Monthly Archives: November 2022

I was first introduced to neural networks as an undergraduate in 1990. Back then, many people in the AI community were excited about the potential of neural networks, which were impressive, but couldn’t yet accomplish important, real-world tasks. I was excited, too! I did my senior thesis on using parallel computation to train neural networks, thinking we only needed 32X more compute power to get there. I was way off. At that time, we needed 1 million times as much computational power.

A short 21 years later, with exponentially more computational power, it was time to take another crack at neural networks. In 2011, I and a few others at Google started training very large neural networks using millions of randomly selected frames from videos online. The results were remarkable. Without explicit training, the system automatically learned to recognize different objects (especially cats, the Internet is full of cats). This was one transformational discovery in AI among a long string of successes that is still ongoing — at Google and elsewhere.

I share my own history of neural networks to illustrate that, while progress in AI might feel especially fast right now, it’s come from a long arc of progress. In fact, prior to 2012, computers had a really difficult time seeing, hearing, or understanding spoken or written language. Over the past 10 years we’ve made especially rapid progress in AI.

Today, we’re excited about many recent advances in AI that Google is leading — not just on the technical side, but in responsibly deploying it in ways that help people around the world. That means deploying AI in Google Cloud, in our products from Pixel phones to Google Search, and in many fields of science and other human endeavors.

We’re aware of the challenges and risks that AI poses as an emerging technology. We were the first major company to release and operationalize a set of AI Principles, and following them has actually (and some might think counterintuitively) allowed us to focus on making rapid progress on technologies that can be helpful to everyone. Getting AI right needs to be a collective effort — involving not just researchers, but domain experts, developers, community members, businesses, governments and citizens.

I’m happy to make announcements in three transformative areas of AI today: first, using AI to make technology accessible in many more languages. Second, exploring how AI might bolster creativity. And third, in AI for Social Good, including climate adaptation.

1. Supporting 1,000 languages with AI

Language is fundamental to how people communicate and make sense of the world. So it’s no surprise it’s also the most natural way people engage with technology. But more than 7,000 languages are spoken around the world, and only a few are well represented online today. That means traditional approaches to training language models on text from the web fail to capture the diversity of how we communicate globally. This has historically been an obstacle in the pursuit of our mission to make the world’s information universally accessible and useful.

That’s why today we’re announcing the 1,000 Languages Initiative, an ambitious commitment to build an AI model that will support the 1,000 most spoken languages, bringing greater inclusion to billions of people in marginalized communities all around the world. This will be a many years undertaking – some may even call it a moonshot – but we are already making meaningful strides here and see the path clearly. Technology has been changing at a rapid clip – from the way people use it to what it’s capable of. Increasingly, we see people finding and sharing information via new modalities like images, videos, and speech. And our most advanced language models are multimodal – meaning they’re capable of unlocking information across these many different formats. With these seismic shifts come new opportunities.

As part of our this initiative and our focus on multimodality, we’ve developed a Universal Speech Model — or USM — that’s trained on over 400 languages, making it the largest language coverage seen in a speech model to date. As we expand on this work, we’re partnering with communities across the world to source representative speech data. We recently announced voice typing for 9 more African languages on Gboard by working closely with researchers and organizations in Africa to create and publish data. And in South Asia, we are actively working with local governments, NGOs, and academic institutions to eventually collect representative audio samples from across all the regions’ dialects and languages.

2. Empowering creators and artists with AI

AI-powered generative models have the potential to unlock creativity, helping people across cultures express themselves using video, imagery, and design in ways that they previously could not.

Our researchers have been hard at work developing models that lead the field in terms of quality, generating images that human raters prefer over other models. We recently shared important breakthroughs, applying our diffusion model to video sequences and generating long coherent videos for a sequence of text prompts. We can combine these techniques to produce video — for the first time, today we’re sharing AI-generated super-resolution video:

We’ll soon be bringing our text-to-image generation technologies to AI Test Kitchen, which provides a way for people to learn about, experience, and give feedback on emerging AI technology. We look forward to hearing feedback from users on these demos in AI Test Kitchen Season 2. You’ll be able to build themed cities with “City Dreamer” and design friendly monster characters that can move, dance, and jump with “Wobble” — all by using text prompts.

