Maggio, 2021

AI is integral to so much of the work we do at Google. Fundamental advances in computing are helping us confront some of the greatest challenges of this century, like climate change. Meanwhile, AI is also powering updates across our products, including Search, Maps and Photos — demonstrating how machine learning can improve your life in both big and small ways. 

In case you missed it, here are some of the AI-powered updates we announced at Google I/O.

LaMDA is a breakthrough in natural language understanding for dialogue.

Human conversations are surprisingly complex. They’re grounded in concepts we’ve learned throughout our lives; are composed of responses that are both sensible and specific; and unfold in an open-ended manner. LaMDA — short for “Language Model for Dialogue Applications” — is a machine learning model designed for dialogue and built on Transformer, a neural network architecture that Google invented and open-sourced. We think that this early-stage research could unlock more natural ways of interacting with technology and entirely new categories of helpful applications. Learn more about LaMDA.

And MUM, our new AI language model, will eventually help make Google Search a lot smarter.

In 2019 we launched BERT, a Transformer AI model that can better understand the intent behind your Search queries. Multitask Unified Model (MUM), our latest milestone, is 1000x more powerful than BERT. It can learn across 75 languages at once (most AI models train on one language at a time), and it can understand information across text, images, video and more. We’re still in the early days of exploring MUM, but the goal is that one day you’ll be able to type a long, information-dense, and natural sounding query like “I’ve hiked Mt. Adams and now want to hike Mt. Fuji next fall, what should I do differently to prepare?” and more quickly find relevant information you need. Learn more about MUM.

Project Starline will help you feel like you’re there, together.

Imagine looking through a sort of magic window. And through that window, you see another person, life-size, and in three dimensions. You can talk naturally, gesture and make eye contact.

Project Starline is a technology project that combines advances in hardware and software to enable friends, family and co-workers to feel together, even when they’re cities (or countries) apart. To create this experience, we’re applying research in computer vision, machine learning, spatial audio and real-time compression. And we’ve developed a light field display system that creates a sense of volume and depth without needing additional glasses or headsets. It feels like someone is sitting just across from you, like they’re right there. Learn more about Project Starline.

Within a decade, we’ll build the world’s first useful, error-corrected quantum computer. And our new Quantum AI campus is where it’ll happen. 

Confronting many of the world’s greatest challenges, from climate change to the next pandemic, will require a new kind of computing. A useful, error-corrected quantum computer will allow us to mirror the complexity of nature, enabling us to develop new materials, better batteries, more effective medicines and more. Our new Quantum AI campus — home to research offices, a fabrication facility, and our first quantum data center — will help us build that computer before the end of the decade. Learn more about our work on the Quantum AI campus.

Maps will help reduce hard-braking moments while you drive.

Soon, Google Maps will use machine learning to reduce your chances of experiencing hard-braking moments — incidents where you slam hard on your brakes, caused by things like sudden traffic jams or confusion about which highway exit to take. 

When you get directions in Maps, we calculate your route based on a lot of factors, like how many lanes a road has or how direct the route is. With this update, we’ll also factor in the likelihood of hard-braking. Maps will identify the two fastest route options for you, and then we’ll automatically recommend the one with fewer hard-braking moments (as long as your ETA is roughly the same). We believe these changes have the potential to eliminate over 100 million hard-braking events in routes driven with Google Maps each year. Learn more about our updates to Maps.

Your Memories in Google Photos will become even more personalized.

With Memories, you can already look back on important photos from years past or highlights from the last week. Using machine learning, we’ll soon be able to identify the less-obvious patterns in your photos. Starting later this summer, when we find a set of three or more photos with similarities like shape or color, we’ll highlight these little patterns for you in your Memories. For example, Photos might identify a pattern of your family hanging out on the same couch over the years — something you wouldn’t have ever thought to search for, but that tells a meaningful story about your daily life. Learn more about our updates to Google Photos.

And Cinematic moments will bring your pictures to life.

When you’re trying to get the perfect photo, you usually take the same shot two or three (or 20) times. Using neural networks, we can take two nearly identical images and fill in the gaps by creating new frames in between. This creates vivid, moving images called Cinematic moments. 

