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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Apply to the second iteration of the ML for Alignment Bootcamp (MLAB 2) in Berkeley [Aug 15 - Fri Sept 2], published by Buck Shlegeris on May 6, 2022 on The AI Alignment Forum. Redwood Research is running another iteration of MLAB, our bootcamp aimed at helping people who are interested in AI alignment learn about machine learning, with a focus on ML skills and concepts that are relevant to doing the kinds of alignment research that we think seem most leveraged for reducing AI x-risk. We co-organized the last iteration of the bootcamp with Lightcone in January, and there were 28 participants. The program was rated highly (see below for more), and several participants are now working full-time on alignment. We expect to start on Aug 15 but might push it back or forward by a week depending on applicant availability. Apply here by May 27. We’re expecting to have space for about 40 participants. We’ll pay for housing, travel, and food, as well as salaries for the TAs. We’re now accepting applications for participants and TAs. TAs are expected to either know this material already or have a month free before MLAB to study all the content. Last time the schedule was roughly the following: Prep work: Pytorch array programming Week 1: Pytorch, optimization Implement a renderer in pytorch, as an exercise in mathematical array programming Implement ResNet from scratch in pytorch, implementing all the layers from scratch and loading weights from a trained model. Implement interpretability techniques on the ResNet. Implement SGD and other local optimization algorithms, run remote hyperparameter searches on a simple architecture Implement a simple clone of some of Pytorch, with particular focus on the implementation of backpropagation (Optional) CUDA programming day–write various CUDA kernels, see how close to the performance of Pytorch’s kernels you can get Week 2: Transformers Implement BERT from scratch, load weights from the real pretrained BERT Implement GPT-2, implement beam search Fine tune BERT on classification, fine-tune GPT-2 on some specific corpus Look at various interpretability techniques on GPT-2 Data-parallel training Week 3 Pipeline parallelism Tensor parallelism Deep RL (DQN, policy gradient) RL algorithms on language models More transformer interpretability (Optional) ELK day Week 4: Optional final projects week, Q&As with various alignment researchers This time, we’ll probably have more systematic transformer interpretability content, because we’ve spent a lot of time since MLAB doing our own transformer interpretability research and have a bunch more opinions now. We might also have more systematic content on various relevant math. I’m also hoping that we’ll be able to cover content more efficiently as a result of experience gained from running the program the first time. Past participants report that MLAB was time-consuming; we strongly recommend against trying to juggle other commitments concurrently. About 8 hours a day, 5 or 6 (if you participate in the optional day) days a week will be spent on pair programming, in addition to daily lectures and readings. There is a lot of content packed into each day; not everyone will finish every part of the curriculum. We aim to create a learning environment that is focused but not frantic; we’d rather have you understand the material deeply than finish 100% of the day’s content. The program is aimed at people who are already strong programmers who are comfortable with about one year’s worth of university level applied math (e.g. you should know what eigenvalues and eigenvectors of a matrix are, and you should know basic vector calculus; in this course you’ll have to think about Jacobian matrices and make heavy use of tensor diagram notation, so you should be able to pick up both of those pretty fast). We expect that...