Learning Theory Model Train 
Beth's Blog: How Nonprofits Can Use Social Media
FEBRUARY 18, 2014
I’m co-facilitating a session on Nonprofit Training Design and Delivery with colleagues John Kenyon, Andrea Berry, and Cindy Leonard at the NTEN Nonprofit Technology Conference on Friday March 14th at 10:30 am! There are two different methods to evaluate your training. Use Learning Theory.
Beth's Blog: How Nonprofits Can Use Social Media
FEBRUARY 11, 2014
As a trainer and facilitator who works with nonprofit organizations and staffers, you have to be obsessed with learning theory to design and deliver effective instruction, have productive meetings, or embark on your own self-directed learning path. Here’s some examples.
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Beth's Blog: How Nonprofits Can Use Social Media
JANUARY 27, 2014
Join me for a FREE Webinar: Training Tips that Work for Nonprofits on Jan.29th I’ll be sharing my best tips and secrets for designing and delivering training for nonprofit professionals that get results. 29th at 1:00 PM EST/10:00 AM PST. I use a simple structure to design: before, during, and after.
Beth's Blog: How Nonprofits Can Use Social Media
DECEMBER 23, 2011
I’ve been curating resources on training techniques and capacity building over at scoop.it A lot of work I do around social media is training — good training requires good design – not just content. The model balances content, learning design, and participants. This book will help.
Google Research AI blog
APRIL 30, 2023
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.
The AI Alignment Forum
FEBRUARY 20, 2025
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 Learning Theory (SLT) [ 1 , 2 , 3 , 4 , 5 , 6 ]. We scaled key SLT-derived techniques to models with billions of parameters, eliminating our main concerns around tractability.
The AI Alignment Forum
FEBRUARY 28, 2025
For example, suppose we have a metric d over the space of all reward functions, and that we have reason to believe that a given reward function R 2 is within a distance of to the ideal reward function R 1 (based on our learning algorithm and amount of training data, etc). If not, what errors and failure modes should we expect to see?
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