TheSequence - ⚛️ DeepMind’s Push Into Real World ML
Was this email forwarded to you? Sign up here 📝 EditorialDeep learning continues advancing at an incredible pace, regularly reaching new milestones in research and development. Despite that progress, many recent deep learning breakthroughs still feel like advanced lab experiments that are not ready for real-world environments. DeepMind has been one of the labs at the forefront of deep learning breakthroughs in such areas as computer gaming and robotics, which sometimes might appear disconnected from the realities of real-world problems. However, without making too much noise about it, DeepMind has been steadily applying deep learning to some of the most sophisticated problems across several industries. If you followed DeepMind’s research in the last two years, you would find an incredible balance between academic and practical deep learning. Let’s go through some examples of the latter. In recent months, DeepMind unveiled models like Nowacasting, which can perform short-term precipitation forecasts; and the famous AlphaFold, which can predict the structure of proteins based on a sequence of amino acids. Just this week, DeepMind published papers that tackle problems like video compression at YouTube and, get this, nuclear fusion plasma control. In each and every one of these cases, DeepMind has brilliantly adapted techniques pioneered in controlled game environments to extremely complex real-world problems. It is refreshing to see companies being open to applying DeepMind’s somewhat futuristic research to their current environments. 🔺🔻 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#167: we discuss generative adversarial networks; we walk through the original GAN paper by Ian Goodfellow; we explore TF-GANs. Edge#168: we deep dive into Lean, an OpenAI math model that can solve Olympiad math problems Now, let’s review the most important developments in the AI industry this week 🔎 ML ResearchMuZero for Video Compression DeepMind published a paper detailing a collaboration with YouTube to apply its MuZero supermodel to video compression optimization →read more on DeepMind blog Deep Learning for Nuclear Fusion Control DeepMind published a paper showing the use of reinforcement learning for controlling nuclear fusion plasma →read more on DeepMind blog Fairness in Speech Recognition Meta (Facebook) AI Research (FAIR) published a paper and open-source dataset to help benchmark fairness in speech recognitions systems →read more on FAIR blog Carbon Emissions in ML Pipelines Google Research published a paper providing details about the energy consumptions of ML hardware topologies →read more on Google Research blog 🤖 Cool AI Tech ReleasesSelf-Supervised Learning in TensorFlow TensorFlow Similarity has added support for self-supervised learning →read more on TensorFlow blog H2O.ai Hydrogen Torch H2O.ai released Hydrogen Torch, a no-code platform for training and deploying deep learning models →read more on H2O.ai blog VMware partnered with Nvidia Two heavy-weights teamed up to work on an AI-powered hybrid cloud platform ->read more on Nvidia blog 💎 We recommend🛠 Real World MLUber Carbon Feed The Uber engineering team published details about their Carbon Feed platform for delivering personalized information to Uber earners →read more on Uber Eng blog 🗯 Useful TweetWe turn our educational Edges into threads and recommend helpful books and courses. ![]() ![]() 💸 Money in AI
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