Tag Archives: #20YearsOfSearch

Improving Search for the next 20 years

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Growing up in India, there was one good library in my town that I had access to—run by the British Council.  It was modest by western standards, and I had to take two buses just to get there. But I was lucky, because for every child like me, there were many more who didn’t have access to the same information that I did. Access to information changed my life, bringing me to the U.S. to study computer science and opening up huge possibilities for me that would not have been available without the education I had.
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The British Council Library in my hometown.


When Google started 20 years ago, our mission was to organize the world’s information and make it universally accessible and useful. That seemed like an incredibly ambitious mission at the time—even considering that in 1998 the web consisted of just 25 million pages (roughly the equivalent of books in a small library).
Fast forward to today, and now we index hundreds of billions of pages in our index—more information than all the libraries in the world could hold. We’ve grown to serve people all over the world, offering Search in more than 150 languages and over 190 countries.
Through all of this, we’ve remained grounded in our mission. In fact, providing greater access to information is as core to our work today as it was when we first started. And while almost everything has changed about technology and the information available to us, the core principles of Search have stayed the same.
  • First and foremost, we focus on the user. Whether you’re looking for recipes, studying for an exam, or finding information on where to vote, we’re focused on serving your information needs.
  • We strive to give you the most relevant, highest quality information as quickly as possible. This was true when Google started with the Page Rank algorithm—the foundational technology to Search. And it’s just as true today.
  • We see billions of queries every day, and 15 percent of queries are ones we’ve never seen before. Given this scale, the only way to provide Search effectively is through an algorithmic approach. This helps us not just solve all the queries we’ve seen yesterday, but also all the ones we can’t anticipate for tomorrow.
  • Finally, we rigorously test every change we make. A key part of this testing is the rater guidelines which define our goals in search, and which are publicly available for anyone to see. Every change to Search is evaluated by experimentation and by raters using these guidelines. Last year alone, we ran more than 200,000 experiments that resulted in 2,400+ changes to search. Search will serve you better today than it did yesterday, and even better tomorrow.
As Google marks our 20th anniversary, I wanted to share a first look at the next chapter of Search, and how we’re working to make information more accessible and useful for people everywhere. This next chapter is driven by three fundamental shifts in how we think about Search:
    Underpinning each of these are our advancements in AI, improving our ability to understand language in ways that weren’t possible when Google first started. This is incredibly exciting, because over 20 years ago when I studied neural nets at school, they didn’t actually work very well...at all!
    But we’ve now reached the point where neural networks can help us take a major leap forward from understanding words to understanding concepts. Neural embeddings, an approach developed in the field of neural networks, allow us to transform words to fuzzier representations of the underlying concepts, and then match the concepts in the query with the concepts in the document. We call this technique neural matching. This can enable us to address queries like: “why does my TV look strange?” to surface the most relevant results for that question, even if the exact words aren’t contained in the page. (By the way, it turns out the reason is called the soap opera effect).
    Finding the right information about my TV is helpful in the moment. But AI can have much more profound effects. Whether it’s predicting areas that might be affected in a flood, or helping you identify the best job opportunities for you, AI can dramatically improve our ability to make information more accessible and useful.
    I’ve worked on Search at Google since the early days of its existence. One of the things that keeps me so inspired about Search all these years is our mission and how timeless it is. Providing greater access to information is fundamental to what we do, and there are always more ways we can help people access the information they need. That’s what pushes us forward to continue to make Search better for our users. And that’s why our work here is never done.

    Posted by Ben Gomes, VP, Search, News and Assistant

    Keeping people safe with AI-enabled flood forecasting

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    For 20 years, Google Search has provided people with the information they need, and in times of crisis, access to timely, actionable information is often crucial. Last year we launched SOS Alerts on Search and Maps to make emergency information more accessible. Since then, we’ve activated SOS Alerts in more than 200 crisis situations, in addition to tens of thousands of Google Public Alerts, which have been viewed more than 1.5 billion times.
    Floods are devastating natural disasters worldwide—it’s estimated that every year, 250 million people around the world are affected by floods, also costing billions of dollars in damages. Flood forecasting can help individuals and authorities better prepare to keep people safe, but accurate forecasting isn’t currently available in many areas. And the warning systems that do exist can be imprecise and non-actionable, resulting in far too many people being underprepared and under informed before a flood happens.
    To help improve awareness of impending floods, we're using AI and significant computational power to create better forecasting models that predict when and where floods will occur, and incorporating that information into Google Public Alerts. A variety of elements—from historical events, to river level readings, to the terrain and elevation of a specific area—feed into our models. From there, we generate maps and run up to hundreds of thousands of simulations in each location. With this information, we’ve created river flood forecasting models that can more accurately predict not only when and where a flood might occur, but the severity of the event as well.
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    These images depict a flood simulation of a river in Hyderabad, India. The left side uses publicly available data while the right side uses Google data and technology. Our models contain higher resolution, accuracy, and up-to-date information.


    We started these flood forecasting efforts in India, where 20 percent of global flood-related fatalities occur. We’re partnering with India’s Central Water Commission to get the data we need to roll out early flood warnings, starting with the Patna region. The first alert went out earlier this month after heavy rains in the region.
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    Flood alert shown to users in the Patna region.


    We’re also looking to expand coverage to more countries, to help more people around the world get access to these early warnings, and help keep them informed and safe.

    Posted by Yossi Matias, VP, Engineering