The Effect Artificial Intelligence Has Had On Search

This is a guest post written by Sydney Tierney.

Artificial intelligence (AI) has been a trending topic over the last few years. With those waiting anxiously to see how the future is planned out, and others riding the wave of excitement, trying to get their hands on the developing process.

However, there has been a huge amount of speculation on the development of AI and deep learning; and how it is going to affect the behavior of search engines in the future. In the past, the way in which search performed was rather unsophisticated. Yet now, the algorithms have evolved to aid those who use search and work alongside it within a business.

Although, many may not know how much AI in search has changed the way it works, and how it can benefit businesses all over, aiding in their productivity rates.

So, in order to ensure everyone is entirely up to date on this development effort, we will be discussing just how artificial intelligence has affected search…And why the change has created so many benefits for those who use it.

What Is AI and Machine Learning?

To start, artificial intelligence, alongside machine learning, are the aspects that will change the way in which search works.

Machine learning is a process in which humans will give the computer a string of data to analyze giving the result it needs to reach after it. This way, the machine will take time to work out the best possible process to take, to have the most successful outcome to offer the human. And through this, the machine can either work alongside a real-life source (a person) or by itself.

These are the two components that have worked together in order to offer the development of search and has affected it greatly.

What Is AI Search?

So, now is the best time to discuss just how these components have developed AI in search and the extensive future that it may reach. With technology moving so fast, it is only the knowledge of the companies themselves holding the intelligence back from working to its fullest potential.

We all use search tools, almost every day. Google experiences 40,000 searches every second.

From a search, we expect to find results in web pages that offer us the information we need, ready to consume. Yet in some cases, we may find ourselves extending the search to page two or three of the Google platform, which realistically is inefficient. From here, one may decide to change their search or click onto a website that was no help at all, and bounceback to somewhere different.

These kinds of actions are what causes the search issues. While the evidence possessed that suggests the algorithm needs to be changed, in order to make the task easier for those who use it.

So, how has it reached features within the search?

The Traditional Google Search

One of the biggest changes in search is the way in which it has improved when giving its’ results to the users. To focus on Google in particular; the engineers have been working constantly to develop a particular algorithm for search, to offer users the best results it possibly can.

The main issue faced with this process was the scale in which the engineers had to work. For example, there is a suggested 60 trillion web pages that make up the layers of Google search results. These had to be worked into a format that would allow each to be suggested when called for with efficiency and relevance.

Because of the clear extensiveness of this task, machine learning quickly came into play in order to aid the engineers in completing this. The intelligence works to analyze Google search trends to test whether the customer has received a resourceful result. If they hadn’t, then machine learning works to better the search results produced from the keywords placed into the search. And, if the results were positive, they would stay the same.

The continuous testing and adapting to the results provided has allowed machine learning to offer real and relevant resources to the users. Therefore making the Google search process much more satisfactory.

Image Search

In the past, those who use images within their Google webpages would have to build them in a specific way. Then, Google can see them. This process was called optimizing the images. Those who work within SEO would add keyword filtered names and alt text labels, in order to make them visible.

However, with the development of machine learning, Google can now see the images without all of the professional set-ups. With the development of AI in search algorithms, computers are quickly learning how they can analyze a page with images to the point where they are made visible. The features taken into consideration are the shapes, color scheme and even gradient of the image.

Although, within the development of AI in search, this has been one of the hardest tasks for engineers to overcome. It began with a set of training data being given to the machine learning systems. These were asked to analyze and identify where the ‘cats’ in the image were shown. It began by producing wrong answers, with nothing being correct for a long time.

So instead, the machine decided to focus on reduction. Anything that was wrong was stored in memory, abling the machine to know where to go next. This led to the point of it producing the right answer, and confidently being able to show where a cat is in a picture.

By developing this, it has made image search possible again. With Google having a far better understanding of the photos that make up a webpage, allowing them to be searched for with success.

Google Translate

One stereotype labelled against Google translate was its inability to provide the user with correct answers. Those who used it to cheat on homework or tests were quickly found out. This is because the results given were grammatically incorrect, had confusing context, or really just translated something entirely irrelevant.

Because of this, it was imperative that the engineers developed the machine learning to be more refined and produce completely correct answers. This was with the hopes that people would begin using it consistently again.

By utilizing the machine learning; Google translate now has the ability to analyze what is being asked, and can translate it with complete coherence. This way, the context that would be used within that native language; rather than just translating a sentence word for word when it likely won’t make sense.

The natural translation has allowed Google search to be used widely again, with those knowing they will receive correct terminology and appropriate grammar.

Google Paid Search

The next amazing innovation that has been created after the development of machine learning is working with Google Adwords.

It is being used to analyze a wide amount of data taken from those who use Google search. This is done in order to offer them adverts that match the sort of keywords they are searching. This will then will benefit both the user and the company advertising.

By doing this, there is a clear increase in those who are purchasing items, due to the adverts being tailored to the products they are looking for. This aids in the productivity of a company and helping them grow in popularity.

The power of machine learning in search helps to better analyze the user’s purchase intent. By focusing on the final point a user will hit just before they buy something; the machine can develop the release of adverts at this point. This will work to persuade the user further into buying said product, or those that will be interesting to them. This allows a company to receive many benefits from a simple Google ad, especially now with the further intellect of Google aiding in its popularity.

Voice Search

Voice search has been an element developed that has exploded within machine learning and artificial intelligence within search. From starting on phones offering the ‘Siri’ assistants to any iPhone user. To now developing devices such as; ‘Alexa’ or ‘Google Home’ that aid those within the comfort of their living room; to complete tasks from booking a taxi to finding out a chicken stock recipe.

Despite this, it didn’t all begin with this simplicity. The voice automated search began with a rather disappointing makeup. It didn’t respond to questions with relevant answers, but simple confusion, giving the user only the feeling of frustration.

The issue stemmed from the voice search not being able to configure various accents. This showed the engineers that a change needs to be made. Then it can get to a wider audience using these life-changing devices.

From here, the voice search algorithm had to be constantly updated and trained. This was until the best set up was found in order for it to work most satisfactory for the users, and to better understand voice searches. This was all completed by those at Google speech team. They were able to adjust the machine learning to produce the brilliantly intellectual devices we use every day; to make calls, set appointments and buy products.

Summary

The process of artificial intelligence in search is being quickly developed every day. With engineers working countless hours to increase the development of machine learning; there is a sure future to search being benefitted even further; alongside other elements on the online community.

With the AI search results, translate, images and adverts already changing with the development of machine learning; there is no way to now much further this intelligence can go. Focusing changes within Google search was one of the best ideas, due to it being a platform used every day by a variety of people, all looking for something different.

Not only does this make the work for the engineers harder, but it also makes it far more worth it. This is because it is benefitting a large number of people to receive relevant and satisfactory search results. Then, increasing those who use the extension platforms more and more, such as translate, imagery and adverts.

Author Bio:

 

Sydney Tierney just recently finished her studies and is working her way into the world of content writing as a digital marketing assistant. 

 

 

What are your thoughts on artificial intelligence and search?