3 ways AI is scaling helpful technologies worldwide

3 ways AI is scaling helpful technologies worldwide

3 ways AI is scaling helpful technologies worldwide
3 ways AI is scaling helpful technologies worldwide
Nov 02, 2022
Decades of research have led to today’s rapid progress in AI — today we’re announcing 3 new ways people are poised to benefit
Jeff Dean
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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:

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