Gen Z creator uses the web to educate on racial justice

Kahlil Greene is a self-described “Gen Z historian” who uses social media to advocate for change. He established himself as an influencer, educator and voice for justice with his thought-provoking videos, engaging oratory style and eye-catching graphics. He has more than 500,000 followers and 20 million views across his social media profiles — with 5.5 million likes on TikTok alone.

From influencer to entrepreneur

Kahlil first used social media to educate, activate and inform students while he was a sophomore at Yale. He was elected the college’s first Black student body president, and worked to represent student activists, whose voices were historically marginalized or suppressed. To help do so, he designed social media infographics for the Yale College Council, and their Instagram post on “Being the Change” lists over 70 advocacy projects that were completed under his leadership.

Off campus, Kahlil worked to develop an online presence as a racial justice advocate, usinghis TikTok andInstagram accounts to spark candid conversation about Black history and racism. HisJuneteenth slideshow on Instagram from July 2020 “went insanely viral,” with over 57,000 likes. Another post from a month earlier about how people talk aboutviolence against the Black community gained as much attention.

Kahlil’s Instagram profile features his image superimposed over posts addressing current events.

Kahlil’s Instagram profile features his take on current events, using videos and infographics.

Today, Kahlil is an in-demand public speaker and consultant, educating schools, nonprofits and businesses on diversity, equity and inclusion (DEI) initiatives. He also works with brands on their corporate social responsibility campaigns. “I share a Gen Z perspective on subjects related to history, culture and politics,” he explains. “I can amplify arguments that other Gen Zers might not be able to make. I'm forging a bridge between our generational ideas and large organizations and their leaders.”

Brands value the perspective and insights Kahlil brings, as they seek to reach Gen Z — a socially conscious, social-media savvy generation. “Society is trending towards the values that Gen Z holds,” Kahlil says. “Gen Zers are graduating college and choosing where to work. I wrote a Harvard Business Review article about how companies fail to meet those standards. Diversity and inclusion is not a 'nice to have' anymore. It's a 'need to have.'”

As Kahlil’s influence grew, businesses were reaching out to him via his email links on social media. “But those [profiles] didn’t tell people enough about me,” he says. “Other creators I admired had websites with blogs that looked very professional.” Kahlil decided he needed his own website to develop his voice as a writer, showcase his work and create a hub for his brand.

Kahlil is pictured on his website smiling, wearing a black Yale sweatshirt with a big kente-patterned “Y” and jeans. His homepage text lists his accomplishments as the Gen Z Historian.

Kahlil’s website pulls together his experience as the Gen Z Historian.

Creating a business website

Kahlil launched his Gen Z Historian website in March 2021, bringing together his ideas, experiences and media coverage. “I wanted my own space where I could document my journey and develop deeper connections with people,” Kahlil says. A website also gives Kahlil ownership over his content, he notes. In January 2022, he launched his blog, where he posts long-form articles such as “What Is DEI in the New Decade?,” a popular topic for his public speaking.

Kahlil keeps an editorial calendar and posts frequently, including around holidays, remembrances and other events, such as Martin Luther King Jr. Day. His ongoing Instagram series on “Hidden History” tells America’s untold stories, such as the link between racism and the Salem Witch Trials. He plans to reach an even wider audience through book, podcast and TV projects in the works.

Kahlil stands outside, in front of a tree in a green and white striped shirt and khaki pants. A building can be seen in the distance.

Kahlil encourages other advocacy-focused creators to find their voice on major online platforms to engage and enlighten audiences.

Finding a voice

To creators seeking a platform for advocacy, Kahlil offers this advice: “Find out what is not being said clearly, and use your voice to clarify that. Also, find topics that haven’t been talked about to death. A lot of my niche and audience come from either ideas that I clarify with my communication style or ideas that exist but haven't been shared for mass audience engagement. Those are the ways that you grab an audience if you want to be an online educator.”

Treats, trends and touchdowns — it’s game time

We are just days away from the biggest night in football and Google is here to help you prep. Warm-up with some trending food and stats, get ready to watch the game with Google TV, tackle football-related queries with Assistant and re-watch all your favorite commercials on AdBlitz. Start to finish, we’ve got you covered.

Gameday stats

It isn’t a gameday without delicious food. This time last year, we saw searches for wings increased more than 130% compared to a typical Sunday in 2021 on Google Maps, and around this time every year, we see search interest in buffalo wings spike. This year, we decided to take a look at what the most-searched wing flavors were across the United States.

Map of the United States showing which the most-searched wing flavors were by state.

But Sunday isn't just about food: We also checked out regional search interest in this year’s teams and quarterbacks.

Map of the United States showing which quarterback was the most-searched by state.
Map of the United States showing which football team was the most-searched by state.

And the big game isn’t complete without an epic halftime show. Take a look at this year’s performers ranked by search interest over the past year.

