Attending #EMNLP2022 this year? Stop by the Google booth where you can chat with researchers, attend a demo or Q&A session, and learn about some of the many projects our researchers are presenting throughout the conference. https://
goo.gle/3VVsjQG
@googleai
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Google Researchers Present AI Projects at EMNLP2022
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Google Quantum AI Demonstrates Photon Interaction Using Sycamore
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Under normal conditions, photons do not interact with each other. However, new research by @GoogleQuantumAI using the Sycamore quantum computer demonstrates how microwave photons can be made to interact, forming robust bound states. Check it out at https://
goo.gle/3VZuRx1 -
Differentially Private SGD Improves Ad Model Training Efficiency
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Learn how differentially private stochastic gradient descent (DP-SGD) can be applied to train ad prediction models privately with more improved model utility than previously expected, all while reducing computation and memory overhead. Read more → https://
goo.gle/3VUTlbn -
New Protocol Evaluates Machine Learning Model Input Salience Methods
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#MachineLearning models sometimes make correct predictions by using information that is irrelevant to the task. Learn more about a new protocol for evaluating input salience methods, which can help verify that a model isn’t relying on such information → https://
goo.gle/3gZSOWl -
Interactive Language Framework Enables Real-Time Language-Conditionable Robots
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Interactive Language is an imitation learning framework for producing real-time, open vocabulary language-conditionable robots. Learn more and check out the newly released and largest available language-annotated robot dataset, called Language-Table → https://t.co/ZdQCeYEFJl pic.twitter.com/5zMyoa9B57
— Google AI (@GoogleAI) 1 décembre 2022Interactive Language is an imitation learning framework for producing real-time, open vocabulary language-conditionable robots. Learn more and check out the newly released and largest available language-annotated robot dataset, called Language-Table → http://
bit.ly/3Umujjt -
ML-Driven Systems Research Workshop at Google
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Interested in learning more about ML for Systems research at Google? Stop by the booth today at 10:30 am to hear @martin_maas discuss the latest in ML-driven systems! And if you want to learn more about the area, join the ML for Systems workshop on Saturday, December 3rd.
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Google Phenaki: Text-to-Video Generation Breakthrough Presentation
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Today at 3:30 pm @RubenEVillegas and @piekindermans will be at the Google booth to describe how the Phenaki model overcomes the challenges of text-to-video generation to synthesize realistic videos from text prompts. More at: https://t.co/B2OdMk8l2S pic.twitter.com/f0KohBFnbF
— Google AI (@GoogleAI) 30 novembre 2022Today at 3:30 pm @RubenEVillegas and @piekindermans will be at the Google booth to describe how the Phenaki model overcomes the challenges of text-to-video generation to synthesize realistic videos from text prompts. More at: https://
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Google Sycamore Simulates Quantum Wormhole Dynamics
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As part of a collaboration with researchers at Caltech, Harvard, MIT, and Fermilab, learn how we simulated a quantum theory on the Google Sycamore processor to probe the dynamics of a quantum system equivalent to a wormhole in a model of gravity → https://
goo.gle/3gLymZ0 -
Google Booth: Functional View of Generative Models Discussion
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Drop by the Google booth today at 10:30am to hear @ziwphd discuss a functional view of generative models — generating functions by learning from functions! pic.twitter.com/8cwm4rJhMv
— Google AI (@GoogleAI) 30 novembre 2022Drop by the Google booth today at 10:30am to hear @ziwphd discuss a functional view of generative models — generating functions by learning from functions!
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MLCommons Algorithms Benchmark Standardizes Neural Network Training Comparison
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Today at 3:30pm, come by the #NeurIPS2022 Google booth to hear @zacharynado talk about the @MLCommons Algorithms benchmark, an effort to standardize how researchers compare training algorithms for neural networks. https://
github.com/mlcommons/algo
rithmic-efficiency
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