June 26, 2020
Are you looking to learn how tech giant Google is using machine learning to grow its business? Here are 6 machine learning applications developed by Google
In 2016, CEO Sundar Pichai announced the tech company was rebranding itself as a machine learning first company.
As of January 2020, Google reached $1 trillion in market value and machine learning was one of the factors driving the tech giant’s success.
According to a McKinsey analysis, the average professional receives 120 messages per day and spends 2.6 hours reading and answering email.
That’s a stupefying large amount of time dedicated to clearing out your inbox! Not to mention it lowers your daily productivity rate.
Professionals have been reported to take one of the following approaches when it comes to their inboxes: obsessively keeping them clear or giving up control, just letting them fill up. Which one are you?
Leveraging the power of machine learning, Google comes to the rescue of every inbox-challenged professional. Google has created two features designed to help us save time: Smart Compose and Smart Reply.
Smart Compose suggests complete sentences in your emails so that you can draft them with ease.
Working in the background, this feature will offer suggestions as you type, from your greeting to your closing.
Smart Compose helps save you time by cutting back on repetitive writing, while reducing the chance of spelling and grammatical errors.
Responding to your emails on your desktop is easy. But for professionals always on the go, responding to emails on mobile is not as easy.
That’s why Google designed Smart Reply, the feature which saves you time by suggesting quick responses to your messages.
Based on the email you received, Smart Reply suggests three responses.
Once you’ve selected one, you can send it immediately or edit your response starting with the Smart Reply text.
Because this feature is built on machine learning, the more you use it, the better responses it will give you.
The latest statistic about the estimated number of digital photos taken worldwide is 1.2 trillion for the year 2017. That means 160 pictures a day for every one of the 7 billion people on Planet Earth.
Here’s another statistic: every 2 minutes, humans take more photos than ever existed in total 150 years ago.
The reason? Smartphones. It’s easy to take pictures with your smartphone.
Because smartphones are very convenient and user-friendly, a change in user behavior has been reported. We no longer take photos to make memories, but to also express how we feel, to illustrate our state of mind.
Taking photos is a big part of our lives now and Google created a feature to support that. The feature is called Suggested actions and it is powered by machine learning.
The feature shows up on your photos in Google Photos as you view them and gives you the option to adjust light, play with pops of color, crop, share, rotate or archive a picture.
We use Google to find answers by typing questions in the search bar.
What about when you can’t explain what you are looking at with words, but you can show the subject of your inquiry in a picture?
For this type of situation, Google created Google Lens.
Google Lens is a machine-learning powered feature which helps users browse the world around them, just by pointing their camera.
Are you walking by an interesting looking building wishing to know what it’s called?
Point the camera at the building and ask Google Lens. The Lens will search through billions of building images and find a match in a split second.
You can also use it to style search. When an outfit or home decor piques your attention, with Google Lens you can get info on that specific item and also similar choices and where to buy them.
It is also worth mentioning that Lens works in real time, surfacing information instantly.
This wouldn’t be possible without state-of-the-art machine learning.
According to WHO, there are 2.2 billion people with vision impairment or blindness in the world today. Being independent and not relying on others is their main challenge.
To help blind and visually impaired people, Google developed Lookout, an app which provides them with tools they can use to learn about their surroundings.
Lookout gives auditory cues as they encounter objects, text and people around them.
The app processes items of importance in the environment and shares information it believes to be relevant—the location of a bathroom, an exit sign, a chair or a person nearby and delivers spoken notifications.
As more people use the app, Lookout will use machine learning to learn what people are interested in hearing about, and will deliver these results more often.
In 2019, Google’s ad revenue amounted to almost $134.81 billion, 2x more than Facebook ($70 billion), its runner-up, with Alibaba ($30 billion) and Amazon ($14 billion) struggling to stay in the race.
Google reports the biggest ad revenue in the world and machine learning is one of the factors that drive such amazing results.
To support advertisers get more from their ads campaigns, Google turned to machine learning.
In 2018, the company introduced responsive search ads which enable advertisers to deliver relevant, valuable and optimized ads.
Marketers provide Google with 15 headlines and 4 description lines, and let the company’s powerful machine learning system do the rest.
The system will test different combinations, learn which ad creative performs best and deliver it to your target.
Advertisers who use Google’s machine learning to test multiple creative see up to 15% more clicks.
In the past decade, many voices in the media have spoken against digital blaming it for the disappearance of publishers which failed to transition from traditional to online.
Machine learning provides journalists all over the world with tools that have not been available before digital.
Machine learning allows them to comb through massive amounts of digital datasets, uncover valuable insights and write more relevant stories.
With digital tools, journalists can tag images and videos based on the content inside.
This allows editors to find relevant visuals more quickly than going through them by hand, one by one.
Machine learning can also classify content more accurately which helps increase readership by driving smarter article recommendations.
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