"DataPerf: Benchmarks for Data-Centric AI Development" What if instead of holding the data constant and benchmarking different models, we held the model constant and benchmarked different data pipelines? [1/7]

#Exascale and beyond 'myths' to be debunked: Satoshi Matsuoka at #ModSim2022 💯agree on the need to debunk the Intel HPC propagated myth that the next goal beyond Exscale is Zettascale in 2027 #HPC #AI via Simon McIntosh-Smith

!!!! Ok I recorded a (new!) 2h25m lecture on "The spelled-out intro to neural networks and backpropagation: building micrograd" youtube.com/watch?v=VMj-3S… . This is the culmination of about 8 years of obsessing about the best way to explain neural nets and backprop.

We are excited to release MedPerf platform, an open-source federated evaluation framework for AI model #benchmarking that promises to deliver new levels of clinical efficiency, prioritize patient privacy & mitigating legal and regulatory risks. medperf.org

Bristol-based semiconductor company Graphcore has lost its deal with Microsoft, causing key investors to write down their stake uktech.news/deep-tech/grap… #AI via Patrick Moorhead

Great session SC25 Nov 16 MLPerf: A Benchmark for M/L. Tom St John, Murali Emani, Geoffrey Fox, Oana Balmau, David Kanter, Piotr Luszczek discuss developments within #MLPerf org & update our recent MLPerf HPC benchmark results. sc22.supercomputing.org/presentation/?…

For folks who did NOT register for #SC22, you can still watch Jack Dongarra's #TuringLecture on Youtube youtube.com/watch?v=ifVjPD… #HPC #AI

#SC22: Reinventing High-Performance Computing Torsten Hoefler 🇨🇭 has an interesting take The network is the #supercomputer! The commoditization of #HPC is a strong sign of our impact We need to reinvent ourselves

ChatGPT is sometimes amazing, and sometimes hilariously wrong. Here, it confidently explains to me why an abacus is faster than a GPU. 😃

Our paper "Benchmarking Graph Neural Networks" has been accepted for publication at Journal of Machine Learning Research Journal of Machine Learning Research! arxiv.org/pdf/2003.00982… (after rejection from NeurIPS, ICLR and ICML :)

Delighted to share our new Google Health Google AI @Deepmind paper at the intersection of LLMs + health. Our LLMs building on Flan-PaLM reach SOTA on multiple medical question answering datasets including 67.6% on MedQA USMLE (+17% over prior work). arxiv.org/abs/2212.13138

Let's talk about a detail that occurs during PyTorch 2.0's codegen - tiling. In many cases, tiling is needed to generate efficient kernels. Even for something as basic as torch.add(A, B), you might need tiling to be efficient! But what is tiling? And when is it needed? (1/13)

Call for applications for the inaugural Machine Learning and Systems Rising Stars 2023 workshop! Website: sites.google.com/g.harvard.edu/…

If you're planning to attend Hot Chips, come check out our ML inference tutorial on Sunday. We've got a great line-up of speakers from NVIDIA AI, Berkeley AI Research, Qualcomm, @MetaAI, and Moffett AI. #HotChips35 hotchips.org

Excited to announce a HUGE secret with Lisa Su: Lamini has been building LLMs on AMD GPUs *in production* for over the past year! We’ve made running LLMs on AMD super easy and a highly competitive option through our LLM Superstation, available now at ~10x lower cost than

Today on the blog, learn how we’re supporting a new effort by the non-profit MLCommons Association that aims to bring together expert researchers across academia and industry to develop standard AI safety benchmarks that everyone can use and understand. ↓ goo.gle/3QtydZf

SC23 attendees join MLCommons BOF sessions to add your voice to the MLCommons community. Wed, 11/15, 5:15pm in Rm 601-603 MLPerf: A Benchmark for Machine Learning, or in Rm 702 join the conversation around the Future of Benchmarks in Supercomputing. sc23.supercomputing.org/program/ #SC23

Introducing the AlgoPerf: Training Algorithms Benchmark! Compete for a share of the $50,000 prize pool by submitting more effective and efficient neural network training algorithms. Learn more mlcommons.org/2023/11/mlc-al… #Algorithms #MachineLearning #Competition

The Gemini era is here. Thrilled to launch Gemini 1.0, our most capable & general AI model. Built to be natively multimodal, it can understand many types of info. Efficient & flexible, it comes in 3 sizes each best-in-class & optimized for different uses blog.google/technology/ai/…