Google’s AI Evolution
April 2, 2025 Kelsey Way Digital Marketing

From Machine Learning to Google Gemini: Google’s AI Evolution

While artificial intelligence (AI) may feel like a recent explosion onto our technology landscape, for decades it’s played an integral role in shaping and powering search engine algorithms. Google’s AI development has been instrumental in shaping the way search engines understand and rank content. From early machine learning models to AI-driven algorithms like RankBrain and BERT, Google has continuously evolved its approach to deliver more relevant and people-first search results. As AI technology evolves, so does Google's ability to process natural language, predict user intent and enhance the user’s overall search experience. Our experts explore key milestones in Google's AI development, generative engine optimization (GEO) and how AI technology has transformed the search ecosystem over time.

Google's AI And Machine Learning Development

Throughout their history, Google has been at the forefront of AI and machine learning development to push the boundaries of search. Google's development of machine learning technology in this space has been driven by the goals of enhancing search accuracy, delivering quality user experience and developing automation. Pushing the boundaries of technology has been a core principle in Google's ethos, from their investment in machine learning to help correct spelling in the early 2000’s through the development of a powerful AI model Google Gemini in 2023. 

What Is Machine Learning Technology?

Machine learning is a division of AI that enables computers to learn from data and improve without explicit programming. While intertwined, AI and machine learning are used for different purposes. AI systems mimic human intelligence, whereas machine learning focuses on recognizing patterns and making data-driven predictions. Both are used to power Google's search systems.

What Is Machine Learning Used For?

Machine learning is used in a variety of ways, including search engine algorithms, fraud detection and speech recognition. For search engines, machine learning enhances algorithms like Google's RankBrain, improving search result relevance based on user behavior. 

Google's Technology Evolution

There are several chapters that tell the story of Google's history with machine learning and AI development. A timeline of key achievements helps us understand what pivotal events shaped the Google we know today.

  • 2001: Google began using machine learning to help search users correct their spelling
  • 2006: Google Translate becomes available, connecting the world through language
  • 2016: The AI program AlphaGo is the first AI system to defeat a human world champion in Go
  • 2017: Google Research launches a neural network architecture called the Transformer, helping Google understand language
  • 2019: BERT improves how search understands user intent
  • 2023: Google launches the generative AI system, Bard (now Google Gemini)
  • 2023: Google launches PaLM 2, an advanced AI system with improved coding, and reasoning functionality 

Powering Google's Search Engine Algorithms and Services: AI and Machine Learning Systems

Google utilizes a variety of AI systems to improve search accuracy, enhance user experience and promote automation across their various platforms. From RankBrain, which enhances search result relevance, to BERT and MUM, which refine language understanding, these AI models play a critical role in delivering high quality AI-powered search results. Beyond search, AI powers tools like Google Lens, Google Assistant and spam detection in Gmail. We will further explore the key AI systems that Google currently uses and how they shape its ecosystem.

RankBrain (2015)

RankBrain is the company’s first machine learning-based algorithm that improves search results dynamically and is fundamental to how Google understands and relates words to concepts. It helps Google interpret and process unfamiliar search terms more effectively by moving beyond traditional keyword matching to considering the overall meaning of a search rather than the individual words. In short, RankBrain changed how Google understands and ranks a search by introducing machine learning, contextual awareness and adaptive ranking. Additionally, RankBrain set the stage for more advanced AI-powered search improvements like BERT and MUM.

Neural Matching (2018)

Neural Matching is an AI system that helps Google better understand webpage content. It connects the dots between a user's search and helpful information on webpages. This system drove Google Search beyond simple keyword matching and toward a deeper understanding of concepts, relationships and intent.

Bidirectional Encoder Representations from Transformers (BERT)(2019)

Fundamental to how Google Search works today, BERT is an AI system that understands how combinations of words can have different meanings and intent. This system dramatically improved Google's ability to understand natural language, leading to improved search accuracy and the powering of other Google products such as Google Assistant and Google Translate. 

Passage Ranking System (2020)

Passage Ranking System helps Google identify sections of web pages that relate to a user's search to understand if that webpage is relevant enough to display in the SERP. This system made Google Search smarter by ensuring that deeply buried, but highly relevant information within a page could rank, improving search accuracy for long and detailed searches. 

