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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?
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This increase in accuracy is important to make AI applications good enough for production , but there has been an explosion in the size of these models. It is safe to say that the accuracy hasn’t been linearly increasing with the size of the model. They define it as “buying” stronger results by just throwing more compute at the model.
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.
Concretely, this research agenda involves answering questions such as: What is the right method for expressing goals and instructions to AI systems? The next question is whether or not a given reward learningmethod is guaranteed to converge to a reward function that is sufficiently accurate in this sense.
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 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 ]. We scaled key SLT-derived techniques to models with billions of parameters, eliminating our main concerns around tractability.
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. Were interested in more research on this, and other stress tests of todays state-of-the-art alignment methods.
Kochenderfer Contact : philhc@stanford.edu Links: Paper Keywords : deep learning or neural networks, sparsity and feature selection, variational inference, (application) natural language and text processing Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss Authors : Jeff Z. Smith, Scott W.
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).
Platt , Fernando Pereira , Dale Schuurmans Keynote Speakers The Data-Centric Era: How ML is Becoming an Experimental Science Isabelle Guyon The Forward-Forward Algorithm for Training Deep Neural Networks Geoffrey Hinton Outstanding Paper Award Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Chitwan Saharia , William Chan (..)
One way is to see if it helps us prove things about models that we care about knowing. 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. Whats the theme? Jason Gross (00:01:02): Okay.
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