Most-searched 2022 halftime artists over the past year in the U.S.

  1. Eminem
  2. Snoop Dogg
  3. Dr. Dre
  4. Kendrick Lamar
  5. Mary J. Blige

Say “Hey Google” to get your head in the game

Get into the game day spirit with Google Assistant, which is ready to answer all of your questions about football’s biggest game day. Start with some of these questions that will have you sounding like a pro:

  • “Hey Google, who’s going to win the big game?”
  • “Hey Google, help me talk like a football fan.”
  • "Hey Google, Touchdown!"
  • “Hey Google, give me facts about football.”

From endzone to AdBlitz

The commercials are (almost) just as important as the game, and you can find big game ads on the YouTube AdBlitz channel. There you can stream playlists featuring the most comedic, dramatic, action-packed and inspirational commercials and teasers. YouTube lets you decide which brands scored big with their game day ad, too — and the five ads with the most views by February 20 will be given the honor of entering the AdBlitz winners’ circle.

Catch the game with Google TV

Tune into the big game on Sunday and stream everything from kickoff to the winning play with your Google TV device, other Android TV OS device or in the Google TV app on Android devices in the U.S. Coverage begins at 12pm ET on NBC and Peacock, or on the NBC channel in your preferred live TV app with a YouTube TV or Sling TV subscription.

If you’ve recently bought a new Google TV or other Android TV OS device, you can get started by enjoying six months of Peacock Premium at no extra cost (terms apply). Just head to your Apps tab and scroll down to find your Peacock offer.

Unlocking the Full Potential of Datacenter ML Accelerators with Platform-Aware Neural Architecture Search

Posted by Sheng Li, Staff Software Engineer and Norman P. Jouppi, Google Fellow, Google Research

Continuing advances in the design and implementation of datacenter (DC) accelerators for machine learning (ML), such as TPUs and GPUs, have been critical for powering modern ML models and applications at scale. These improved accelerators exhibit peak performance (e.g., FLOPs) that is orders of magnitude better than traditional computing systems. However, there is a fast-widening gap between the available peak performance offered by state-of-the-art hardware and the actual achieved performance when ML models run on that hardware.

One approach to address this gap is to design hardware-specific ML models that optimize both performance (e.g., throughput and latency) and model quality. Recent applications of neural architecture search (NAS), an emerging paradigm to automate the design of ML model architectures, have employed a platform-aware multi-objective approach that includes a hardware performance objective. While this approach has yielded improved model performance in practice, the details of the underlying hardware architecture are opaque to the model. As a result, there is untapped potential to build full capability hardware-friendly ML model architectures, with hardware-specific optimizations, for powerful DC ML accelerators.

In “Searching for Fast Model Families on Datacenter Accelerators”, published at CVPR 2021, we advanced the state of the art of hardware-aware NAS by automatically adapting model architectures to the hardware on which they will be executed. The approach we propose finds optimized families of models for which additional hardware performance gains cannot be achieved without loss in model quality (called Pareto optimization). To accomplish this, we infuse a deep understanding of hardware architecture into the design of the NAS search space for discovery of both single models and model families. We provide quantitative analysis of the performance gap between hardware and traditional model architectures and demonstrate the advantages of using true hardware performance (i.e., throughput and latency), instead of the performance proxy (FLOPs), as the performance optimization objective. Leveraging this advanced hardware-aware NAS and building upon the EfficientNet architecture, we developed a family of models, called EfficientNetX, that demonstrate the effectiveness of this approach for Pareto-optimized ML models on TPUs and GPUs.

Platform-Aware NAS for DC ML Accelerators
To achieve high performance, ML models need to adapt to modern ML accelerators. Platform-aware NAS integrates knowledge of the hardware accelerator properties into all three pillars of NAS: (i) the search objectives; (ii) the search space; and (iii) the search algorithm (shown below). We focus on the new search space because it contains the building blocks needed to compose the models and is the key link between the ML model architectures and accelerator hardware architectures.

We construct TPU/GPU specialized search spaces with TPU/GPU-friendly operations to infuse hardware awareness into NAS. For example, a key adaptation is maximizing parallelism to ensure different hardware components inside the accelerators work together as efficiently as possible. This includes the matrix multiplication units (MXUs) in TPUs and the TensorCore in GPUs for matrix/tensor computation, as well as the vector processing units (VPUs) in TPUs and CUDA cores in GPUs for vector processing. Maximizing model arithmetic intensity (i.e., optimizing the parallelism between computation and operations on the high bandwidth memory) is also critical to achieve top performance. To tap into the full potential of the hardware, it is crucial for ML models to achieve high parallelism inside and across these hardware components.

Overview of platform-aware NAS on TPUs/GPUs, highlighting the search space and search objectives.

