In Asia Pacific, cloud adoption is already in overdrive and will continue to gain traction. The region is set to outpace the rest of world with a 31.6 percent CAGR in cloud data center workloads from 2015 to 2020
By Henry Silverman, Operations Integrity Specialist and Lin Huang, Engineer People have told us that they dislike spammy posts on Facebook that goad them into interacting with likes, shares, comments, and other actions. For example, “LIKE this if you’re an Aries!” This tactic, known as “engagement bait,” seeks to take advantage of our News Feed […]
As networks grow, so does the complexity that comes with adding new applications and on-boarding new devices—including IoT—all while delivering a more secure network. Add to that challenge the drive to transform the network to deliver new innovative services and applications while providing the best experience for your customers. Read on, because Cisco has the […]
In the news and technology communities, the collective sense of urgency about the future of journalism reached new heights this year. Never before has the press been so important—or so under threat. Technology and platforms like the ones Google has built present extraordinary opportunities to strengthen journalism, but they require newsrooms and tech companies working closely together to get it right. That’s why the Google News Lab exists.
In a Keyword series this week, we’ve shared the work the News Lab is doing around the world to address industry challenges and take advantage of new technologies. Today, in our final post in this series, we’re stepping back to give a holistic view of 10 major developments in our work this last year. We’re looking forward to an even bigger 2018 and would love your feedback on how we can partner with the industry to build a stronger future for news.
The spread of misinformation is a growing problem for open societies everywhere. So, helping news organizations confront that challenge—especially during elections—was a key focus for us. We helped the First Draft Coalition pioneer new collaborative reporting models to combat misinformation and verify news stories during the UK, French, and German elections.
We worked closely with the news industry to better highlight accurate, quality content on our platforms with new product features and partnerships. Along with the Trust Project, we produced eight indicators of trust that newsrooms can add to their content to help users distinguish between quality content and misinformation. We also partnered with the International Fact-Checking Network and The Poynter Institute to increase the number of verified fact checkers across the world.
Bringing underrepresented voices into newsrooms can help uncover important stories that are left out of mainstream news coverage. We supported ASNE’s survey to get a better sense of diversity in newsrooms across the U.S. We also partnered with organizations in the U.S., Brazil, France and Germany to provide journalists from diverse backgrounds with in-depth programs to develop their careers.
With revenue pressures challenging the creation of quality local news content, we began investing in projects to strengthen local newsrooms across the U.S. We partnered with the Society for Professional Journalists to train more than 9,000 local reporters in digital skills. We’re also supporting Report for America, an initiative that will use a Teach for America model to place a thousand journalists in local newsrooms over the next five years.
To better understand key challenges facing the news industry, we produced studies on the state of data journalism in 2017 and how audiences experience VR and what it means for journalists. We also supported the ICFJ’s newsroom study on the usage of technology in newsrooms.
From drones to virtual reality, we helped news organizations understand and use emerging technologies to shape their reporting and engage audiences in new ways. And we experimented with machine learning, too—we partnered with ProPublica to launch Documenting Hate, a project which uses AI to help build a national database for hate crime and bias incidents.
Our research into the state of data journalism found that while half of newsrooms have a dedicated data journalist, many lack the tools and resources to be successful. So we built a number of tools—Flourish, Tilegrams, Data Gif Maker, Election Databot— to make data journalism more accessible to newsrooms and journalists across the world.
With our online training center, advanced online learning partnerships, and in-person trainings, we helped train more than 500,000 journalists across the world in digital tools and skills for storytelling and reporting. To develop the next generation of digital journalists, we offered more than 50 News Lab Fellowships with major news organizations across 12 countries.
Google Trends data offers news organizations a look at the candidates and issues that voters are interested in during election season. In Germany, we created a Google Trends Hub to show users’ search interest in key candidates and built a visualization tool to bring the data to life. In France, we launched a data driven web app that showed search interest in the candidates over time.
This year we launched the News Lab in two new markets: Brazil and in Asia. To kick things off we held inaugural News Lab Summits in both regions—convening journalists from 15 states in Brazil and journalists from 15 countries in Singapore. Since then, we’ve trained more than 8,000 journalists in Brazil and 12,000 journalists in Asia.
It’s an exciting time for journalism. There are many challenges, but we are eager to work with the news industry to build a more informed world. Tell us where you think we should put our efforts—we’d love to hear feedback and new ideas.
