TheSequence - 💻 Meta’s AI SuperComputer
Was this email forwarded to you? Sign up here 📝 EditorialLarge neural networks are the norm in modern deep learning, and they certainly require a lot of computation power. From that perspective, access to large computing resources has become a key requirement to advance AI research. Considering the progress in areas such as self-supervised learning of transformers, it’s hard not to be curious about what those AI models could accomplish with virtually limitless computation power. This week Meta (Facebook) AI announced a giant step towards that goal by unveiling a new AI supercomputer. Called the AI Research SuperCluster (RSC), Meta’s supercomputer was designed to train large language and computer vision models in trillions of parameters. RSC’s architecture is based on 760 NVIDIA DGX A100 systems as its compute nodes, for a total of 6,080 GPUs. The storage tier is equally impressive, having 175 petabytes of Pure Storage FlashArray, 46 petabytes of cache storage. These are just some of the details of a remarkable infrastructure design. RSC is up and running now, but there is a new phase of ongoing development. The new supercomputer is likely to play a pivotal role in augmenting Meta AI Research capabilities continuing the path towards massively large AI models. 🔺🔻 TheSequence Scope is our Sunday free digest. To receive high-quality educational content about the most relevant concepts, research papers, and developments in the ML world every Tuesday and Thursday, please subscribe to TheSequence Edge 🔺🔻 🗓 Next week in TheSequence Edge: Edge#161: we start a new series about deep generative models; explore Optimus, a large generative model for language tasks; overview ART that uses generative models to protect neural networks. Edge#162: we deep dive into CoreWeave, a modern cloud infrastructure. Now, let’s review the most important developments in the AI industry this week 🔎 ML ResearchInstructGPT OpenAI published a paper detailing InstructGPT, a transformer model that surpassed GPT-3 in following natural language instructions and is claimed to be less toxic →read more on OpenAI blog LaMDA Google Research published a paper unveiling LaMDA, a model for high-quality, specialized dialogs →read more on Google Research blog Robot Learning with Language and Video Stanford University published a paper detailing a method to use crowdsourced language descriptions of videos to train robots on different tasks →read more on Stanford University blog MILAN MIT researchers published a paper detailing MILAN, an interpretability technique that can attach language descriptions to components of a neural network →read more on MIT News 🤖 Cool AI Tech ReleasesMeta AI SuperCluster Meta (Facebook) provided details about its AI Research SuperCluster, which is likely to become the fastest AI supercomputer in the world when it is fully built →read more on Meta AI blog OpenAI Embeddings API OpenAI announced a new endpoint to its API that enables text and code embedding models →read more on OpenAI blog 🛠 Real World MLPerformance tests at Netflix A very detailed blog about techniques that helps the Netflix team identify and fix regressions before they happen ->read more on Netflix tech blog DeepMind Podcast DeepMind announces the second season of their amazing podcast →read more on their blog Big Data Cost Management at Uber Uber provided some details about the architecture and techniques used to reduce costs in their big data infrastructure →read more on Uber Eng blog 💸 Money in AI
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📝 Guest post: The Original Open Source Feature Store - Hopsworks*
Friday, January 28, 2022
In TheSequence Guest Post our partners explain in detail what machine learning (ML) challenges they help deal with. This article reintroduces the core concepts of a Feature Store; the dual storage
🟢 ⚪️Edge#158: A Deep Dive Into Aporia, the ML Observability Platform
Thursday, January 27, 2022
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📝 Guest post: Data Labeling and Its Role in E-commerce Today – Recent Use Cases*
Wednesday, January 26, 2022
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🔄🔄 Edge#159: MLOps Full Recap
Tuesday, January 25, 2022
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👷♀️🧑🏻🎓👩💻👨🏻🏫 The MoE Momentum
Sunday, January 23, 2022
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