Advanced platform-aware NAS has an optimized search space containing a set of complementary techniques to holistically improve parallelism for ML model execution on TPUs and GPUs:

  1. It uses specialized tensor reshaping techniques to maximize the parallelism in the MXUs / TensorCores.
  2. It dynamically selects different activation functions depending on matrix operation types to ensure overlapping of vector and matrix/tensor processing.
  3. It employs hybrid convolutions and a novel fusion strategy to strike a balance between total compute and arithmetic intensity to ensure that computation and memory access happens in parallel and to reduce the contention on VPUs / CUDA cores.
  4. With latency-aware compound scaling (LACS), which uses hardware performance instead of FLOPs as the performance objective to search for model depth, width and resolutions, we ensure parallelism at all levels for the entire model family on the Pareto-front.

EfficientNet-X: Platform-Aware NAS-Optimized Computer Vision Models for TPUs and GPUs
Using this approach to platform-aware NAS, we have designed EfficientNet-X, an optimized computer vision model family for TPUs and GPUs. This family builds upon the EfficientNet architecture, which itself was originally designed by traditional multi-objective NAS without true hardware-awareness as the baseline. The resulting EfficientNet-X model family achieves an average speedup of ~1.5x–2x over EfficientNet on TPUv3 and GPUv100, respectively, with comparable accuracy.

In addition to the improved speeds, EfficientNet-X has shed light on the non-proportionality between FLOPs and true performance. Many think FLOPs are a good ML performance proxy (i.e., FLOPs and performance are proportional), but they are not. While FLOPs are a good performance proxy for simple hardware such as scalar machines, they can exhibit a margin of error of up to 400% on advanced matrix/tensor machines. For example, because of its hardware-friendly model architecture, EfficientNet-X requires ~2x more FLOPs than EfficientNet, but is ~2x faster on TPUs and GPUs.

EfficientNet-X family achieves 1.5x–2x speedup on average over the state-of-the-art EfficientNet family, with comparable accuracy on TPUv3 and GPUv100.

Self-Driving ML Model Performance on New Accelerator Hardware Platforms
Platform-aware NAS exposes the inner workings of the hardware and leverages these properties when designing hardware-optimized ML models. In a sense, the “platform-awareness” of the model is a “gene” that preserves knowledge of how to optimize performance for a hardware family, even on new generations, without the need to redesign the models. For example, TPUv4i delivers up to 3x higher peak performance (FLOPS) than its predecessor TPUv2, but EfficientNet performance only improves by 30% when migrating from TPUv2 to TPUv4i. In comparison, EfficientNet-X retains its platform-aware properties even on new hardware and achieves a 2.6x speedup when migrating from TPUv2 to TPUv4i, utilizing almost all of the 3x peak performance gain expected when upgrading between the two generations.

Hardware peak performance ratio of TPUv2 to TPUv4i and the geometric mean speedup of EfficientNet-X and EfficientNet families, respectively, when migrating from TPUv2 to TPUv4i.

Conclusion and Future Work
We demonstrate how to improve the capabilities of platform-aware NAS for datacenter ML accelerators, especially TPUs and GPUs. Both platform-aware NAS and the EfficientNet-X model family have been deployed in production and materialize up to ~40% efficiency gains and significant quality improvements for various internal computer vision projects across Google. Additionally, because of its deep understanding of accelerator hardware architecture, platform-aware NAS was able to identify critical performance bottlenecks on TPUv2-v4i architectures and has enabled design enhancements to future TPUs with significant potential performance uplift. As next steps, we are working on expanding platform-aware NAS’s capabilities to the ML hardware and model design beyond computer vision.

Acknowledgements
Special thanks to our co-authors: Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc Le. We also thank many collaborators including Jeff Dean, David Patterson, Shengqi Zhu, Yun Ni, Gang Wu, Tao Chen, Xin Li, Yuan Qi, Amit Sabne, Shahab Kamali, and many others from the broad Google research and engineering teams who helped on the research and the subsequent broad production deployment of platform-aware NAS.

Source: Google AI Blog


Hyper-local ads targeting made easy and automated with Radium

Posted by Álvaro Lamas, Natalija Najdova

Location targeting helps your advertising to focus on finding the right customers for your business. Are you, as a digital marketer, spending a lot of time optimizing your location targeting settings for your digital marketing campaigns? Are your ads running only in locations where you can deliver your services to your users or outside as well?

Read further to find out how Radium can help automate your digital marketing campaigns location targeting and make sure you only run ads where you deliver your services.

The location targeting settings challenge

Configuring accurate location targeting settings in Marketing Platforms like Google Ads allows your ads to appear in the geographic locations that you choose: countries, cities and zip codes OR radius around a location. As a result, precise geo targeting could help increase the KPIs of your campaigns such as the return on investment (ROI), the cost per acquisition (CPA) at high volumes, etc.

