Author Archives: Carly Schwartz

Talking strategy (and eating churros) with startups in Spain

This past spring, I sat with 12 fellow Googlers on beanbag chairs in the basement of Google for Startups Campus in Madrid. The area was outfitted as part conference room, part social gathering space. We took turns going around in a circle, talking about our arrival in Spain the previous day. We’d felt unsure of ourselves and of what we had to offer—but as we talked, we found ourselves getting inspired to step out of our usual comfort zones and try something new. 

The dozen of us had been chosen to participate in the Startup Advisors Summit, a program during which Googlers spend two weeks working with startups in London, Madrid, Tel Aviv, Seoul, São Paulo and Warsaw. The program is facilitated by the Google for Startups team, which operates Campuses—dedicated spaces for startups to work, connect and access Google resources—in those cities. 

Each morning, my colleagues and I trekked from our hotel on Gran Via past the Royal Palace and down the hill to Campus, where we listened, questioned and observed the startups to learn about the challenges they were facing. From there, we drew on our own expertise to advise in areas where the startups were not yet experts—whether it was communication strategy or data and analytics. We held office hours, hosted workshops, led meetings and participated in Q&A sessions. We helped build product timelines and marketing strategies. We analyzed financial plans and constructed brand identities. (And we all built up a collective appetite for churros, which we enthusiastically gobbled up each night at the local chocolateria.) 

Carly Schwartz at Startup Advisors Summit

The author, teaching startups how to tell their companies’ stories. 

My area of expertise focused on storytelling, and I worked with a number of different startups to refine their tactics. I wrote a video script with the founders of Routive, a travel guide company that specializes in guided car tours through Southeast Asia. I designed a social media program with the CEO of Adopta un Abuelo, a network that connects young volunteers to work with elders who might otherwise feel isolated. I edited website copy with the duo behind Doinn, a platform for house cleaning services. And I taught a group of 35 local entrepreneurs about storytelling, and how it can apply to their companies.

Gabriel Domínguez, Routive’s founder, was developing the company’s new website with his team when they entered the Google for Startups residency program, which includes two weeks dedicated to the Startups Advisors Summit. During that time, they designed a complete site structure and a strategy to expand it globally. “The Startup Advisors Summit was the best part of the Google for Startups Residency program for us,” he said. “We even redefined our business mission for the future.”

Sofia Benjumea, who runs Google for Startups in Spain, described the summit as a “win-win” for Googlers and startup founders alike. The founders get access to an experienced cohort of tech professionals who can provide unique insights and consulting. The Googlers push the limits of their own knowledge base, learn new leadership skills that then serve them well when they return to their core roles, and, of course, make new friends (and eat churros). 

The Google for Startups residency runs at six locations around the world, offering tailored mentorship and workplaces to growing companies. Residents also receive access to Google products, connections and trainings. If you’re interested in learning more about the program, the fourth edition of the Google for Startups Residency in Madrid will start in February, and applications are open until November 15. For more information on programs run at Google for Startups’ campuses, check out the program’s global website.

Machine learning gives environmentalists something to tweet about

Editor’s note: TensorFlow, our open source machine learning library, is just that—open to anyone. Companies, nonprofits, researchers and developers have used TensorFlow in some pretty cool ways, and we’re sharing those stories here on Keyword. Here’s one of them.


Victor Anton captured tens of thousands of birdsong recordings, collected over a three-year period. But he had no way to figure out which birdsong belonged to what bird.

The recordings, taken at 50 locations around a bird sanctuary in New Zealand known as “Zelandia,” were part of an effort to better understand the movement and numbers of threatened species including the Hihi, Tīeke and Kākāriki. Because researchers didn’t have reliable information about where the birds were and how they moved about, it was difficult to make good decisions about where to target conservation efforts on the ground.

Endangered species include the Kākāriki, Hihi, and Tīekei
Endangered species include the Kākāriki, Hihi, and Tīekei.

That’s where the recordings come in. Yet the amount of audio data was overwhelming. So Victor—a Ph.D. student at Victoria University of Wellington, New Zealand—and his team turned to technology.

“We knew we had lots of incredibly valuable data tied up in the recordings, but we simply didn’t have the manpower or a viable solution that would help us unlock this,” Victor tells us. “So we turned to machine learning to help us.”

Some of the audio recorders set up at 50 sites around the sanctuary
Some of the audio recorders set up at 50 sites around the sanctuary.

