Unravel the intricate relationship between search engines and machine learning. Discover the nine certainties that shape this dynamic, from algorithms to user experience, and how they're revolutionizing the way we navigate the digital world.
AI Types
Narrow or Weak AI: This type of AI is designed to perform specialized tasks that must be “taught” to the algorithm
- narrow AI (ANI) is able to quickly recognize patterns and perform tasks in a way that outpaces human ability
- General or Strong AI: Capable of autonomously learning and solving problems, general AI (AGI) takes machine learning to the next level
- AI is powered by deep learning processes designed to mirror the human brain’s neural networks, allowing the algorithm to make decisions without instruction
- Artificial superintelligence (ASI) still lands fully in the category of science fiction
- Currently, there are no clear-cut examples of AGI
Natural Language Processing
It’s important for a search engine to recognize how similar one piece of text is to another. This applies not just to the words being used but also their deeper meaning.
- Bidirectional Encoder Representations from Transformers (BERT) is a natural learning processing framework that Google uses to understand the context of a user’s search query.
Synonyms Identification
Google uses RankBrain to identify synonyms.
Query Clarification
By analyzing click patterns and the content type that users engage with, a search engine can leverage machine learning to determine the intent behind the user’s search
- This is changing how SEOs look at link structure and placement as Google’s algorithm uses tools like BERT to better evaluate the context of where those links are placed
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, significant impact (currently) on our SERPs.
- Google’s end goal is to use technology to provide users with a better experience.
- Don’t assume machine learning will soon take over all search ranking.
While machine learning isn’t perfect, it’s getting better
The more humans interact with it, the more accurate and “smarter” it will get
- 63% of respondents are hopeful for the future of humanity with AI
- Google and other search engines have revolutionized machine learning so that we are able to find the information and services we need, when we need it
Custom Signals Based on Specific Query
Google’s personalized search patent, US20050102282A1, states that: “…personalized search generates different search results to different users of the search engine based on their interests and past behavior.”
- Often used in conference presentations, proving this process is as simple as typing a string of queries into Google in one sitting and seeing the results change depending on what you last searched.
Ad Quality & Targeting Improvements
Google wants to provide the most relevant ads for its individual users
- Ad Rank can be influenced by a machine learning system
- The bid amount, your auction-time ad quality, the Ad Rank thresholds, and the context of the search all get fed into the system
Pattern Detection
Low-quality content typically has distinct similarities
- Lots of uses of stop words or synonyms
- The occurrence rate of identified “spammy” keywords
- Machine learning recognizes these patterns and flags them
- Even though Google still uses human quality raters, utilizing machine learning to detect these patterns drastically cuts down on the amount of manpower necessary to review the content
Identification of New Signals
RankBrain is a machine learning algorithm developed by Google that helps identify patterns in queries and also helps the search engine identify possible new ranking signals.
- As search engines are able to teach technology how to run predictions and data on their own, there can be less manual labor and employees can move toward other things machines can’t do, like innovation or human-centered projects.
