Technology
25 Biggest Moments in Search, From Helpful Images to AI
Here’s how we’ve made Search more helpful over 25 years — and had a little fun along the way, too.
When Google first launched 25 years ago, it was far from the first search engine. But quickly, Google Search became known for our ability to help connect people to the exact information they were looking for, faster than they ever thought possible.
Over the years, we’ve continued to innovate and make Google Search better every day. From creating entirely new ways to search, to helping millions of businesses connect with customers through search listings and ads (starting with a local lobster business advertising via AdWords in 2001), to having some fun with Doodles and easter eggs — it’s been quite a journey.
For our 25th birthday, we’re looking back at some of the milestones that made Google more helpful in the moments that matter, and played a big role in where Google is today. Learn more about our history in our Search Through Time site.
2001: Google Images
When Jennifer Lopez attended the 2000 Grammy Awards, her daring Versace dress became an instant fashion legend — and the most popular query on Google at the time. Back then, search results were just a list of blue links, so people couldn’t easily find the picture they were looking for. This inspired us to create Google Images.
2001: “Did you mean?”
“Did you mean,” with suggested spelling corrections, was one of our first applications of machine learning. Previously, if your search had a misspelling (like “floorescent”), we’d help you find other pages that had the same misspelling, which aren’t usually the best pages on the topic. Over the years we’ve developed new AI-powered techniques to ensure that even if your finger slips on the keyboard, you can find what you need.

2002: Google News
During the tragic events of September 11, 2001, people struggled to find timely information in Search. To meet the need for real-time news, we launched Google News the following year with links to a diverse set of sources for any given story.
2003: Easter eggs
Googlers have developed many clever Easter eggs hidden in Search over the years. In 2003, one of our first Easter eggs gave the answer to life, the universe and everything, and since then millions of people have turned their pages askew, done a barrel roll, enjoyed a funny recursive loop and celebrated moments in pop culture.

One of our earliest Easter eggs is still available on Search.
2004: Autocomplete
Wouldn’t it be nice to type as quickly as you think? Cue Autocomplete: a feature first launched as “Google Suggest” that automatically predicts queries in the search bar as you start typing. Today, on average, Autocomplete reduces typing by 25% and saves an estimated over 200 years of typing time per day.
2004: Local information
People used to rely on traditional phone books for business information. The web paved the way for local discovery, like “pizza in Chicago” or “haircut 75001.” In 2004, Google Local added relevant information to business listings like maps, directions and reviews. In 2011, we added click to call on mobile, making it easy to get in touch with businesses while you’re on the go. On average, local results in Search drive more than 6.5 billion connections for businesses every month, including phone calls, directions, ordering food and making reservations.
2006: Google Translate
Google researchers started developing machine translation technology in 2002 to tackle language barriers online. Four years later, we launched Google Translate with text translations between Arabic and English. Today, Google Translate supports more than 100 languages, with 24 added last year.

2006: Google Trends
Google Trends was built to help us understand trends on Search with aggregated data (and create our annual Year in Search). Today, Google Trends is the world’s largest free dataset of its kind, enabling journalists, researchers, scholars and brands to learn how searches change over time.
2007: Universal Search
Helpful search results should include relevant information across formats, like links, images, videos, and local results. So we redesigned our systems to search all of the content types at once, decide when and where results should blend in, and deliver results in a clear and intuitive way. The result, Universal Search, was our most radical change to Search at the time.
2008: Google Mobile App
With the arrival of Apple’s App Store, we launched our first Google Mobile App on iPhone. Features like Autocomplete and “My Location” made search easier with fewer key presses, and were especially helpful on smaller screens. Today, there’s so much you can do with the Google app — available on both Android and iOS — from getting help with your math homework with Lens to accessing visual translation tools in just a tap.
2008: Voice Search
In 2008, we introduced the ability to search by voice on the Google Mobile App, expanding to desktop in 2011. With Voice Search, people can search by voice with the touch of a button. Today, search by voice is particularly popular in India, where the percentage of Indians doing daily voice queries is nearly twice the global average.

2009: Emergency Hotlines
Following a suggestion from a mother who had a hard time finding poison control information after her daughter swallowed something potentially dangerous, we created a box for the poison control hotline at the top of the search results page. Since this launch, we’ve elevated emergency hotlines for critical moments in need like suicide prevention.
2011: Search by Image
Sometimes, what you’re searching for can be hard to describe with words. So we launched Search by Image so you can upload any picture or image URL, find out what it is and where else that image is on the web. This update paved the way for Lens later on.
2012: Knowledge Graph
We introduced the Knowledge Graph, a vast collection of people, places and things in the world and how they’re related to one another, to make it easier to get quick answers. Knowledge Panels, the first feature powered by the Knowledge Graph, give you a quick snapshot of information about topics like celebrities, cities and sports teams.