Mapping your business area to the available targeting options (country, city and zip code targeting or radius targeting) in Marketing Platforms is a challenge that every business doing online marketing campaigns has faced. This challenge becomes critical if you offer a service that is only available in a certain geographical area. This is particularly relevant for Food or Grocery Delivery Apps or organizations that run similar business models.

Adjusting these location targeting settings is a time consuming process. In addition, manually translating your business or your physical stores delivery areas into geo targeting settings is also an error prone process. And not having optimal targeting options might lead to ads shown to users that you cannot really deliver your services to, so you would likely lose money and time setting location targeting manually.

How can Radium help you?

Radium is a simple Web Application, based on App Scripts, that can save you money and time. Its UI helps you automatically translate a business area into radius targeting settings, one of the three options for geo targeting in Google Ads. It also provides you with an overview of the geographical information about how well the radius targeting overlaps with your business delivery area.

It has a few extra features like merging a few areas into one and generating the optimal radius targeting settings for those.

How does it work?

You can get your app deployed and running in less than an hour following these instructions. Once you’re done, in no time you can customize and improve your radius targeting settings to better meet your needs and optimize your marketing efforts.

Per delivery area that you provide, you will be able to visualize different circles in the UI, select one from the default circles or opt in for custom circle radius settings:

  • Large Circle: Pre-generated circle that englobes the rectangle that surrounds the targeting area
  • Small Circle: Pre-generated circle contained in the rectangle that surrounds the targeting area, touching its sides
  • Threshold Circle: Pre-generated circle with the minimum radius to cover at least the 90% of your delivery area, to maximize targeting and minimize waste
  • Custom Circle: Circle which center and radius can be customized manually by drag-and-drop and using the controls of the UI

    Large Circle

    Small Circle

    Threshold Circle

    Custom Circle

Take advantage of metrics to compare between all the radius targeting options and select the best fit for your needs. In red you can see the visualization of the business targeting area and, overlapped in gray, the generated radius targeting.

Metrics:

  • Radius: radius of the circle, in km
  • % Intersection: area of the Business Targeting Area inside the circle / total Business Targeting Area size
  • % Waste: area of circle excluding the Business Targeting Area / total Business Targeting Area size
  • Circle Size: area of the circle, in km2
  • Intersection Size: area of the Business Targeting Area, in km2
  • Waste Size: area of the circle excluding the Business Targeting Area, in km2
  • Circle Score: % Intersection - % Waste. The highest score represent the sweet spot, maximizing the targeting area and minimizing the waste area

Once you are done optimizing the radius settings, it’s time to activate them in your marketing campaigns. Radium offers you different ways of storing and activating this output, so you can use the one that better fits your needs:

  • Export your data to a Spreadsheet. This will allow you to have a mapping of readable names for each delivery area and its targeting settings, to generate the campaign settings in the csv format expected by Google Ads and to bulk upload them using Google Ads Editor
  • Directly download the csv file that can be uploaded to Google Ads via Google Ads Editor to bulk upload the settings of your campaigns
  • Upload them manually using the Google Ads UI

Find all the details about how to activate your location targeting settings in this step by step guide

Getting started with Radium

There are only 2 things you need to have in order to benefit from Radium:

  • Very easy to generate Maps JavaScript API Key
  • Map of your business’ delivery areas in either format:
    • KML file representing the polygon shaped targeting areas (see sample file)
    • CSV file with lat-lng, radius and name of the area it belongs to, more oriented to physical stores and restaurants (see sample file)

To get you started please visit the Radium repository on Github.

Summary

So, in conclusion, Radium helps you automate the location targeting configuration and optimization for your Google Ads campaigns, saving you time and minimizing errors of manual adjustments.

Ask a Techspert: What does AI do when it doesn’t know?

As humans, we constantly learn from the world around us. We experience inputs that shape our knowledge — including the boundaries of both what we know and what we don’t know.

Many of today’s machines also learn by example. However, these machines are typically trained on datasets and information that doesn’t always include rare or out-of-the-ordinary examples that inevitably come up in real-life scenarios. What is an algorithm to do when faced with the unknown?

I recently spoke with Abhijit Guha Roy, an engineer on the Health AI team, and Ian Kivlichan, an engineer on the Jigsaw team, to hear more about using AI in real-world scenarios and better understand the importance of training it to know when it doesn’t know.

Abhijit, tell me about your recent research in the dermatology space.

We’re applying deep learning to a number of areas in health, including in medical imaging where it can be used to aid in the identification of health conditions and diseases that might require treatment. In the dermatological field, we have shown that AI can be used to help identify possible skin issues and are in the process of advancing research and products, including DermAssist, that can support both clinicians and people like you and me.

In these real-world settings, the algorithm might come up against something it's never seen before. Rare conditions, while individually infrequent, might not be so rare in aggregate. These so-called “out-of-distribution” examples are a common problem for AI systems which can perform less well when it’s exposed to things they haven’t seen before in its training.

