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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.
Finally, in the last post, I will also provide some resources for anyone who wants to contribute to this (or similar) research, in the form of both open problems and some thoughts on how these problems could be approached. A nave answer might be to measure their L 2 -distance.
As nonprofits attempt to tackle some of our communities' most difficult problems; funders, government agencies and the general public are actively calling for accountability, transparency and proof that a program is producing change. The root problem here is poor evaluation capacity. Illustration by Jocelyn Ruiz.
To me, the main takeaway from this paper is that we should be careful with the assumption that the basic RL setting really captures everything that we intuitively consider to be part of the problem domain of sequential decision-making. This paper is discussed in more detail in this post.
Beth is an expert in facilitating online and offline peer learning, curriculum development based on traditional adult learningtheory and other instructional approaches. She has trained thousands of nonprofits around the world. Gen Z by the Numbers. Tips for Engaging Gen Z on Social and Mobile Channels.
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. Motivation: Two lines of recent work have looked for undesirable behaviors in LLMs, approaching the problem from two different angles: Andriushchenko et al.
She has functionally branded herself in the institution as the person who is helping other staff members learn about innovative technologies, try new experiments, and measure their attempts. Basically, the idea is that most organizations learn in a single loop that connects programs to results. She keeps a failure portfolio.
They are used for different applications, but nonetheless they suggest that the development in infrastructure (access to GPUs and TPUs for computing) and the development in deep learningtheory has led to very large models. Here, model size is measured by the amount of floating-point operations.
A Workshop for Algorithmic Efficiency in Practical Neural Network Training Workshop Organizers include: Zachary Nado , George Dahl , Naman Agarwal , Aakanksha Chowdhery Invited Speakers include: Aakanksha Chowdhery , Priya Goyal Human in the Loop Learning (HiLL) Workshop Organizers include: Fisher Yu, Vittorio Ferrari Invited Speakers include: Dorsa (..)
And the takeaway from this paper is that you can use proofs to measure how much compression you get. You could imagine saying, Oh, we figured out that the difficulties in finding It was still kind of hard and our lives would be easier if we solved sub-problems, X, Y and Z. Jason Gross (00:01:02): Okay.
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