2015: Popular Times: We launched the Popular Times feature in Search and Maps to help people see the busiest times of the day when they search for places like restaurants, stores, and museums.
2016: Discover
By launching a personalized feed (now called Discover) we helped people explore content tailored to their interests right in the Google app, without having to search.
2017: Lens
Google Lens turns your camera into a search query by looking at objects in a picture, comparing them to other images, and ranking those other images based on their similarity and relevance to the original picture. Now, you can search what you see in the Google app. Today, Lens sees more than 12 billion visual searches per month.
2018: Flood forecasting
To help people better prepare for impending floods, we created forecasting models that predict when and where devastating floods will occur with AI. We started these efforts in India and today, we’ve expanded flood warnings to 80 countries.

2019: BERT
A big part of what makes Search helpful is our ability to understand language. In 2018, we introduced and open-sourced a neural network-based technique to train our language understanding models: BERT (Bidirectional Encoder Representations from Transformers). BERT makes Search more helpful by better understanding language, meaning it considers the full context of a word. After rigorous testing in 2019, we applied BERT to more than 70 languages. Learn more about how BERT works to understand your searches.
2020: Shopping Graph
Online shopping became a whole lot easier and more comprehensive when we made it free for any retailer or brand to show their products on Google. We also introduced Shopping Graph, an AI-powered dataset of constantly-updating products, sellers, brands, reviews and local inventory that today consists of 35 billion product listings.
2020: Hum to Search
We launched Hum to Search in the Google app, so you’ll no longer be frustrated when you can’t remember the tune that’s stuck in your head. The machine learning feature identifies potential song matches after you hum, whistle or sing a melody. You can then explore information on the song and artist.
2021: About this result
To help people make more informed decisions about which results will be most useful and reliable for them, we added “About this result” next to most search results. It explains why a result is being shown to you and gives more context about the content and its source, based on best practices from information literacy experts. ‘About this’ result is now available in all languages where Search is available.
2022: Multisearch
To help you uncover the information you’re looking for — no matter how tricky — we created an entirely new way to search with text and images simultaneously through Multisearch. Now you can snap a photo of your dining set and add the query “coffee table” to find a matching table. First launched in the U.S., Multisearch is now available globally on mobile, in all languages and countries where Lens is available.
2023: Search Labs & Search Generative Experience (SGE)
Every year in Search, we do hundreds of thousands of experiments to figure out how to make Google more helpful for you. With Search Labs, you can test early-stage experiments and share feedback directly with the teams working on them. The first experiment, SGE, brings the power of generative AI directly into Search. You can get the gist of a topic with AI-powered overviews, pointers to explore more and natural ways to ask follow ups. Since launching in the U.S., we’ve rapidly added new capabilities, with more to come.
As someone who’s been following the world of search engines for more than two decades, it’s amazing to reflect on where Google started — and how far we’ve come.
Technology
Zoho Launches Nathu La Server
By Modupe Gbadeyanka
A designed-in-house server known as Nathu La has been launched by a global technology company, Zoho Corporation.
Nathu La is engineered with hardware-rooted security at every layer of the stack. Its indigenous IP-driven approach reduces dependency on external entities for security audits, firmware updates, and licensing continuity.
The solution aligns with open-source software principles and reflects Zoho’s broader commitment to building sustainable, secure, and scalable digital infrastructure. It also supports the growing global focus on digital sovereignty, local innovation ecosystems, and high-performance computing capabilities.
The platform was introduced by the company as part of a pivotal step in its journey towards building its full technology stack, from the hardware layer to software applications.
With Nathu La, Zoho has achieved equivalent performance with 12-18 per cent lower power consumption and 20-30 per cent lower total cost of ownership (TCO), thereby reducing inference costs.
The Nathu La server, comprising Intel® Xeon® 6 processors, was developed collaboratively with Intel, leveraging their enablement capabilities and technical expertise.
The design philosophy behind Nathu La is rooted in the Open Compute Project (OCP), emphasising modularity, thermal efficiency, and ease of maintenance. This enables Zoho’s data centres to significantly reduce total cost of ownership and power consumption.
Zoho plans to host its applications on the Nathu La server platform, enabling the company to optimise the full software-hardware stack for its specific workloads, reduce costs, improve performance, and strengthen data governance for its global customers. This will also help bring down inference costs for Zoho’s AI usage.
The Nathu La server motherboard and chassis platform is the result of five years of R&D across hardware, firmware, and systems management. Based on Intel® Xeon® 6 Processors, the server is designed to optimise performance for virtualisation (VM), High Performance Computing (HPC), AI inference, and storage applications. This results in improved performance of Zoho applications for end users.
The server features customised power delivery subsystems, an in-house DC-SCM (Data Centre Secure Control Module) design, and modular chassis options compatible with diverse end-user environments, offering flexibility across deployment types.