Can you explain what “out-distribution” means for AI?

Most traditional machine learning examples that are used to train AI deal with fairly unsubtle — or obvious — changes. For example, if an algorithm that is trained to identify cats and dogs comes across a car, then it can typically detect that the car — which is an “out-of-distribution” example — is an outlier. Building an AI system that can recognize the presence of something it hasn’t seen before in training is called “out-of-distribution detection,” and is an active and promising field of AI research.

Okay, let’s go back to how this applies to AI in medical settings.

Going back to our research in the dermatology space, the differences between skin conditions can be much more subtle than recognizing a car from a dog or a cat, even more subtle than recognizing a previously unseen “pick-up truck” from a “truck”. As such, the out-of-distribution detection task in medical AI demands even more of our focused attention.

This is where our latest research comes in. We trained our algorithm to recognize even the most subtle of outliers (a so-called “near-out of distribution” detection task). Then, instead of the model inaccurately guessing, it can take a safer course of action — like deferring to human experts.

Ian, you’re working on another area where AI needs to know when it doesn’t know something. What’s that?

The field of content moderation. Our team at Jigsaw used AI to build a free tool called Perspective that scores comments according to how likely they are to be considered toxic by readers. Our AI algorithms help identify toxic language and online harassment at scale so that human content moderators can make better decisions for their online communities. A range of online platforms use Perspective more than 600 million times a day to reduce toxicity and the human time required to moderate content.

In the real world, online conversations — both the things people say and even the ways they say them — are continually changing. For example, two years ago, nobody would have understood the phrase “non-fungible token (NFT).” Our language is always evolving, which means a tool like Perspective doesn't just need to identify potentially toxic or harassing comments, it also needs to “know when it doesn’t know,” and then defer to human moderators when it encounters comments very different from anything it has encountered before.

In our recent research, we trained Perspective to identify comments it was uncertain about and flag them for separate human review. By prioritizing these comments, human moderators can correct more than 80% of the mistakes the AI might otherwise have made.

What connects these two examples?

We have more in common with the dermatology problem than you'd expect at first glance — even though the problems we try to solve are so different.

Building AI that knows when it doesn’t know something means you can prevent certain errors that might have unintended consequences. In both cases, the safest course of action for the algorithm entails deferring to human experts rather than trying to make a decision that could lead to potentially negative effects downstream.

There are some fields where this isn’t as important and others where it’s critical. You might not care if an automated vegetable sorter incorrectly sorts a purple carrot after being trained on orange carrots, but you would definitely care if an algorithm didn’t know what to do about an abnormal shadow on an X-ray that a doctor might recognize as an unexpected cancer.

How is AI uncertainty related to AI safety?

Most of us are familiar with safety protocols in the workplace. In safety-critical industries like aviation or medicine, protocols like “safety checklists” are routine and very important in order to prevent harm to both the workers and the people they serve.

It’s important that we also think about safety protocols when it comes to machines and algorithms, especially when they are integrated into our daily workflow and aid in decision-making or triaging that can have a downstream impact.

Teaching algorithms to refrain from guessing in unfamiliar scenarios and to ask for help from human experts falls within these protocols, and is one of the ways we can reduce harm and build trust in our systems. This is something Google is committed to, as outlined in its AI Principles.

Finding answers gets better with Chrome

Every month, we look to add more features to Chrome to help you find information and get things done while navigating the web, whether you're on your laptop or phone. Here’s what’s new:

Jump back into your Journeys and find what's next

Our days are constantly filled with interruptions. You might be researching across multiple pages for hikes for the weekend or information about vaccines, then quickly need to switch over to a last-minute work call, only to forget where you originally left off. Now with Journeys, rolling out in the latest version of Chrome for desktop, you can revisit past explorations grouped by topic.

When you type a related word into your search bar and click on “Resume your research” or visit the Chrome History Journeys page, you see a list of relevant sites you visited and can quickly pick up where you left off, whether it was earlier today or weeks ago. Journeys will even take into account how much you’ve interacted with a site to put the most relevant information front and center, while also bringing you helpful suggestions on related searches you may want to try next.

Importantly, you’re always in control of your data. You can delete individual items or entire clusters of activity — or turn off Journeys completely. As always, you’ll be able to clear your browsing history right from Chrome settings. Finally, Journeys currently only groups history on your device — nothing is saved to your Google account. And based on user feedback and interest, we’ll explore adding the ability to access Journeys in Chrome across multiple devices (just like bookmarks or passwords). Journeys is rolling out to Chrome desktop on any OS in English, German, Spanish, French, Italian, Dutch, Portuguese and Turkish.

Screenshot of the Journeys in Chrome browser history. The user can see a cluster of recent searches they did related to a trip to Yosemite or finding information on their new Nest Audio devices.