Producing this effect from scratch would take professional animators hours, but with machine learning we can automatically generate these moments and bring them to your Recent Highlights. Best of all, you don’t need a specific phone; Cinematic moments will come to everyone across Android and iOS. Learn more about Cinematic moments in Google Photos.

Originally, Maysam Moussalem dreamed of being an architect. “When I was 10, I looked up to see the Art Nouveau dome over the Galeries Lafayette in Paris, and I knew I wanted to make things like that,” she says. “Growing up between Austin, Paris, Beirut and Istanbul just fed my love of architecture.” But she found herself often talking to her father, a computer science (CS) professor, about what she wanted in a career. “I always loved art and science and I wanted to explore the intersections between fields. CS felt broader to me, and so I ended up there.”

While in grad school for CS, her advisor encouraged her to apply for a National Science Foundation Graduate Research Fellowship. “Given my lack of publications at the time, I wasn’t sure I should apply,” Maysam remembers. “But my advisor gave me some of the best advice I’ve ever received: ‘If you try, you may not get it. But if you don’t try, you definitely won’t get it.’” Maysam received the scholarship, which supported her throughout grad school. “I’ll always be grateful for that advice.” 

Today, Maysam works in AI, in Google’s Machine Learning Education division and also as the co-author and editor-in-chief of the People + AI Research (PAIR) Guidebook. She’s hosting a session at Google I/O on “Building trusted AI products” as well, which you can view when it’s live at 9 am PT Thursday, May 20, as a part of Google Design’s I/O Agenda. We recently took some time to talk to Maysam about what landed her at Google, and her path toward responsible innovation.

How would you explain your job to someone who isn’t in tech?

I create different types of training, like workshops and labs for Googlers who work in machine learning and data science. I also help create guidebooks and courses that people who don’t work at Google use.

What’s something you didn’t realize would help you in your career one day?

I didn’t think that knowing seven languages would come in handy for my work here, but it did! When I was working on the externalization of the Machine Learning Crash Course, I was so happy to be able to review modules and glossary entries for the French translation!

How do you apply Google’s AI Principles in your work? 

I’m applying the AI Principles whenever I’m helping teams learn best practices for building user-centered products with AI. It’s so gratifying when someone who’s taken one of my classes tells me they had a great experience going through the training, they enjoyed learning something new and they feel ready to apply it in their work. Just like when I was an engineer, anytime someone told me the tool I’d worked on helped them do their job better and addressed their needs, it drove home the fourth AI principle: Being accountable to people. It’s so important to put people first in our work. 

This idea was really important when I was working on Google’s People + AI Research (PAIR) Guidebook. I love PAIR’s approach of putting humans at the center of product development. It’s really helpful when people in different roles come together and pool their skills to make better products. 

How did you go from being an engineer to doing what you’re doing now? 

At Google, it feels like I don’t have to choose between learning and working. There are tech talks every week, plus workshops and codelabs constantly. I’ve loved continuing to learn while working here.

Being raised by two professors also gave me a love of teaching. I wanted to share what I’d learned with others. My current role enables me to do this and use a wider range of my skills.

My background as an engineer gives me a strong understanding of how we build software at Google’s scale. This inspires me to think more about how to bring education into the engineering workflow, rather than forcing people to learn from a disconnected experience.

How can aspiring AI thinkers and future technologists prepare for a career in responsible innovation? 

Pick up and exercise a variety of skills! I’m a technical educator, but I’m always happy to pick up new skills that aren’t traditionally specific to my job. For example, I was thinking of a new platform to deliver internal data science training, and I learned how to create a prototype using UX tools so that I could illustrate my ideas really clearly in my proposal. I write, code, teach, design and I’m always interested in learning new techniques from my colleagues in other roles.

And spend time with your audience, the people who will be using your product or the coursework you’re creating or whatever it is you’re working on. When I was an engineer, I’d always look for opportunities to sit with, observe, and talk with the people who were using my team’s products. And I learned so much from this process.