On November 20, two special guests visited Samsung’s Cinema LED screen at the Super-S Theater in Lotte World Tower, Seoul. On a short visit to Korea, Franz
Maru Ahues Bouza, an Engineering Manager at Google, wouldn’t be where she is today without her father’s encouragement to learn computer science (CS). Growing up in Venezuela, there were no CS classes for children, so when Maru was just 10 years old, her father enrolled her and her sister in an adult CS class. At first, the girls showed little interest, but with steady support from their father, Maru and her sister became the top performers in the class. Maru continued with CS, graduating from Universidad Simón Bolívar with a Computer Engineering degree. Maru says that she couldn’t have learned CS without her father’s confidence: “if you’re taught from a young age that you can definitely do it, you’re going to grow up knowing you can be successful.”
Our latest research confirms that this type of support and encouragement is indeed critical. In partnership with Gallup, today we are releasing a new research brief, Encouraging Students Toward Computer Science Learning, and a set of CS education reports for 43 U.S. states. Here are the top six things you should know about the research:
Simple words of support can help more kids like Maru learn CS, no matter who they are or where they live. It’s not hard to encourage students, but we often don’t do so unless a student shows explicit interest. So this winter break, read the research about CS education and take a few minutes to encourage a student to create something using computer science, like coding their own Google logo. This encouragement could spark a student’s lifelong interest in computer science, just like it did for Maru.
Augmented reality is a powerful way to bring the physical and digital worlds together. AR places digital objects and useful information into the real world around us, which creates a huge opportunity to make our phones more intuitive, more helpful and a whole lot more fun.
We’ve been working on augmented reality since 2014, with our earliest investments in Project Tango. We’ve taken everything we learned from that to build ARCore, which launched in preview earlier this year. Whereas Tango required special hardware, ARCore is a fast, performant, Android-scale SDK that enables high-quality augmented reality across millions of qualified mobile devices.
Developers can experiment with ARCore now, and we’ve seen some amazing creations from the community. ARCore also powers AR Stickers on the Pixel camera, which launched earlier this week and lets you add interactive AR characters and playful emojis directly into photos and videos to bring your favorite stories to life.
Today, we’re releasing an update to our ARCore Developer Preview with several technical improvements to the SDK, including:
A new C API for use with the Android NDK that complements our existing Java, Unity, and Unreal SDKs;
Functionality that lets AR apps pause and resume AR sessions, for example to let a user return to an AR app after taking a phone call;
Improved accuracy and runtime efficiency across our anchor, plane finding, and point cloud APIs.
As we focus on bringing augmented reality to the entire Android ecosystem with ARCore, we’re turning down support of Tango. Thank you to our incredible community of developers who made such progress with Tango over the last three years. We look forward to continuing the journey with you on ARCore.
If you’re a developer interested in AR, now’s the time to start experimenting. In the coming months, we’ll launch ARCore v1.0, with support for over 100 million devices. And soon, many augmented reality experiences will be available in the Play Store. We can’t wait to see what you create.
Featuring a thoughtfully refined design that allows users to do so much more using the built-in S Pen, Samsung’s new Notebook 9 Pen was built for those who are
With people spending more time on social media, many rightly wonder whether that time is good for us. Do people connect in meaningful ways online? Or are they simply consuming trivial updates and polarizing memes at the expense of time with loved ones?
Just weeks after getting access to CrowdTangle, La Gaceta Salta — a three-year old Argentine local news outlet with accelerating growth and a burgeoning online audience — was seeing value in their newsroom. The La Gaceta Salta team had a problem on their hands:…
Editor’s note: Senior Product Manager Berit Hoffmann leads Hire, a recruiting application Google launched earlier this year. In this post, she shares five ways businesses can improve their hiring process and secure great talent.
With 2018 quickly approaching, businesses are evaluating their hiring needs for the new year.
According to a recent survey of 2,200 hiring managers, 46 percent of U.S. companies need to hire more people but have issues filling open positions with the right candidates. If your company lacks great hiring processes and tools, it can be easy to make sub-optimal hiring decisions, which can have negative repercussions.
We built Hire to help businesses hire the right talent more efficiently, and integrated it with G Suite to help teams collaborate more effectively throughout the process. As your business looks to invest in talent next year, here are five ways to positively impact your hiring outcomes.
