Google I/O Archives - Liwaiwai

Google Cloud Advances Generative AI At I/O: New Foundation Models, Embeddings, And Tuning Tools In Vertex AI

Generative AI has unleashed a new breed of digital assistants, content creation tools, and applications, changing how apps are built, who can build them, and the capabilities end users expect from them. Google is a leader in this field, from the creation of Google’s Transformer architecture that makes generative AI possible, to today’s announcement of PaLM 2, our next-generation language model with improved multilingual, reasoning, and coding capabilities. At Google Cloud, we’re committed to bringing the power of these transformational foundation models to our customers and empowering developers to innovate in entirely new ways. We took a big step in this journey in March with…

Play I/O FLIP, An AI-Designed Card Game

At Google I/O 2023, we shared how advancements in AI can make information more accessible and improve our everyday lives — and help us have a little fun, too. Before we hit the stage, we released I/O FLIP, an AI-designed card game that shows you the playful, experimental side of generative AI in action. How to play I/O FLIP I/O FLIP is a spin on a classic card game, where you compete in best-of-three matches for the longest winning streak on the leaderboard. But in this case, you’re playing with custom cards made using generative AI. Here’s how it works: You choose…

Pixel 7a: Built To Perform, Packed With Value

Pixel 7a is available for purchase now, bringing advanced hardware and software capabilities at an accessible price. Meet Google Pixel 7a, our latest A-Series phone that delivers all the helpfulness of Google for less. It’s built with Google Tensor G2, our flagship processor, and Titan M2, our dedicated security chip, making it faster, more efficient and more secure.1 Pixel 7a is packed with many of the must-have features of our premium phones that are now available on an A-series phone for the first time — like Face Unlock, 8GB of RAM, an up to 90Hz Smooth Display and wireless charging. Pixel…

Technology

Meet The New Google Pixel Tablet That’s Helpful In Your Hands And At Home

Built with the latest Android features, our Google Tensor G2 chip, and our Pixel design. Say hello to the Google Pixel Tablet, designed to be helpful 24/7 in your hand and in your home. It’s built with the latest Android features, our Google Tensor G2 chip and our iconic Pixel design. Here’s a look at what’s in the new tablet and all the reasons why you won’t leave this device forgotten in a drawer. A premium tablet built for all your entertainment needs With a brilliant 11-inch display and four built-in speakers for crisp audio, the tablet is the perfect entertainment…

Gears

Introducing Pixel Fold: Google’s First Foldable Phone

Whether open or closed, there are endless ways of having fun and getting things done. Google Pixel Fold is now available for pre-order and will begin shipping next month. Our new foldable phone combines the helpfulness of Pixel with a design that unfolds into a compact tablet, unlocking even more ways for you to use your device. And it’s all powered by our Google Tensor G2 chip, which also makes Pixel Fold fast and secure. Here’s a look at the newest addition to our Pixel portfolio. A thin, durable design When closed, Pixel Fold has a familiar smartphone silhouette that fits in…

Google I/O 2019 | Cutting Edge TensorFlow: New Techniques

Google I/O 2019 | Cutting Edge TensorFlow: New Techniques There’s lots of great new things available in TensorFlow since last year’s IO. This session will take you through 4 of the hottest from Hyperparameter Tuning with Keras Tuner to Probabilistic Programming to being able to rank your data with learned ranking techniques and TF-Ranking. Finally, you will look at TF-Graphics that brings 3D functionalities to TensorFlow. Speakers: Elie Burzstein , Josh Dillon, Michael Bendersky, Sofien Bouaziz Session ID: TDA482

Google I/O 2019 | Cloud TPU Pods: AI Supercomputing for Large Machine Learning Problems

Google I/O 2019 | Cloud TPU Pods: AI Supercomputing for Large Machine Learning Problems Cloud Tensor Processing Unit (TPU) is an ASIC designed by Google for neural network processing. TPUs feature a domain specific architecture designed specifically for accelerating TensorFlow training and prediction workloads and provides performance benefits on machine learning production use. Learn the technical details of Cloud TPU and Cloud TPU Pod and new features of TensorFlow that enables a large scale model parallelism for deep learning training. Speakers: Kaz Sato and Martin Gorner Session ID: TF6510

Google I/O 2019 | TF-Agents: A Flexible Reinforcement Learning Library for TensorFlow

Google I/O 2019 | TF-Agents: A Flexible Reinforcement Learning Library for TensorFlow TF-Agents is a clean, modular, and well-tested open-source library for Deep Reinforcement Learning with TensorFlow. This session will cover recent advancements in Deep RL, and show how TF-Agents can help to jump start your project. You will also see how TF-Agent library components can be mixed, matched, and extended to implement new RL algorithms. Speakers: Sergio Guadarrama and Eugene Brevdo Session ID: TFA7A8

Google I/O 2019 | Machine Learning Fairness: Lessons Learned

Google I/O 2019 | Machine Learning Fairness: Lessons Learned ML fairness is a critical consideration in machine learning development. This session will present a few lessons Google has learned through our products and research and how developers can apply these learnings in their own efforts. Techniques and resources will be presented that enable evaluation and improvements to models, including open source datasets and tools such as TensorFlow Model Analysis. This session will enable developers to proactively think about fairness in product development. Speakers: Tulsee Doshi and Jacqueline Pan Session ID: T8ACB1

Google I/O 2019 | Federated Learning: Machine Learning on Decentralized Data

Google I/O 2019 | Federated Learning: Machine Learning on Decentralized Data Meet federated learning: a technology for training and evaluating machine learning models across a fleet of devices (e.g. Android phones), orchestrated by a central server, without sensitive training data leaving any user’s device. Learn how this privacy-preserving technology is deployed in production in Google products and how TensorFlow Federated can enable researchers and pioneers to simulate federated learning on their own datasets. Speakers: Daniel Ramage and Emily Glanz Session ID: TDC839

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