All modular components – including the DC-SCM and NIC (Network Interface Card) – were designed in-house by Zoho’s hardware engineering team and assembled through electronics manufacturing partners, enabling tighter integration and quality control across the platform. Over five patents have been filed covering advanced thermal management and cost-optimised server architecture designs.
“Zoho Corporation has invested in building its own technology stack from the ground up over the last three decades. The Nathu La server launch is in line with that goal.
“With our strategy of using contextual, right-sized models, running on our own platform, on our own servers, in our own data centres, we are compounding the benefits accrued from owning and operating our entire technology stack. This ensures that our solutions are more sustainable and accessible for businesses.
“These long-term R&D investments we are making at every layer of the stack are aimed at delivering customer value,” the Country Head for Zoho Nigeria, Mr Kehinde Ogundare, stated.
In 2020, Zoho established a small R&D team in Nagpur, a Tier 2 town in India, focused on projects such as server design and systems engineering.
Members of the Nathu La R&D team include hires from SETU – short for Students’ Engagement for Transformative Upskilling – an initiative designed to build a pipeline of industry-ready engineers, with a focus on advanced learning in Electronics System Design and Manufacturing (ESDM).
Technology
MTN Fintech Targets Credit Market With Direct Lending Plans
By Adedapo Adesanya
The financial technology arm of MTN is mulling a direct shift into lending after bringing on its parent company, MTN Group, as a major investor to help cushion against losses that have plagued the business.
According to MTN Group Fintech chief executive, Mr Serigne Dioum, the company wants to move beyond helping customers access loans through partners.
He said in markets where regulators allow it, MTN wants to lend directly and use its own balance sheet.
“We’ve expanded access to credit for more people, but we also want to move further up the lending value chain,” Mr Dioum told investors at the company’s capital markets day.
“Where appropriate, we will seek licences that allow us not only to facilitate loans but also to lend directly to customers and deploy our own balance sheet.”
This development is expected to create a shift in its current fintech model which provides financial services, including deposits, payments, transfers and digital wallets to individuals and small businesses via digital and mobile‑based platforms.
The company has applied for Payment Solution Service Provider and Payment Terminal Service Provider licences through MoMo PSB, its Nigerian fintech subsidiary. If approved, the licences would allow MTN to handle more payment processing, build merchant payment tools, deploy and manage POS terminals, and reduce its dependence on third-party processors.
Despite the opportunities present in the credit market, direct lending could give MTN a larger share of revenue, but it would also expose the company to credit risk, regulation and tougher competition with banks and digital lenders.
Mr Dioum said only about 4 per cent to 5 per cent of adults have access to formal credit across the African continent. In Nigeria, the funding problem is especially severe.
A 2025 report by the National Credit Guarantee Company said nearly 80 per cent of Nigerian MSMEs lack access to formal credit, while Stears has estimated the country’s MSME financing gap at about $236 billion.
For traders, small shop owners, transport operators and households, access to small loans can determine whether they restock inventory, pay suppliers, cover emergencies or expand a business.
In April, MTN Nigeria announced that its parent firm, based in South Africa, would acquire a 60 per cent stake in MoMo Payment Service Bank Limited (MoMo PSB) and Y’ello Digital Financial Services (YDFS) Limited.
The fintech units are currently loss-making, and this move will help MTN Nigeria to reduce financial risk and share future losses and investment burden. However, it will still keep a significant minority stake (40 per cent).
Technology
Meta Expands Business Agent to Instagram, WhatsApp, Messenger
By Aduragbemi Omiyale
The reach of the Meta Business Agent is being expanded to Instagram and other platforms of the social media giant.
Meta Business Agent is an artificial intelligence (AI) that allows business owners to attend to customers’ needs with ease.
Customers expect instant responses, but no team can be everywhere at once. This innovation handles such without hassles.
It helps businesses to answer questions specific to the business, makes product recommendations from the catalogue, books appointments, qualifies incoming leads, and closes sales.
More than one million businesses are already using a Meta Business Agent on WhatsApp and Messenger to respond to customers around the clock.
“We’re now expanding our Business Agent to businesses big and small globally, so within minutes you can have yours up and running, responding in your customer’s local language using your tone,” Meta said in a statement.
“We’re also expanding these agents to Instagram since businesses connect with their customers there, too. Businesses can activate their Business Agent here. Getting started with the Business Agent is free. In the coming months, businesses will access the agent through our paid subscription offerings, with options for businesses of every size,” it added.
Meta also stated that it is making it simpler for people to discover businesses powered by a Meta Business Agent directly on WhatsApp. It noted that starting soon, people will be able to find businesses by typing their name in the Search bar, or by sharing their phone number or contact card in chats with friends and family. This way, when more customers reach out, they get a quick, helpful response.
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