The Journeys feature of Chrome groups together your search history based on topic or intent

Take action directly from your address bar

Rolling out now, we’re releasing more Chrome Actions to help you get more things done quickly from the Chrome address bar. We first released Chrome Actions a couple years ago, with Actions like Clear browsing data. You can save time with an Action by typing its title. The Chrome address bar also predicts when you could benefit from a Chrome Action based on the words that have been typed. Some of our favorite new Actions are:

  • “Manage settings”
  • “Customize Chrome”
  • “View your Chrome history”
  • “Manage accessibility settings”
  • “Share this tab”
  • “Play Chrome Dino game”

Soon, look out for more Chrome Actions coming to more languages and to mobile.

Chrome-ify your Android home screen

With the new Chrome widgets for Android, you can quickly start a text search, voice search, Lens search or open an Incognito tab right from your homescreen. There’s even a shortcut to play the Chrome dino game – even if you’re online. Or, if you really love the dino game, there’s a widget dedicated just to our prehistoric friend. Rolling out now, to get the Chrome widget for Android, long press the Chrome icon then select “widgets.”

Screenshot of an Android homepage with a Chrome dino widget and two sizes of Chrome shortcuts widgets, which include a Search box and buttons for voice search, incognito, Lens and the dino game

Add new Chrome widgets to your Android homescreen

We have so many more tools and features that we think you’ll love in 2022. If you do have any suggestions on things you want to see, send us a tweet @googlechrome.

Introducing the new Search Ads 360

A lot has changed since Search Ads 360 was developed more than ten years ago. The ways people search and the expectations for the ads they see have evolved and expanded. As a result, advertisers are managing more complexity than ever before and turn to platforms like Search Ads 360 to simplify campaign management, measure performance and grow their business.

Today we’re introducing the new Search Ads 360 to help advertisers be ready for what’s next. We’re making the platform easier to use with a new user interface, and adding support for more search engine features and campaign types based on feedback from advertisers who told us they want an easier and more convenient way to build campaigns across advertising platforms. You’ll now have immediate support for most new Google Ads features and improved support for other channels and search engines, like Microsoft Advertising and Yahoo! Japan. We’ve also built new enterprise features that are only available in the new Search Ads 360 which will give you new ways to centralize and scale your day-to-day tasks across engines and accounts. As a result, now you’ll be able to get more of your work done from one place, which will save you time and help you drive better results.

Rebuilt on a Proven Platform

In order to help advertisers keep up with today’s demands, we redesigned and rebuilt Search Ads 360 using the same technology that powers Google Ads.

With this new common technology in place, the new Search Ads 360 will be able to manage and process more data than ever before — while still conforming to strict latency guidelines to deliver a fast user experience — which will unlock new enterprise innovations to help you centralize and scale your work. Second, it’ll offer immediate management support for most new features in Google Ads like Performance Max and Discovery campaigns.

It’s a total game-changer that most Google Ads content is now available for management. This not only extends productivity and automation, but unlocks the ability to activate things like Value Based Bidding in a way that was never before possible. Brent Ramos
Product Director, Adswerve

Let’s take a look at some of the key changes in the new Search Ads 360.

New Look, Faster Navigation

The refreshed look and feel of Search Ads 360 introduces a familiar user experience that closely resembles search engine tools like Google Ads and Microsoft Advertising — platforms that many Search Ads 360 users are familiar with — making navigation faster and easier for enterprise marketers to manage campaigns and drive performance.

I found the new simplified user interface easier to navigate and use. The new budget forecasting capabilities, reporting features, and reduced complexity allowed me to streamline workflows and minimize my workload by at least 20% last month.* Elizaveta Markina
Search Specialist, OMD NZ
Gif of the new interface

The new Search Ads 360 interface

Improved Search Engine Support

With a more modern infrastructure, the new Search Ads 360 is also able to offer better support across multiple advertising channels and search engines, including third-party partners. This means advertisers can access more features from other search engines than ever before. For example, the new Search Ads 360 introduces support for a number of new Yahoo! Japan features, like Dynamic Ads For Search and sitelink extension scheduling, as well as over ten additional Microsoft Advertising features including responsive search ads, call extensions, local inventory ads and additional Microsoft audience types like customer match.

The new user interface in Search Ads 360 help Media.Monks account teams identify optimization opportunities on a more streamlined basis. In fact, since we started using the new experience, the team is saving 2-3 hours per week. Jorie Fox
Director, Media.Monks (formerly MightyHive)

This added support will enable less task switching between advertising platforms so you can focus on getting more of your work done all in one place. We’ll continue to add more features and channel support in the coming quarters, and we encourage you to continue sharing feedback on what you’d like to see added.