In addition to 2D images, text-to-3D is now a reality with DreamFusion, which produces a three-dimensional model that can be viewed from any angle and can be composited into any 3D environment. Researchers are also making significant progress in the audio generation space with AudioLM, a model that learns to generate realistic speech and piano music by listening to audio only. In the same way a language model might predict the words and sentences that follow a text prompt, AudioLM can predict which sounds should follow after a few seconds of an audio prompt.

We’re collaborating with creative communities globally as we develop these tools. For example, we’re working with writers using Wordcraft, which is built on our state-of-the-art dialog system LaMDA, to experiment with AI-powered text generation. You can read the first volume of these stories at the Wordcraft Writers Workshop.

3. Addressing climate change and health challenges with AI

AI also has great potential to address the effects of climate change, including helping people adapt to new challenges. One of the worst is wildfires, which affect hundreds of thousands of people today, and are increasing in frequency and scale.

Today, I’m excited to share that we’ve advanced our use of satellite imagery to train AI models to identify and track wildfires in real time, helping predict how they will evolve and spread. We’ve launched this wildfire tracking system in the U.S., Canada, Mexico, and are rolling out in parts of Australia, and since July we’ve covered more than 30 big wildfire events in the U.S. and Canada, helping inform our users and firefighting teams with over 7 million views in Google Search and Maps.

We’re also using AI to forecast floods, another extreme weather pattern exacerbated by climate change. We’ve already helped communities to predict when floods will hit and how deep the waters will get — in 2021, we sent 115 million flood alert notifications to 23 million people over Google Search and Maps, helping save countless lives. Today, we’re sharing that we’re now expanding our coverage to more countries in South America (Brazil and Colombia), Sub-Saharan Africa (Burkina Faso, Cameroon, Chad, Democratic Republic of Congo, Ivory Coast, Ghana, Guinea, Malawi, Nigeria, Sierra Leone, Angola, South Sudan, Namibia, Liberia, and South Africa), and South Asia (Sri Lanka). We’ve used an AI technique called transfer learning to make it work in areas where there’s less data available. We’re also announcing the global launch of Google FloodHub, a new platform that displays when and where floods may occur. We’ll also be bringing this information to Google Search and Maps in the future to help more people to reach safety in flooding situations.

Finally, AI is helping provide ever more access to healthcare in under-resourced regions. For example, we’re researching ways AI can help read and analyze outputs from low-cost ultrasound devices, giving parents the information they need to identify issues earlier in a pregnancy. We also plan to continue to partner with caregivers and public health agencies to expand access to diabetic retinopathy screening through our Automated Retinal Disease Assessment tool (ARDA). Through ARDA, we’ve successfully screened more than 150,000 patients in countries like India, Thailand, Germany, the United States, and the United Kingdom across deployed use and prospective studies — more than half of those in 2022 alone. Further, we’re exploring how AI can help your phone detect respiratory and heart rates. This work is part of Google Health’s broader vision, which includes making healthcare more accessible for anyone with a smartphone.

AI in the years ahead

Our advancements in neural network architectures, machine learning algorithms and new approaches to hardware for machine learning have helped AI solve important, real-world problems for billions of people. Much more is to come. What we’re sharing today is a hopeful vision for the future — AI is letting us reimagine how technology can be helpful. We hope you’ll join us as we explore these new capabilities and use this technology to improve people’s lives around the world.

Communities around the world are facing the effects of climate change — from devastating floods and wildfires to challenges around food security. As global leaders meet in Egypt for COP27, a key area of focus will be on how we can work together to adapt to climate change and implement sustainable solutions. At Google, we’re investing in technologies that can help communities prepare for and respond to climate-related disasters and threats.

Tools to alert people and governments about immediate risks

Natural disasters are increasing in frequency and intensity due to climate change. As part of our Crisis Response efforts, we’re working to bring trusted information to people in critical moments to keep them safe and informed. To do so, we rely on the research and development of our AI-powered technologies and longstanding partnerships with frontline emergency workers and organizations. Here’s a look at some of our crisis response efforts and new ways we’re expanding these tools.

