In less than two decades, Google has gone from processing 10,000 searches per day to 63,000 per second on any given day. Furthermore, there are now more than 1.94 billion websites and over three billion Internet users worldwide.
These staggering numbers pose considerable challenges for Google and other search engines. After all, they can no longer rely solely on a group of search engineers to adjust algorithms manually and monitor trends to provide the most relevant search engine results.
The solution? A combination of Artificial Intelligence (AI), Deep Learning, and Machine Learning.
AI, Deep Learning, and Machine Learning: Making Searches Easier
AI, Deep Learning, and Machine Learning are powerful analytic tools that analyze huge volumes of data. They can find correlations, data patterns, and probable cause-and-effect relationships at a non-human speed.
AI search engines utilize “automated machine learning” techniques such as image analysis and natural language processing (NLP) to understand search queries. However, it still mostly relies on information retrieval strategies, recognition algorithms, or data analytics comprising complex mathematical algorithms for data modeling, training, processing, and testing.
An important part of AI search engines is its ability to PREDICT. It does this by understanding the context of the search query and the user’s intention: the type of device (computer, cell phone, tablet, etc.), location of the device, search history, time of day, previous queries, keywords, etc.
Today, Google uses a machine-learning artificial intelligence system known as “RankBrain” to help sort through its search results.
In 2017, Bing launched Intelligent Search to make it easier for users to find what they are looking for—whether it’s an answer to a difficult question or an item that you want to learn more about.
Every Day, Practical Applications of AI and Machine Learning
Here are some everyday examples of how AI and machine learning are making searches easier for users:
- AI-Powered Predictions
Using anonymous location data from smartphones, Google Maps can analyze the speed of movement of traffic at any given period. Moreover, with its acquisition of the traffic app “Waze” in 2013, Maps can now incorporate user-reported traffic incidents like accidents and construction. Access to huge amounts of data from being fed to its proprietary algorithms means that Google Maps can reduce commutes by recommending the fastest routes to and from work.
- Spam Filters
Your email inbox may seem like an unlikely place for AI, but did you know that this technology is actually powering one of its most important features—the spam filter? Simple rules-based filters (i.e., filter out messages with the words “Nigerian Prince” and “Lottery Winner” from unknown addresses) are not always effective against spam, because spammers can adapt their messages to work around them. Instead, spam filters must continuously learn from different signals, such as message keywords, message metadata, etc.
Furthermore, they must personalize their results based on your definition of what constitutes a spam message. Perhaps the “Daily Deals” email that you consider as spam is actually important for others. Through the use of AI and machine learning algorithms, Gmail successfully filters 99.9% of spam messages.
- Social Networking
Did you notice that when you upload photos to Facebook, it automatically highlights faces and suggests friends to tag? Facebook uses artificial neural networks and machine learning algorithms to power its facial recognition software. Also, did you know that Facebook uses AI to personalize your newsfeed? By doing so, they ensure that you are seeing posts that interest you. They also show ads that are relevant to your preferences.
On Pinterest, they use “computer vision”—an application of AI where computers are taught to “see” in order to automatically identify objects in pins (images) and then recommend visually similar pins. Other applications of machine learning at Pinterest included search and discovery, email marketing, and ad performance and monetization.
On Instagram, they utilize machine learning to identify the contextual meaning of emojis which are steadily replacing Internet slang (i.e., a laughing emoji can replace “lol”). Instagram algorithmically identifies the sentiments behind emojis and creates and auto-suggests emojis and emoji hashtags. This may seem like a trivial use of AI, but Instagram has actually seen a big increase in emoji use among all demographics.
- Online Shopping: Search and Recommendations
Your Amazon searches (“headphones,” “PS4,” “laptop,” etc.) quickly return a list of the most relevant products related to your search. While they don’t exactly reveal how they do this, in a description of their product search technology, Amazon notes that they use certain algorithms to automatically learn to combine multiple relevant features. Their catalog’s structured data provides them with many relevant features and they learn from past search patterns to adapt to what is important to their customers.
As for recommendations: Did you notice that you see recommendations for products that you are interested in? For instance, you see prompts like “customers who bought this item also bought” or “customers who viewed this item also viewed,” as well as recommendations on the bottom of item pages, the home page, and through email. To generate these product recommendations, Amazon uses artificial neural networks.
- Voice-to-Text and Smart Personal Assistants
One of the standard features of today’s smartphones is voice-to-text. With the press of a button or by saying a particular phrase (i.e., “Ok Google”), you can open a voice-to-text app. From there, you can start speaking and your phone converts that audio into text. To power voice search, Google uses artificial neural networks.
Now that the technology for voice-to-text is accurate enough to use for basic conversations, it has become the control interface for a new generation of smart personal assistants. Today, smart personal assistants such as Siri, Google Assistant, Alexa, and Cortana can help perform tasks, such as Internet searches, direction assistance, calendar integration, playing music, making calls or sending texts, and more.
Artificial Intelligence is no longer limited to sci-fi movies. In fact, it is expected to grow into a $190.6 billion market in the years to come!
Don’t get left behind—if you don’t have it yet, it is now the time to integrate AI technologies into your website and online marketing campaigns.
I am Louise Savoie Digital Marketer at Proweaver, a web development company specializing in Custom Web Design which helps sole proprietors and small companies increase their sales and grow their business. I am responsible in Content Marketing and Social Media Marketing. You can find us on Twitter: @proweaver