Improving Search for the next 20 years

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. ?

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:

The shift from answers to journeys: To help you resume tasks where you left off and learn new interests and hobbies, we’re bringing new features to Search that help you with ongoing information needs.

The shift from queries to providing a queryless way to get to information: We can surface relevant information related to your interests, even when you don’t have a specific query in mind.

And the shift from text to a more visual way of finding information: We’re bringing more visual content to Search and completely redesigning Google Images to help you find information more easily.

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.

原文鏈接:https://www.blog.google/products/search/improving-search-next-20-years/

?著作權(quán)歸作者所有,轉(zhuǎn)載或內(nèi)容合作請聯(lián)系作者
  • 序言:七十年代末蝌诡,一起剝皮案震驚了整個濱河市,隨后出現(xiàn)的幾起案子炼绘,更是在濱河造成了極大的恐慌磺送,老刑警劉巖微姊,帶你破解...
    沈念sama閱讀 219,490評論 6 508
  • 序言:濱河連續(xù)發(fā)生了三起死亡事件噪矛,死亡現(xiàn)場離奇詭異挡鞍,居然都是意外死亡兽间,警方通過查閱死者的電腦和手機,發(fā)現(xiàn)死者居然都...
    沈念sama閱讀 93,581評論 3 395
  • 文/潘曉璐 我一進店門价匠,熙熙樓的掌柜王于貴愁眉苦臉地迎上來爷速,“玉大人,你說我怎么就攤上這事霞怀。” “怎么了莉给?”我有些...
    開封第一講書人閱讀 165,830評論 0 356
  • 文/不壞的土叔 我叫張陵毙石,是天一觀的道長。 經(jīng)常有香客問我颓遏,道長徐矩,這世上最難降的妖魔是什么? 我笑而不...
    開封第一講書人閱讀 58,957評論 1 295
  • 正文 為了忘掉前任叁幢,我火速辦了婚禮滤灯,結(jié)果婚禮上,老公的妹妹穿的比我還像新娘。我一直安慰自己鳞骤,他們只是感情好窒百,可當(dāng)我...
    茶點故事閱讀 67,974評論 6 393
  • 文/花漫 我一把揭開白布。 她就那樣靜靜地躺著豫尽,像睡著了一般篙梢。 火紅的嫁衣襯著肌膚如雪。 梳的紋絲不亂的頭發(fā)上美旧,一...
    開封第一講書人閱讀 51,754評論 1 307
  • 那天渤滞,我揣著相機與錄音,去河邊找鬼榴嗅。 笑死妄呕,一個胖子當(dāng)著我的面吹牛,可吹牛的內(nèi)容都是我干的嗽测。 我是一名探鬼主播绪励,決...
    沈念sama閱讀 40,464評論 3 420
  • 文/蒼蘭香墨 我猛地睜開眼,長吁一口氣:“原來是場噩夢啊……” “哼论咏!你這毒婦竟也來了优炬?” 一聲冷哼從身側(cè)響起,我...
    開封第一講書人閱讀 39,357評論 0 276
  • 序言:老撾萬榮一對情侶失蹤厅贪,失蹤者是張志新(化名)和其女友劉穎蠢护,沒想到半個月后,有當(dāng)?shù)厝嗽跇淞掷锇l(fā)現(xiàn)了一具尸體养涮,經(jīng)...
    沈念sama閱讀 45,847評論 1 317
  • 正文 獨居荒郊野嶺守林人離奇死亡葵硕,尸身上長有42處帶血的膿包…… 初始之章·張勛 以下內(nèi)容為張勛視角 年9月15日...
    茶點故事閱讀 37,995評論 3 338
  • 正文 我和宋清朗相戀三年,在試婚紗的時候發(fā)現(xiàn)自己被綠了贯吓。 大學(xué)時的朋友給我發(fā)了我未婚夫和他白月光在一起吃飯的照片懈凹。...
    茶點故事閱讀 40,137評論 1 351
  • 序言:一個原本活蹦亂跳的男人離奇死亡,死狀恐怖悄谐,靈堂內(nèi)的尸體忽然破棺而出介评,到底是詐尸還是另有隱情,我是刑警寧澤爬舰,帶...
    沈念sama閱讀 35,819評論 5 346
  • 正文 年R本政府宣布们陆,位于F島的核電站,受9級特大地震影響情屹,放射性物質(zhì)發(fā)生泄漏坪仇。R本人自食惡果不足惜,卻給世界環(huán)境...
    茶點故事閱讀 41,482評論 3 331
  • 文/蒙蒙 一垃你、第九天 我趴在偏房一處隱蔽的房頂上張望椅文。 院中可真熱鬧喂很,春花似錦、人聲如沸皆刺。這莊子的主人今日做“春日...
    開封第一講書人閱讀 32,023評論 0 22
  • 文/蒼蘭香墨 我抬頭看了看天上的太陽芹橡。三九已至毒坛,卻和暖如春,著一層夾襖步出監(jiān)牢的瞬間林说,已是汗流浹背煎殷。 一陣腳步聲響...
    開封第一講書人閱讀 33,149評論 1 272
  • 我被黑心中介騙來泰國打工, 沒想到剛下飛機就差點兒被人妖公主榨干…… 1. 我叫王不留腿箩,地道東北人豪直。 一個月前我還...
    沈念sama閱讀 48,409評論 3 373
  • 正文 我出身青樓,卻偏偏與公主長得像珠移,于是被迫代替她去往敵國和親弓乙。 傳聞我的和親對象是個殘疾皇子,可洞房花燭夜當(dāng)晚...
    茶點故事閱讀 45,086評論 2 355

推薦閱讀更多精彩內(nèi)容