Advanced Enterprise Innovations

We’ve also added a number of features unique to the new Search Ads 360. For instance, the new Search Ads 360 takes enterprise workflows to the next level by giving you new ways to centralize and scale your day-to-day tasks and key activities — like campaign management, automated rules and labels — and you'll now be able to make these changes across multiple advertisers at the same time.

"The new Search Ads 360 has unlocked value for our team: from the ability to manage new extensions for Google Ads and Microsoft Advertising, to cross-engine performance overviews, and editing campaigns from multiple accounts in a unified and familiar interface. All of these improvements have enabled us to manage paid search much more easily."
—Rodrigo Pozos, SEM Manager, Aeromexico

Gif of the rules builder UI

Automated rules builder in the new Search Ads 360

Current Search Ads 360 customers use automated campaign building features like inventory management and ad builder to efficiently manage their marketing at scale. In the new Search Ads 360 these will become a single unified feature called “Templates.” We expect this to become available later this year, at which point you’ll be able to activate business data across client accounts to automatically build and update your ads at scale using your own data feed.

Another great feature in the new Search Ads 360 is “Performance Center” — an improvement to budget management — that will take you beyond simply managing budgets. We expect this to become fully available later this year, at which point you’ll be able to incorporate enterprise planning capabilities like improved forecasting across search engines where you can explore different hypothetical scenarios to plan your media budgets.

Gif of the Performance Center UI

Performance Center in the new Search Ads 360

If you’d like to learn more about these and other new features in the new Search Ads 360, please reach out to your account representative or contact the support team.

The Road Ahead

This is an exciting new chapter for Search Ads 360 that paves the way for the next generation of enterprise innovations to optimize workflows and help you maximize performance. We look forward to building the future of enterprise Search together, and we encourage you to share your feedback so we can continue improving Search Ads 360.

Over the next couple of months, current Search Ads 360 users can expect to start gaining access to the new Search Ads 360. To get started, we recommend diving into the new Skillshop learning pathGet Started with Search Ads 360 for an in depth overview.

*Individual results may vary

Connect confidently with Google Meet security features

Safer Internet Day is about coming together for a better, safer internet – and we at Google for Education are committed to working with schools and families to provide a safe online learning environment. Every day, Google keeps more people safe online than anyone else in the world with products that are secure by default, private by design and put you in control. And this promise extends to all that we build for you, school leaders.

Constant online protections for education

At Google for Education, we’re always looking for new ways to keep you safe. All of our products are private by design, which means they support compliance with the most rigorous data privacy standards — including FERPA, COPPA and GDPR — and are regularly audited by independent, third-party organizations. By making Google for Education products secure by default, we provide additional layers of protection, with ad-free learning experiences that help keep students safe from online threats and age-inappropriate content. And we put you in control, with a dashboard that gives you full visibility of your data and security, regular Google Security Checkups that help you maintain a secure account and additional security features in your security center to protect your school’s data and devices.

Our goal is to support and protect each member of your education community so they can focus on what matters most: teaching and learning.

Google Meet offers more moderation, control and integration

With our ongoing effort to provide a safer learning environment, we’ve been focusing on combating a prominent security pain point for many schools today: video meetings. We’re excited to share some recently announced enhanced security measures for Google Meet to help educators and students connect in a full class setting or one-on-one with fewer distractions and more privacy and security.

In-meeting moderation controls: To help educators engage with their students, we’ve added more ways to help moderate class meetings and eliminate unwanted intrusions or interruptions. With these new features hosts can:

  • Control who can use the chat and present features
  • Turn on or off audio and video of individuals or everyone in the main call and breakout rooms
  • Move participants from breakout rooms[f18fc6]back to the main room
  • Share moderation controls with up to 25 co-hosts

Control and visibility: We know admins need more ways to protect their schools and more data and insights to drive comprehensive decision making, so we’ve rolled out additional admin features that allow them to:

  • Apply safety settings across their domain
  • End meetings for everyone and prevent people from rejoining
  • Get insights into how people are using Meet
  • Identify, triage and act upon any security breaches[f3304d]

Google Classroom integration: We’re making Meet and Classroom work even better together. The Google Meet integration with Classroom helps educators meet and work with their classes more easily and securely, allowing them to:

  • Access the Class Meet link from the stream to limit distribution to class members only, while making meeting links easier for teachers to manage and for students to find
  • Keep students in a waiting room until the teacher joins, and uninvited guests must ask to join to ensure a safer environment for class interaction
  • All designated co-teachers are co-hosts by default so multiple teachers can help keep the class meeting on track and secure

Built-in security

In addition to these newly added moderation and security features, Google Meet runs on one of the world's most advanced security infrastructures for scalability and control. Meet adheres to IETF security standards for Datagram Transport Layer Security (DTLS) and Secure Real-time Transport Protocol (SRTP). In Meet, all data is encrypted in transit by default, whether meeting on a web browser, on the Android and iOS apps, or in meeting rooms with Google meeting room hardware. Meeting IDs are 10 characters long, with 25 characters in the set, making unauthorized access by guessing the ID extremely difficult.