• Floods: Catastrophic damage from flooding affects more than 250 million people every year. In 2018, we launched our flood forecasting initiative that uses machine learning models to provide people with detailed alerts. In 2021, we sent 115 million flood alert notifications to 23 million people over Search and Maps, helping save countless lives. Today, we’re expanding our flood forecasts to river basins in 18 additional countries across Africa, Latin America and Southeast Asia. We’re also announcing the global launch of the new FloodHub, a platform that displays flood forecasts and shows when and where floods may occur to help people directly at risk and provide critical information to aid organizations and governments. This expansion in geographic coverage is possible thanks to our recent breakthroughs in AI-based flood forecasting models, and we’re committed to expanding to more countries.

• Wildfires: Wildfires affect hundreds of thousands of people each year, and are increasing in frequency and size. I experienced firsthand the need for accurate information when wildfires occur and this inspired our crisis response work. We detect wildfire boundaries using new AI models based on satellite imagery and show their real-time location in Search and Maps. Since July, we’ve covered more than 30 big wildfire events in the U.S. and Canada, helping inform people and firefighting teams with over 7 million views in Search and Maps. Today, wildfire detection is now available in the U.S., Canada, Mexico and Australia.

• Hurricanes: Access to authoritative forecasts and safety information about hurricanes can be life-saving. In the days before a hurricane in North America or a typhoon in Japan, detailed forecasts from authoritative sources appear on SOS Alerts in Search and Maps to show a storm’s predicted trajectory. We’re also using machine learning to analyze satellite imagery after disasters and identify which areas need help. When Hurricane Ian hit Florida in September, this technology was deployed in partnership with Google.org grantee GiveDirectly to quickly allocate aid to those most affected.

Managing current and future climate impacts

Climate change poses a threat to our world’s natural resources and food security. We’re working with governments, organizations and communities to provide information and technologies to help adapt to these changes.

• Keeping cities greener and healthier: Extreme temperatures and poor air quality are increasingly common in cities and can impact public health. To mitigate this, our Project Green Light uses AI to optimize traffic lights at intersections around the world with the aim to help minimize congestion and related pollution. Project Air View also brings detailed air quality maps to scientists, policymakers and communities. And we’re working to expand our Environmental Insights Explorer’s Tree Canopy Insights tool to hundreds of cities by the end of this year so they can use trees to lower street-level temperatures and improve quality of life.
• Meeting the world’s growing demand for food: Mineral — a project from X, Alphabet’s moonshot factory — is working to build a more sustainable and productive food system. The team is joining diverse data sets in radically new ways — from soil and weather data to drone and satellite images — and using AI to reveal insights never before possible about what’s happening with crops. As part of our Startups For Sustainable Development program, we’re also supporting startups addressing food security. These include startups like OKO, which provides crop insurance to keep farmers in business in case of adverse weather events and has reached tens of thousands of farmers in Mali and Uganda.
• Helping farmers protect their crops: Pest infestations can threaten entire crops and impact the livelihoods of millions. In collaboration with InstaDeep and the Food and Agriculture Organization of the United Nations, our team at the Google AI Center in Ghana is using AI to better detect locust outbreaks so that it’s possible to implement control measures. In India, Google.org Fellows worked with Wadhwani AI to create an AI-powered app that helps identify and treat infestations of pests, resulting in a 20% reduction in pesticide sprays and a 26% increase in profit margins for farmers. Google Cloud is also working with agricultural technology companies to use machine learning and cloud services to improve crop yields.
• Analyzing a changing planet: Using Google Cloud and Google Earth Engine, organizations and businesses can better assess and manage climate risks. For example, the U.S. Forest Service uses these tools to analyze land-cover changes to better respond to new wildfire threats and monitor the impacts of invasive insects, diseases and droughts. Similarly, the Bank of Montreal is integrating climate data — like precipitation trends — into its business strategy and risk management for clients.

AI already plays a critical role in addressing many urgent, climate-related challenges. It is important that we continue to invest in research and raise awareness about why we are doing this work. Google Arts and Culture has collaborated with artists on the Culture meets Climate collection so everyone can explore more perspectives on climate change. And at COP27 we hope to generate more awareness and engage in productive discussions about how to use AI, innovations, and shared data to help global communities address the changing climate.