I was first introduced to neural networks — computer systems that roughly imitate how biological brains accomplish tasks — as an undergrad in 1990. I did my senior thesis on using parallel computation to train neural networks. In those early days, I thought if we could 32X more compute power (using 32 processors at the time!), we could get neural networks to do impressive things. I was way off. It turns out we would need about 1 million times as much computational power before neural networks could scale to real-world problems.
A decade later, as an early employee at Google, I became reacquainted with machine learning when the company was still just a startup. In 2001 we used a simpler version of machine learning, statistical ML, to detect spam and suggest better spellings for people’s web searches. But it would be another decade before we had enough computing power to revive a more computationally-intensive machine learning approach called deep learning. Deep learning uses neural networks with multiple layers (thus the “deep”), so it can learn not just simple statistical patterns, but can learn subtler patterns of patterns — such as what’s in an image or what word was spoken in some audio. One of our first publications in 2012 was on a system that could find patterns among millions of frames from YouTube videos. That meant, of course, that it learned to recognize cats.
To get to the helpful features you use every day — searchable photo albums, suggestions on email replies, language translation, flood alerts, and so on — we needed to make years of breakthroughs on top of breakthroughs, tapping into the best of Google Research in collaboration with the broader research community. Let me give you just a couple examples of how we’ve done this.
A big moment for image recognition
In 2012, a paper wowed the research world for making a huge jump in accuracy on image recognition using deep neural networks, leading to a series of rapid advances by researchers outside and within Google. Further advances led to applications like Google Photos in 2015, letting you search photos by what’s in them. We then developed other deep learning models to help you find addresses in Google Maps, make sense of videos on YouTube, and explore the world around you using Google Lens. Beyond our products, we applied these approaches to health-related problems, such as detecting diabetic retinopathy in 2016, and then cancerous cells in 2017, and breast cancer in 2020. Better understanding of aerial imagery through deep learning let us launch flood forecasting in 2018, now expanded to cover more than 360 million people in 2021. It’s been encouraging to see how helpful these advances in image recognition have been.
Similarly, we’ve used deep learning to accelerate language understanding. With sequence-to-sequence learning in 2014, we began looking at how to understand strings of text using deep learning. This led to neural machine translation in Google Translate in 2016, which was a massive leap in quality, particularly for less prevalent languages. We developed neural language models further for Smart Reply in Gmail in 2017, which made it easier and faster for you to knock through your email, especially on mobile. That same year, Google invented Transformers, leading to BERT in 2018, then T5, and in 2021 MUM, which lets you ask Google much more nuanced questions. And with “sparse” models like GShard, we can dramatically improve on tasks like translation while using less energy.
We’ve driven a similar arc in understanding speech. In 2012, Google used deep neural networks to make major improvements to speech recognition on Android. We kept advancing the state of the art with higher-quality, faster, more efficient speech recognition systems. By 2019, we were able to put the entire neural network on-device so you could get accurate speech recognition even without a connection. And in 2021, we launched Live Translate on the Pixel 6 phone, letting you speak and be translated in 48 languages — all on-device, while you’re traveling with no Internet.
More invention ahead
As our research goes forward, we’re balancing more immediately applied research with more exploratory fundamental research. So we’re looking at how, for example, AI can aid scientific discovery, with a project like mapping the brain of a fly, which could one day help better understand and treat mental illness in people. We’re also pursuing quantum computing, which will likely take a decade or longer to reach wide-scale applications. This is why we publish nearly 1000 papers a year, including around 200 related to responsible AI, and we’ve given over 6500 grants to external researchers over the past decade and a half.
Looking ahead from 2021 to 2031, I’m excited about the next-generation AI systems we can build, and how much more helpful they’ll be. We’re planting the seeds today with new architectures like Pathways, with more to come.
(Courtesy Google/by Jeff Dean)
