cole , Weidi Xie, Tali Dekel , William Freeman , Andrew Zisserman , Michael Rubinstein Increasing Confidence in Adversarial Robustness Evaluations Roland S.
And the takeaway from this paper is that you can use proofs to measure how much compression you get. And then you prove this, and the measure of compression is how long your proof is. And a technical note: it needs to be in some first-order system or alternatively, you need to measure proof checking time as opposed to proof length.
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