We look forward to sharing more about our work to keep you safer with Google, including details on our new partnership with Khan Academy to develop free, online lessons that will help teach people how to stay safe online.

We remain committed to providing industry-leading privacy and security protections built into Google for Education products, which enable students and teachers to work better together by connecting safely and securely.

Get a slice of these Google Maps pizza trends

With all of the change we’ve experienced over the past couple of years, one thing remains constant … people’s love for pizza. In fact, pizza was the top-searched dish on Google Maps during every month of 2021 in the U.S.

To celebrate National Pizza Day on February 9, we’ve compiled some not-so-cheesy Google Maps pizza trends so you can get a slice of the action:

  • In garlic crust we trust. For the second year in a row, Domino’s Pizza remains the top searched pizza spot in the U.S.
  • Pizza cravin’ in New Haven. As of February 2022, Connecticut is the state with the most pizzerias per capita. New Jersey comes in at a close second.
  • A not-so-meaty New York Slice. People in the New York metropolitan area search for “vegan pizza” on Google Maps more than in any other U.S. metro.
  • Rolling in the deep dish. It likely comes as no surprise that people in Chicago search for “deep dish” on Google Maps more than in any other U.S. metro. In fact, Google Maps searches for “deep dish” in Chicago are nearly four times higher than the second place city, which is Los Angeles.
  • Which city can’t be topped? Of the 10 top-searched local pizzerias on Google Maps, nine are located in New York or Chicago. The one exception? Seattle’s ROCCO'S.

Shop talk with a 'crust'-ed pizza source

To celebrate the occasion we also caught up with Chuck Mound, a level 10 Local Guide from New Jersey with a lifetime of pizza love. Seriously, when he was 10 years old he tried to set a Guinness World Record for the most consecutive days eating a slice of pizza! Today he travels the country as a sport performance coach leaving reviews of pizzerias.

How did you get started sharing reviews on Google Maps?

In my 30-year coaching career, I’ve coached on all levels of competition — from youth to professional, including Olympic and NFL teams. While presenting in over 45 states, I’ve had the opportunity to eat the best foods around the country during pre- and post-game festivities. I like to help people not only understand where to go, but what you should order once you get there.

What qualities do you look for in a pizzeria and how do you approach your pizza reviews?

When I go somewhere new I love to know their niche or their signature dish. I like to dive in and learn more about the dough, the crust and the sauce. When reviewing places I try to describe the whole experience. Customer service can be just as important as the food.

What types of pizzerias get you excited?

I love to support local businesses. I won’t go cookie cutter. I like to find that hole-in-the-wall place where Grandma is making the sauce. I feed off of passion. If you’re passionate about what you do, that’s fantastic … I’ve eaten a lot of bad pizza, I’m over eating bad pizza.

Check out Chuck's list of knead-to-know pizzerias around the U.S. And once you’ve chewed your way through that, we’ve compiled some of the top searched and best-reviewed pizza spots —  excluding major chains — on Google Maps!

The ultimate pizza lists

A gif showing the top pizza chains in each US state, according to Google Maps searches

The top pizza chains in each US state, according to Google Maps searches

Here are the 10 top-searched pizzerias on Google Maps:

  1. Carnegie Pizza (New York, NY)
  2. Enzo Bruni la pizza gourmet (New York, NY)
  3. Rubirosa Pizza & Ristorante (New York, NY)
  4. ROCCO'S (Seattle, WA)
  5. Quartino Ristorante (Chicago, IL)
  6. Prince St. Pizza (New York, NY)
  7. 99 Cent Fresh Pizza (New York, NY)
  8. Bleecker Street Pizza (New York, NY)
  9. Bar Siena (Chicago, IL)
  10. Joe's Pizza on 8th Ave (New York, NY)

And if you're looking for some suggestions on where to get the best slice, these are 10 of the best reviewed pizza joints on Maps in the U.S.:

  1. Varasano's Pizzeria - Buckhead (Atlanta, GA)
  2. Olli Olive Pizza (Lauderhill, FL)
  3. Tommie's Pizza (Saint Paul, MN)
  4. PanezaNellie Breadstick Shoppe (Sublimity, OR)
  5. KC’s Family Kitchen (Lockport, IL)
  6. Raceway (Yonkers, NY)
  7. Nolita Pizza (New York, NY)
  8. Zeneli Pizzeria e cucina Napoletana (New Haven, CT)
  9. Michigan & Trumbull (Detroit, MI)
  10. Goodfella’s Pizzeria of Sunnyside (Queens, NY)

So no matter how you celebrate National Pizza Day — whether you’re looking to discover a new slice or order from your local favorite — Google Maps is here to help you cheese the day. ?