In one of the quirkier applications of machine learning, they trained a Google TensorFlow-based system to recognize specific bird calls and measure bird activity. The more audio it deciphered, the more it learned, and the more accurate it became.


It worked like this: the AI system used audio that had been recorded and stored, chopping it into minute-long segments, and then converting the file into a spectrogram. After the spectrograms were chopped into chunks, each spanning less than a second, they were processed individually by a deep convolutional neural network. A recurrent neural network then tied together the chunks and produced a continual prediction of which of the three birds was present across the minute-long segment. These segments were compiled to create a fuller picture about the presence and movement of the birds.

TensorFlow processed the Spectograms and learned to identify the calls of different species

TensorFlow processed the Spectograms and learned to identify the calls of different species.

The team faced some unique challenges. They were starting with a small quantity of labelled data, the software would often pick up other noises like construction, cars and even doorbells, and some of the bird species had a variety of birdsongs or two would sing at the same time.

To overcome these hurdles, they tested, verified and retrained the system many times over. As a result, they have learned things that would have otherwise remained locked up in thousands of hours of data. While it’s still early days, already conservation groups are talking to Victor about how they can use these initial results to better target their efforts. Moreover, the team has seen enough encouraging signs that they believe that their tools can be applied to other conservation projects.

“We are only just beginning to understand the different ways we can put machine learning to work in helping us protect different fauna,” says Victor, “ultimately allowing us to solve other environmental challenges across the world.”

After a “close call,” a coding champion

Eighteen-year-old Cameroon resident Nji Collins had just put the finishing touches on his final submission for the Google Code-In competition when his entire town lost internet access. It stayed dark for two months.

“That was a really, really close call,” Nji, who prefers to be called Collins, tells the Keyword, adding that he traveled to a neighboring town every day to check his email and the status of the contest. “It was stressful.”

Google’s annual Code-In contest, an effort to introduce teenagers to the world of open source, invites high school students from around the world to compete. It’s part of our mission to encourage and inspire the next generation of computer scientists, and in turn, the contest allows these young people to play a role in building real technologies.

Over the course of the competition, participants complete open-source coding and design “tasks” administered by an array of tech companies like Wikimedia and OpenMRS. Tasks range from editing webpages to updating databases to making videos; one of Collins’ favorites, for example, was making the OpenMRS home page sensitive to keystrokes. This year, more than 1,300 entrants from 62 countries completed nearly 6,400 assignments.

While Google sponsors and runs the contest, the participating tech organizations, who work most closely with the students, choose the winners. Those who finish the most tasks are named finalists, and the companies each select two winners from that group. Those winners are then flown to San Francisco, CA for an action-packed week involving talks at the Googleplex in Mountain View, office tours, segway journeys through the city, and a sunset cruise on the SF Bay.

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The 2017 Code-In winners

“It’s really fun to watch these kids come together and thrive,” says Stephanie Taylor, Code-In’s program manager. “Bringing together students from, say, Thailand and Poland because they have something in common: a shared love of computer science. Lifelong friendships are formed on these trips.”

Indeed, many Code-In winners say the community is their main motivator for joining the competition. “The people are what brought me here and keep me here,” says Sushain Cherivirala, a Carnegie Mellon computer science major and former Code-In winner who now serves as a program mentor. Mentors work with Code-In participants throughout the course of the competition to help them complete tasks and interface with the tech companies.

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Code-In winners on the Google campus

Code-In also acts as an accessible introduction to computer science and the open source world. Mira Yang, a 17-year-old from New Jersey, learned how to code for the first time this year. She says she never would have even considered studying computer science further before she dabbled in a few Code-In tasks. Now, she plans to major in it.

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Code-in winners Nji Collins and Mira Yang

“Code-In changed my view on computer sciences,” she says. “I was able to learn that I can do this. There’s definitely a stigma for girls in CS. But I found out that people will support you, and there’s a huge network out there.”

That network extended to Cameroon, where Collins’ patience and persistence paid off as he waited out his town’s internet blackout. One afternoon, while checking his email a few towns away, he discovered he’d been named a Code-In winner. He had been a finalist the year prior, when he was the only student from his school to compete. This year, he’d convinced a handful of classmates to join in.

“It wasn’t fun doing it alone; I like competition,” Collins, who learned how to code by doing his older sister’s computer science homework assignments alongside her, says. “It pushes me to work harder.”

Learn more about the annual Code-In competition.