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A Comprehensive Guide to Social LearningTheory Gyrus Systems Gyrus Systems - Best Online Learning Management Systems Social learningtheory’s fundamental tenet is that people learn by watching, copying, and behaving like others in social situations. What Is Social LearningTheory?
A Comprehensive Guide to Social LearningTheory GyrusAim LMS GyrusAim LMS - Social learningtheory’s fundamental tenet is that people learn by watching, copying, and behaving like others in social situations. What Is Social LearningTheory?
A Comprehensive Guide to Social LearningTheory GyrusAim LMS GyrusAim LMS - Social learningtheory’s fundamental tenet is that people learn by watching, copying, and behaving like others in social situations. What Is Social LearningTheory?
During that time, most of my main research has been motivated by the goal of developing a theoretical foundation for the field of reward learning. It will be divided into several smaller posts: First, in this post, I will define and motivate the theoretical reward learning research agenda, including my aims and background assumptions.
There are two different methods to evaluate your training. Use LearningTheory. I have written a lot about how it is important to understand how the brain works, how people learn by using learningtheories to guide the design of your workshops. to define the four levels of training evaluation.
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.
Published on February 28, 2025 7:26 PM GMT This is the seventh post in the theoretical reward learning sequence , which starts in this post. Goodhart's Law in Reinforcement Learning As you probably know, "Goodhart's Law" is an informal principle which says that "if a proxy is used as a target, it will cease to be a good proxy".
Published on February 20, 2025 11:54 PM GMT TLDR: We made substantial progress in 2024: We published a series of papers that verify key predictions of Singular LearningTheory (SLT) [ 1 , 2 , 3 , 4 , 5 , 6 ]. The loss landscape (and optimizer) determine the learning process. Figure from Pepin Lehalleur et al. Alignment).
The title of this post is a play on a book I read The Book of Learning and Forgetting by Frank Smith in 1998 when I was working with arts educators on integrating technology into their lesson plans. I would recommend technology resources and they would share books about learning. and tag it. Many times I forget.
Feel free to reach out to the contact authors and the workshop organizers directly to learn more about the work that’s happening at Stanford! Some of the members in our SAIL community also serve as co-organizers of several exciting workshops that will take place on Dec 13-14, so we hope you will check them out!
Adversarial machine learning This cluster of research areas uses simulated red-team/blue-team exercises to expose the vulnerabilities of an LLM (or a system that incorporates LLMs). Were interested in more research on this, and other stress tests of todays state-of-the-art alignment methods.
I learn a ton from her every day and wanted to share her thinking--and her graduate thesis--with you. The purpose of my thesis was two-fold: To research and analyze community and civic engagement practices, methods, theories and examples in other museum programs. Visitors bond and bridge through participatory experiences at MAH.
Machine learning, and especially deep learning, has become increasingly more accurate in the past few years. In the graph below, borrowed from the same article, you can see how some of the most cutting-edge algorithms in deep learning have increased in terms of model size over time. Measure efficiency, not only accuracy.
Posted by Cat Armato, Program Manager, Google This week marks the beginning of the 36th annual Conference on Neural Information Processing Systems ( NeurIPS 2022 ), the biggest machine learning conference of the year. You can learn more about our work being presented in the list below (Google affiliations highlighted in bold ).
In this episode, I speak with Jason Gross about his agenda to benchmark interpretability in this way, and his exploration of the intersection of proofs and modern machine learning. What weve learned about compact proofs in general Daniel Filan (00:48:23): Okay. I dont care that much about group operation. Im like, its nice.
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