Transforming Digital Search Using Artificial Intelligence

The web is a vast storehouse of knowledge and with people becoming more and more internet savvy, search engines have become an integral part of our lives. Companies like Google and Microsoft are actively investing in their respective search engines to keep up with the growing demand for information. However the endeavor does not end there. Search engine providers have to improve the search algorithms continuously, add new features and look into further innovations. Over the years, searching on Google, Bing or even Facebook has seen a drastic improvement, not only in terms of the results they deliver, but also with the accuracy of these results.

Nowadays, we are quite prone to hearing the phrase “Google it”. In fact, Google has monopolized digital search with its robust technology and the ability to deliver accurate results. This is not solely based on the search algorithms and website crawlers, but goes a lot deeper. Using artificial intelligence, the engines can now better adapt to human behavior and predict more tailored search results. Artificial intelligence is changing the way we browse the web, and it is improving faster than we can imagine.

What is Artificial Intelligence?
We all know how the human brain works. It is in a state of continuous development and learning using our senses. AI or artificial intelligence works in a similar way and it is built to mimic the human brain and its behavior. Ever wondered how Facebook can tag your group photos automatically, or how Google search offers suggestions when you search? Artificial Intelligence ingests an unimaginable amount of data using a system of deep neural networks and learns to do more complicated tasks using the data.

There are three main types of AI based on the caliber:

• Artificial Narrow Intelligence (ANI) – This is the more prevalent category of AI which power most of our daily activities on the internet. It typically specializes in a unidirectional mode of activity, like a search or a simple conversation, or maybe a game of chess. Best examples of this kind would be Siri or Google Now, the proprietary voice assistants from Apple and Google respectively. These cannot function by themselves usually and need to be triggered by human action.
• Artificial General Intelligence (AGI) – It is much more advanced than ANI as it involves complex learning and tasking abilities like reasoning, planning and solving complicated problems. AGI is generally at par with human level thinking and functioning and it is something which is yet to be developed in real world.
• Artificial Superintelligence (ASI) – This is more like a theoretical concept as development in this field is far-fetched. Technically, it should be much smarter than regular human intellect. ASI is capable of adapting to scientific creativity, social skills and general wisdom.

Deep Machine Learning and Its Utilization
This method takes advantage of the neural networks powering most of the internet these days. Using a massive network of complex software and hardware, neural nets analyze large chunks of raw data from various sources. They thereby improve in performing tasks like identifying photographs, responding to voice generated search queries and carrying out commands spoken into a smartphone.

In the past few years, this process of learning has changed the way humans interact with the internet and different internet connected devices. From Google and Apple to even social media juggernauts such as Facebook, Twitter and Skype have implemented this technology to generate user specific results and make search more intuitive.

Google and RankBrain
In recent times, Google has rolled out its own neural network system called RankBrain, which has had a massive impact on search results and SEO. Millions of search queries pass through the Google search engine every second and RankBrain reportedly feeds on about 15% of these queries to accurately predict the rank of websites on SERPs. The use of this kind of neural network system comes with a number of advantages and disadvantages.

Advantages:

• Most data scientists and experts have identified neural network to be quite complicated for general understanding and tweaking, which makes it a reliable way to get authentic results.
• Networks like RankBrain provide 10% more accurate predictions than manual ones.
• Since neural nets feed on raw data, they can provide much more accurate feedback based on individual search queries and data.
• These networks can learn rapidly and properly if fed with enough data about something.

Disadvantages:

• Most experts are not very fluent with how this technology works; people simply know that it does, and it does exceptionally well.
• It can be difficult to tweak the functionality of RankBrain to change priorities.

It is still unclear what the implementation of RankBrain means for Google search in the long run. With engineers failing to fathom the core of its success, it is, however, a much more reliable way to rank websites. When a person searches for football, the search results directly shows you images and news related to football and not cricket. While the importance of SEO cannot be undermined in providing tailored search results, a large part of this articulation is also based on RankBrain.

Personalized and Contextual Search
Most search engines are built and improved in a way such that they understand the user and his requirements. For example, if a person looks for a topic on the internet from his smartphone, Google can easily understand that the query was generated from a mobile device and it accordingly returns results optimized for that particular device. With voice search being extremely prevalent and more companies pushing it actively, assistants like Google Now and Siri even try to use the location of the person to provide extremely personalized results.

On the contrary, if a person searches for, say, Cristiano Ronaldo, the search engine can use the context of the search to suggest names of other players which the user might additionally be interested in. This technology is evolving gradually and with neural nets built right into search engines, users can now get more information than ever before.

The concept of search is not only limited to search engines like Google, Bing or Yahoo. Ecommerce sites like Amazon, Ebay and Flipkart also have contextual and personalized search built in. Even Facebook understands who might be present in your pictures, what you might be watching, eating or feeling like. In a world where people are taking in large amount of information, we might not even be aware how much Artificial Intelligence is dominating our lives. Be it a game of chess where you are playing against a predetermined ‘Player 1’, or a currency conversion you want to know about, there is always a second brain hovering above us.