“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the Web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.”
—Larry Page, Google Founder, October 2000
“Google will fulfil its mission only when its search engine is AI-complete. Do you guys know what that means? That’s artificial intelligence.” —Larry Page, Google Founder, May 2002
In October 2015, Google took a significant step toward this vision when it introduced RankBrain, a machine-learning-based system designed to better understand and process complex search queries. RankBrain marked the beginning of AI being deeply integrated into Google’s search algorithm. At that time, RankBrain handled approximately 15% of Google’s daily search queries, particularly focusing on interpreting ambiguous or previously unseen queries. While Google had already used algorithms to sort and rank information, the introduction of machine learning enabled it to go beyond predefined rules and adapt based on patterns in data.
RankBrain is powered by machine learning, a subset of artificial intelligence (AI) where computers can learn and improve from experience without being explicitly programmed. Rather than relying on human-defined rules, RankBrain identifies patterns and learns from the vast volumes of data it processes. This allows it to predict and understand searcher intent, especially when the queries are complex, vague, or unfamiliar.
When Google first revealed RankBrain’s existence, it was already the third most important signal in determining search rankings, and its influence has only grown over time. The ability of RankBrain to refine itself over time, by continuously learning from the queries it processes, means that the search engine is constantly improving its understanding of human language and search behavior.
While RankBrain was a major leap in Google’s AI journey, the last few years have seen even more transformative developments. In 2019, Google introduced BERT (Bidirectional Encoder Representations from Transformers), a neural network-based technique for natural language processing (NLP). Unlike RankBrain, which focused on learning patterns from queries, BERT can understand the context of words in a search query by looking at surrounding words, effectively understanding natural language in a way that mimics human comprehension. It processes search queries in a much more nuanced way, making results more relevant for users.
Building on the same architecture as BERT, Google has now deployed MUM (Multitask Unified Model), introduced in 2021. MUM represents a leap forward in how AI can process not just text, but also images, videos, and even different languages. It’s capable of answering more complex questions by synthesizing information across multiple forms of media and languages, taking Google’s understanding of searcher intent to unprecedented levels.
In parallel, the rise of large language models (LLMs) such as GPT-4, developed by OpenAI, has further revolutionized how AI understands and generates human-like text. These models are trained on massive datasets and have the ability to generate coherent, contextually appropriate text, which is influencing search, recommendation systems, and content creation. GPT-4 and similar models are now being used in search-related applications, where AI-powered chatbots and assistants can provide direct answers to complex questions rather than simply listing web pages.
While RankBrain laid the groundwork for today’s AI-driven search engines, the introduction of BERT, MUM, and LLMs like GPT-4 has redefined what search engines can do. These systems can now handle conversational search queries, provide deeper insights, and even predict what users want with increasing accuracy. As AI systems continue to evolve, businesses need to adapt their SEO strategies to align with these advancements.
Here are a few key considerations for businesses in 2024:
AI-powered algorithms are evolving at a rapid pace. Google’s efforts to achieve Larry Page’s original vision of an “AI-complete” search engine are moving forward with systems like RankBrain, BERT, and MUM, but these are just the beginning. Future AI innovations could offer even deeper personalization, predictive search results, and more accurate representations of user intent. As large language models continue to develop, we may see even more intuitive and human-like interactions in search experiences, transforming the way people access and engage with information on the web.
While the singularity or AI’s total mastery of human thought might still be far off, AI’s grip on search and other daily technologies is already profound. For businesses and users alike, staying informed and adapting to these AI shifts is essential for thriving in this ever-evolving digital landscape.
By Matrix Internet
By Aoife O'Driscoll
By Irene Hislop