Multitask Unified Model (MUM) (2021)

This advanced AI system is used to understand and generate language. MUM is currently used for very specific advanced applications like improving featured snippet callouts that Google displays.

MUM is an evolution of BERT as it can generate, understand and transform information in a much deeper way. MUM can take insights from one language and apply them to answer questions in a different language. This helps Google provide more nuanced answers to search terms. With MUM, Google moved from a keyword-based search engine algorithm to an AI-driven knowledge engine that understands meaning, context and intent at a much higher level.

SpamBrain (2021)

SpamBrain is an advanced, machine learning-based approach to detecting and combating spam across Google Search results. This system learns and adapts to new spam tactics and helps to make results more relevant and trustworthy.

Gemini (2023)

Google's most advanced AI model, Gemini, formerly known as BARD, was developed by Google Deepmind and is a key AI development in the technology landscape. Google Gemini can understand and generate text, images, audio, video and code. Its release was a major milestone in AI and pushed the boundaries of reasoning, problem-solving and real world understanding in AI systems. 

These are just a select few of the systems Google uses within its technology ecosystem. While AI has a major impact on Google’s search algorithm, there are other systems at play.  It is clear that Google evolves and invests in technology’s future. Our experts break down a full timeline of Google’s search algorithm changes.

These are just a few of the many AI-driven systems that power Google’s technology ecosystem. A comprehensive breakdown of Google's search algorithm evolution reveals that while AI plays an impactful role in shaping Google’s search, it works alongside other advanced technologies to enhance accuracy, relevance and user experience. Google’s commitment to innovation and continuous investment in AI-driven technology underscore its role in shaping the future of search. 

Investing In The Future With AI First, A Google for Startups Accelerator

Not only has Google had a hand in the development of AI as it is today but they are also supporting future generations with the Google for Startups Accelerator program. This program brings mentorship and technical project support from Google staff to early-stage startups who use technology to tackle challenges around the world.

Since the introduction of generative engine optimization, our experts at Blue Compass have been diligently researching and refining best practices to implement for our clients. Our approach combines the latest generative engine optimization strategies with traditional SEO principles, ensuring your content remains highly visible across multiple digital platforms. With this approach businesses can future-proof their digital presence and stay competitive in an ever-changing online landscape.

Generative Engine Optimization (GEO): Optimizing For AI-Powered Search

As AI continues to evolve and shape the digital ecosystem, ensuring your content is accessible across platforms—from organic search to AI-powered experiences—is essential for marketing success. Generative engine optimization, also known as generative AI optimization (GAIO), enhances your content’s reach and visibility within AI-generated search results. If your goal as a marketer is to maximize digital visibility, drive engagement and stay ahead of AI-driven search trends, generative engine optimization should be a key part of your strategy.

Since the introduction of generative engine optimization, our experts at Blue Compass have been diligently researching and refining best practices to implement for our clients. Our approach combines the latest generative engine optimization strategies with traditional SEO principles, ensuring your content remains highly visible across multiple digital platforms. With this approach businesses can future-proof their digital presence and stay competitive in an ever-changing online landscape.

Staying Ahead in AI-Powered Search: How Blue Compass Optimizes for the Future of SEO

Google’s history of AI and machine learning development has reshaped how search engines understand, process and deliver information. As AI-powered search and generative models like Google Gemini become more integrated into our lives, businesses and marketers must adapt. Ensuring visibility across traditional and AI-powered search platforms is critical in the current digital realm. Looking ahead, Google’s commitment to AI innovation, including initiatives like the Google for Startups Accelerator program, reinforces its role in shaping the future of technology.

Blue Compass is here to help you navigate the ever-evolving digital environment. Our experts are at the forefront of generative engine optimization best practices, blending traditional SEO strategies with AI-driven optimizations to maximize your digital visibility. We can help ensure your content is optimized for AI-generated search results to future-proof your online presence and help you stay ahead of the competition. Get in touch with us to adapt, thrive and lead in the AI-powered search landscape.