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Designing and delivering a training to a nonprofit audience is not about extreme content delivery or putting together a PowerPoint and answering questions. If you want to get results, you need to think about instructional design and learningtheory. And, there is no shortage of learningtheories and research.
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. Note that reinforcement learning refers to both a problem setting, and to a set of algorithms.
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.
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.
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 spent the majority of the time talking about what went wrong, and she introduced an organizational learningtheory called "double-loop learning" that resonated with me. Basically, the idea is that most organizations learn in a single loop that connects programs to results.
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. For us, we believe in using efficiency metrics in machine learning software.
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 (..)
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. Do you have thoughts on, are there sub-problems such that if we solved them we could do it? Or does it just seem kind of hopeless? Im like, its nice.
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