Take time to define each stage of the hiring process, and think about if and how the process may need to differ. This will help you better tailor your evaluation of each candidate to company expectations, as well as the qualifications of a particular role.
Earlier this year, Google reviewed a subset of its own interview data to discover the optimal number of interviews needed in the hiring process to evaluate whether a candidate is right for Google. Statistical analysis showed that four interviews was enough to predict with 86 percent confidence whether someone should be hired. Of course, every company’s hiring process varies according to size, role or industry—some businesses require double that number of interviews, whereas others may only need one interview.
Using Hire to manage your recruiting activities allows you to configure as many hiring process “templates” as you’d like, as well as use different ones for different roles. For example, you might vary the number of interview rounds based on department. Whatever process you define, you can bring all candidate activity and interactions together within Hire. Plus, Hire integrates with G Suite apps, like Gmail and Calendar, to help you coordinate the process.
For many businesses, sourcing candidates is one of the most time-consuming parts of the hiring process, so Google launched Job Search to help employers better showcase job opportunities in search. Since launching, 60 percent more employers show jobs in search in the United States.
Making your open positions discoverable where people are searching is an important part of attracting the best talent. If you use Hire to post a job, the app automatically formats your public job posting so it is discoverable by job seekers in Google search.
The sooner an interviewer provides feedback, the faster your hiring team can reach a decision, which improves the candidate’s experience. To help speed up feedback submissions, some companies like Genius.com use a “silent process” approach. This means interviewers are not allowed to discuss a candidate until they submit written feedback first.
Hire supports this “silent process” approach by hiding other people’s feedback from interviewers until they submit their own. We’ve found that this can incentivize employees to submit feedback faster because they want to see what their colleagues said. 63 percent of Hire interviewers leave feedback within 24 hours of an interview and 75 percent do so within 48 hours.
Beyond speedy feedback delivery, it’s perhaps more important to receive quality evaluations. Make sure your interviewers know how to write clear feedback and try to avoid common mistakes such as:
One way you can encourage employees to stay focused when they interview a candidate. is to assign them a specific topic to cover in the interview. In Hire, topics are included in each interviewer’s Google Calendar invitation for easy reference without having to log into the app.
Maintaining a high standard for written feedback helps your team not only make hiring decisions today, but also helps you track candidates for future consideration. Even if you don’t hire someone for a particular role, the person might be a better fit for another position down the road. In Hire, you can find candidates easily with Google’s powerful search technology. Plus, Hire takes past interview feedback into account and ranks previous candidates higher if they’ve had positive feedback.
If you don’t manage your hiring process effectively, it can be a huge time sink, especially as employers take longer and longer to hire talent. If your business lags on making a decision, it can mean losing a great candidate.
Implementing a solution like Hire can make it a lot easier for companies to move quickly through the hiring process. Native integrations with the G Suite apps you’re already using can help you cut down on copy-pasting or having to jump between multiple tabs. If you email a candidate in Gmail, it’s automatically synced in Hire so the rest of the hiring team can follow the conversation. And if you need to schedule a multi-slot interview, you can do so easily in Hire which lets you access interviewer availability or even book conference rooms. Since launching in July, we’ve seen the average time between posting a position and hiring a candidate decrease from 128 days to just 21 days (3 weeks!).
Posted by Dave Smith,
Developer Advocate for IoT
Creating robust connections between IoT devices can be difficult. WiFi and
Bluetooth are ubiquitous and work well in many scenarios, but suffer limitations
when power is constrained or large numbers of devices are required on a single
network. In response to this, new communications technologies have arisen to
address the power and scalability requirements for IoT.
Low-power Wireless Personal Area Network (LoWPAN) technologies are specifically
designed for peer-to-peer usage on constrained battery-powered devices. Devices
on the same LoWPAN can communicate with each other using familiar IP networking,
allowing developers to use standard application protocols like HTTP and CoAP.
The specific LoWPAN technology that we are most excited about is Thread: a secure,
fault-tolerant, low-power mesh-networking technology that is quickly becoming an
Today we are announcing API support for configuring and managing LoWPAN as a
part of Android Things Developer Preview 6.1, including first-class networking
support for Thread. By adding an 802.15.4 radio module to one of our developer
kits, Android Things devices can communicate directly with other peer
devices on a Thread network. These types of low-power connectivity solutions
enable Android Things devices to perform edge computing tasks,
aggregating data locally from nearby devices to make critical decisions without
a constant connection to cloud services. See the LoWPAN API guide
for more details on building apps to create and join local mesh networks.
