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Every technology shift is an opportunity to advance scientific discovery, accelerate human progress, and improve lives. I believe the transition we are seeing right now with AI will be the most profound in our lifetimes, far bigger than the shift to mobile or to the web before it. AI has the potential to create opportunities — from the everyday to the extraordinary — for people everywhere. It will bring new waves of innovation and economic progress and drive knowledge, learning, creativity and productivity on a scale we haven’t seen before.

That’s what excites me: the chance to make AI helpful for everyone, everywhere in the world.

Nearly eight years into our journey as an AI-first company, the pace of progress is only accelerating: Millions of people are now using generative AI across our products to do things they couldn’t even a year ago, from finding answers to more complex questions to using new tools to collaborate and create. At the same time, developers are using our models and infrastructure to build new generative AI applications, and startups and enterprises around the world are growing with our AI tools.

This is incredible momentum, and yet, we’re only beginning to scratch the surface of what’s possible.

We’re approaching this work boldly and responsibly. That means being ambitious in our research and pursuing the capabilities that will bring enormous benefits to people and society, while building in safeguards and working collaboratively with governments and experts to address risks as AI becomes more capable. And we continue to invest in the very best tools, foundation models and infrastructure and bring them to our products and to others, guided by our AI Principles.

Now, we’re taking the next step on our journey with Gemini, our most capable and general model yet, with state-of-the-art performance across many leading benchmarks. Our first version, Gemini 1.0, is optimized for different sizes: Ultra, Pro and Nano. These are the first models of the Gemini era and the first realization of the vision we had when we formed Google DeepMind earlier this year. This new era of models represents one of the biggest science and engineering efforts we’ve undertaken as a company. I’m genuinely excited for what’s ahead, and for the opportunities Gemini will unlock for people everywhere.

The People + AI Guidebook is a set of methods, best practices and examples for designing with AI.

Our recommendations are based on data and insights from over a hundred Googlers, industry experts, and academic research.

2023-02-14 08:16 Data Set Tools & Methods

What is Dataset Search?

Dataset Search is a search engine for datasets.

Using a simple keyword search, users can discover datasets hosted in thousands of repositories across the Web.

Our Mission

In addition to making datasets universally accessible and useful, Dataset Search's mission is to:

  • Foster a data sharing ecosystem that will encourage data publishers to follow best practices for data storage and publication
  • Give scientists a way to show the impact of their work through citation of datasets that they have produced

As more dataset repositories use and similar standards to describe their datasets, the variety and coverage of datasets that users find in Dataset Search will continue to grow.

2022-11-17 16:03 Page Research & Reports
We live in a world of great natural beauty — of majestic mountains, dramatic seascapes, and serene forests. Imagine seeing this beauty as a bird does, flying past richly detailed, three-dimensional landscapes. Can computers learn to synthesize this kind of visual experience? Such a capability would allow for new kinds of content for games and virtual reality experiences: for instance, relaxing within an immersive flythrough of an infinite nature scene. But existing methods that synthesize new views from images tend to allow for only limited camera motion.

In a research effort we call Infinite Nature, we show that computers can learn to generate such rich 3D experiences simply by viewing nature videos and photographs. Our latest work on this theme, InfiniteNature-Zero (presented at ECCV 2022) can produce high-resolution, high-quality flythroughs starting from a single seed image, using a system trained only on still photographs, a breakthrough capability not seen before. We call the underlying research problem perpetual view generation: given a single input view of a scene, how can we synthesize a photorealistic set of output views corresponding to an arbitrarily long, user-controlled 3D path through that scene? Perpetual view generation is very challenging because the system must generate new content on the other side of large landmarks (e.g., mountains), and render that new content with high realism and in high resolution.

Read more
2022-05-25 06:59 Weblink Research & Reports
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation. Our key discovery is that generic large language models (e.g. T5), pretrained on text-only corpora, are surprisingly effective at encoding text for image synthesis: increasing the size of the language model in Imagen boosts both sample fidelity and image-text alignment much more than increasing the size of the image diffusion model. Imagen achieves a new state-of-the-art FID score of 7.27 on the COCO dataset, without ever training on COCO, and human raters find Imagen samples to be on par with the COCO data itself in image-text alignment. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models. With DrawBench, we compare Imagen with recent methods including VQ-GAN+CLIP, Latent Diffusion Models, and DALL-E 2, and find that human raters prefer Imagen over other models in side-by-side comparisons, both in terms of sample quality and image-text alignment.
2022-05-06 13:47 Weblink Articles & Editorial
Last year Google Research announced our vision for Pathways, a single model that could generalize across domains and tasks while being highly efficient. An important milestone toward realizing this vision was to develop the new Pathways system to orchestrate distributed computation for accelerators. In “PaLM: Scaling Language Modeling with Pathways”, we introduce the Pathways Language Model (PaLM), a 540-billion parameter, dense decoder-only Transformermodel trained with the Pathways system, which enabled us to efficiently train a single model across multiple TPU v4 Pods. We evaluated PaLM on hundreds of language understanding and generation tasks, and found that it achieves state-of-the-art few-shot performance across most tasks, by significant margins in many cases.

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