How Search Engines Use Machine Learning: 9 Things We Know for Sure

How Search Engines Use Machine Learning: 9 Things We Know for Sure

Tech giants are rising in the emerging wave of artificial intelligence and machine learning, truly making them the most. For example, Facebook, Google, Netflix, Amazon, Apple, and NASA. Machine learning forms the core of all their strategic decisions.

According to experts, Google is one of the most advanced tech giants. Fundamentally, Google’s core search business is based on machine learning and predictive analysis to deliver search results which are monetized through advertising.

Microsoft offers a large portfolio of tools and platforms on Windows, Visual Studio, and Azure for developers. In the machine learning space, Microsoft’s most significant acquisition was Maluuba, which is famous for offering natural language understanding technology.

That huge influx of capital means that AI computing power is making rapid advancements in a range of sectors, from healthcare to construction to marketing and search engine optimization.

In 2019, Microsoft invested in 11 artificial intelligence (AI) startups, with $1 billion for OpenAI alone. And they aren’t even the biggest source of corporate venture capital flooding into AI startups. In that same year, Intel Capital made 19 investments, and Google Ventures made 16 investments.

AI: Types of AI

        1.Narrow or Weak AI is used for performing specialized tasks that must be ‘taught’ to the algorithm (think Google search algorithms).

        2.General or Strong AI is capable of autonomously learn and solving problems, which takes it to the next level. It is powered by deep learning processes designed to mimic the human brain’s neural networks, enabling decisions to be made without explicit instruction.

        3.Artificial Superintelligence lands fully in the category of science fiction, as this type of AI would, theoretically be capable of outperforming human capabilities to solve the ‘unsolvable’ problems.

Within AI, two primary types stand out: Artificial Narrow Intelligence (ANI) and Artificial General Intelligence (AGI). These two branches represent different levels of AI capability, with ANI being prevalent today and AGI being a future goal.

AGI is a theoretical representation of a complete AI that solves complex tasks with generalized human cognitive abilities. The systems can learn to handle unfamilier tasks without additional training before they can handle related tasks within the same domain. While ANI is designed to perform specific tasks, already integrated into daily lives and powers many of the tools and services.

To progress from ANI to AGI, deep learning will be the key to creating stronger AI capable of using deductive reasoning to analyze complex, unstructured data and make independent decisions.

Google has been making steady progress in the way it connects users to the content they’re searching for, including these nine ways we know search engines are using machine learning right now.

• Pattern Detection

Use of a search engine for pattern detection helps in identifying spam or duplicate content -

• Presence of several outbound links to unrelated pages
• Lots of uses of stop words or synonyms
• Occurrence rate of identified ‘spammy’ keywords

Google still uses human quality raters, using machine learning for detecting these patterns drastically and cutting down the amount of manpower necessary to review the content. In this way, Google can automatically sift through pages to weed out low-quality content before an actual human has to get involved.

• Identification of New Signals

RankBrain refers to a machine learning algorithm developed by Google for identifying patterns in queries and also helps in identifying possible ranking signals.

RankBrain is an AI and machine learning based component of Google’s core search algorithm that helps Google interpret and process search queries for delivering more relevant results. Its central purpose of to understand the intent behind search queries, particularly those using new, rare or conversational phrasing.

RankBrain quickly became one of Google’s most influential ranking signals. Alongside content quality and backlinks, it plays a crucial role in determining which pages appear at the top of search results. Its influence is strongest for novel, complex or unusual searches, but it also supports general intent matching and content relevance.

• It’s Weighted as a Small Portion

Even though machine learning is slowly transforming the way search engines find and rank websites, it doesn’t mean it has a major impact on SERPs.

Google’s ultimate goal is to use technology to provide users with a better experience, but they don’t want to automate the entire process.

Don’t assume machine learning will soon take over all search ranking; rather, it is simply a small piece of the puzzle search engines have implemented to hopefully make our lives easier.

• Custom Signals Based on Specific Query

Google's search engine is currently creating personalized search results based on users’ behaviour.

As per Google’s personalized search patent, US20050102282A1, states that “personalized search generates different results to different users of the search engines based on their interests and past behaviour.”

Search history is just one component of the search experience that machine learning uses to provide better results.

• Natural Language Processing

Natural language processing is a field that combines computer science, AI and language studies, helping computers in understanding, processing, and creating human language in a way that makes sense and is useful.

With the growing amount of text data from social media, websites and other sources, NLP is becoming a key tool to gain insights and automate tasks like analyzing text or translating languages.

BERT is designed to replicate human recognition as closely as possible to decode those contextual nuances by learning how users interact with the content and matching search queries with more relevant results.

With the increasing development and transforming language, machines can predict our meanings behind the words we say and provide better information.

• Image Search to Understand Photos

Every second, approximately 1087 photos are uploaded to Instagram, and 4000 are uploaded to Facebook.

Machine learning analyzes color and shape patterns and pairs them with any existing schema data about the photograph to help the search engine understand what an image actually is.

Google not only catalog images but also powers its reverse image search, enabling users to search using an image instead of a text query.

• Ad Quality & Targeting Improvements

Google provide the most relevant ads for its individual users.

As per Google U.S. patents US20070156887 and US9773256 on ad quality, machine learning can be used for improving an “otherwise weak statistical model”, which means that the Ad Rank can be influenced by a machine learning system.

• Synonyms Identification

Search results not including the keyword in the snippet, likely due to Google using RankBrain for identifying synonyms.

When searching for [forest preservation], you’ll see various results with the word “protection” as it can be used interchangeably with “preservation” in this case.

Summary

As more people interact with machine learning, the more accurate and smarter it will get, and machine learning isn’t perfect.

In 2018, Pew Research conducted a poll in which 63% of respondents said that they are hopeful for the future of humanity as it relates to AI, agreeing that by 2030, humans will be better off with the help of artificial intelligence.