OpenThread makes getting started with LoWPAN
on Android Things easy. Choose a supported radio platform, such as the Nordic nRF52840, and
firmware to enable it as a Network Co-Processor (NCP). Integrate the radio
into Android Things using the LoWPAN
NCP user driver. You can also expand support to other radio hardware by
building your own user drivers. See the LoWPAN user driver
API guide for more details.
To get started with DP6.1, use the Android Things Console to
download system images and flash existing devices. Then download the LoWPAN sample app to try it
out for yourself! LoWPAN isn’t the only exciting thing happening in the latest
release. See the release
notes for the full set of fixes and updates included in DP6.1.
Please send us your feedback by filing bug
reports and feature
requests, as well as asking any questions on Stack
Overflow. You can also join Google’s IoT
Developers Community on Google+, a great resource to get updates and discuss
ideas. Also, we have our new hackster.io
community, where everyone can share the amazing projects they have built. We
look forward to seeing what you build with Android Things!
For thousands of years, people have looked up at the stars, recorded observations, and noticed patterns. Some of the first objects early astronomers identified were planets, which the Greeks called “planētai,” or “wanderers,” for their seemingly irregular movement through the night sky. Centuries of study helped people understand that the Earth and other planets in our solar system orbit the sun—a star like many others.
Today, with the help of technologies like telescope optics, space flight, digital cameras, and computers, it’s possible for us to extend our understanding beyond our own sun and detect planets around other stars. Studying these planets—called exoplanets—helps us explore some of our deepest human inquiries about the universe. What else is out there? Are there other planets and solar systems like our own?
Though technology has aided the hunt, finding exoplanets isn’t easy. Compared to their host stars, exoplanets are cold, small and dark—about as tricky to spot as a firefly flying next to a searchlight … from thousands of miles away. But with the help of machine learning, we’ve recently made some progress.
One of the main ways astrophysicists search for exoplanets is by analyzing large amounts of data from NASA’s Kepler mission with both automated software and manual analysis. Kepler observed about 200,000 stars for four years, taking a picture every 30 minutes, creating about 14 billion data points. Those 14 billion data points translate to about 2 quadrillion possible planet orbits! It’s a huge amount of information for even the most powerful computers to analyze, creating a laborious, time-intensive process. To make this process faster and more effective, we turned to machine learning.
Machine learning is a way of teaching computers to recognize patterns, and it’s particularly useful in making sense of large amounts of data. The key idea is to let a computer learn by example instead of programming it with specific rules.
I’m a Google AI researcher with an interest in space, and started this work as a 20 percent project (an opportunity at Google to work on something that interests you for 20 percent of your time). In the process, I reached out to Andrew, an astrophysicist from UT Austin, to collaborate. Together, we took this technique to the skies and taught a machine learning system how to identify planets around faraway stars.
Using a dataset of more than 15,000 labeled Kepler signals, we created a TensorFlow model to distinguish planets from non-planets. To do this, it had to recognize patterns caused by actual planets, versus patterns caused by other objects like starspots and binary stars. When we tested our model on signals it had never seen before, it correctly identified which signals were planets and which signals were not planets 96 percent of the time. So we knew it worked!
Kepler 90i is the eighth planet discovered orbiting the Kepler 90 star, making it the first known 8-planet system outside of our own.
Armed with our working model, we shot for the stars, using it to hunt for new planets in Kepler data. To narrow the search, we looked at the 670 stars that were already known to host two or more exoplanets. In doing so, we discovered two new planets: Kepler 80g and Kepler 90i. Significantly, Kepler 90i is the eighth planet discovered orbiting the Kepler 90 star, making it the first known 8-planet system outside of our own.
Some fun facts about our newly discovered planet: it’s 30 percent larger than Earth, and with a surface temperature of approximately 800°F—not ideal for your next vacation. It also orbits its star every 14 days, meaning you’d have a birthday there just about every two weeks.
The sky is the limit (so to speak) when it comes to the possibilities of this technology. So far, we’ve only used our model to search 670 stars out of 200,000. There may be many exoplanets still unfound in Kepler data, and new ideas and techniques like machine learning will help fuel celestial discoveries for many years to come. To infinity, and beyond!