In memory of Bill Slawski, the generous and tireless Indiana Jones of Google Patents. Your contributions to the SEO community and the understanding of Google’s algorithms will never be forgotten.
Feb 06, 2023 will always be remembered as the date that marked a significant shift in the world of information retrieval technology. The spread of ML technologies and the integration of chatGPT, a conversational agent developed by OpenAI, into Microsoft’s Bing search engine, as well as into its other products, from GitHub to PowerBi, and soon Office365 suite, through a multibillion-dollar partnership, forced Google to take action and respond to this technological revolution. In response, Sundar Pichai, CEO of Google and Alphabet, published an article entitled “An important next step on our AI journey” announcing Google’s commitment to adapting to this changing landscape and advancing their own AI capabilities.
This timeline infographic, updated over time, is a testament to the evolution of Google from a mere lexical search engine to a semantic search engine, capable of understanding the natural language of both user queries and web pages. It charts the journey of Google as it progressed from a simple match between keywords to a deeper understanding of the context and intent behind the user’s search, filtered by their search habits and preferences.
With the advent of ML technologies, the timeline now serves as a historical record of Google’s journey towards becoming a leader in the field of information retrieval and a symbol of the technological revolution that is transforming the way we access and interact with information.
This transition is supported on the one hand by the adoption of some founding principles of Tim Berners-Lee’s vision known as the “Semantic web.” On the other hand, the increasing use of Machine Learning technologies.
If, as the late Bill Slawski, the Indiana Jones of Google’s patent, claims, the first “semantic” invention mentioned on a Google’s patent dates back to 1999 (read his article Google’s First Semantic Search Invention was Patented in 1999), it was in 2012 with the launch of Knowledge Graph (what we have indicated as one of the core principles of the Semantic Web) and later with the release of Rank Brain.
In this timeline, not only the major Core Updates will be included but also other core Google products based on ML and ranging from Translator to Google Photos, Lens, Google Assistant.
Finally, we’ll cover the release in the open-source world of those algorithms or frameworks for creating machine learning models that enable anyone who wants to exploit their potential.
We will also tell some suggestions from some new technologies announced to academia and the public but not yet implemented.
In 2015, Sundar Pichai, made strong statements about Google’s new course about the adoption of machine learning during the quarterly financial report on Alphabet, Google’s parent company.
“Machine learning is a core, transformative way by which we’re rethinking everything we’re doing,” he said.
“Our investments in machine learning and artificial intelligence are a priority for us,” “we’re thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. “We’re in the early days, but you’ll systematically see us think about how we can apply machine learning to all these areas.”
Somehow these statements mark the end of an era, the era of the “retrievals” and of that genius Amit Singhal who managed to bring these technologies to the significant milestones ever reached.
“Singhal was an acolyte of Gerald Salton, a legendary computer scientist. His pioneering work in document retrieval inspired Singhal to help revise the grad-student code of Brin and Page into something that could scale in the modern web era” (as told by Weird in an article titled “How Google is Remaking Itself as a Machine Learning First Company“).
One of the key figures in this transition, also responsible for the internal training of Google engineers in ML, was David Pablo Cohn, ex-Tech Lead for Google Labs.
The ultimate turning point came when machine learning became an integral part of Search, forever transforming the SERP, its flagship and most profitable product.
To some extent, Search has always relied on artificial intelligence. However, for many years, the company’s most precious algorithms, those that supplied the “ten blue links” in response to a search query, were thought too critical for ML’s learning algorithms.
“Because search is such a large part of the company, ranking is very, very highly evolved, and there was much skepticism you could move the needle very much,” says Giannandrea.
At the beginning of 2014, “We had a series of discussions with the ranking team,” says Jeff Dean of the Brain Team. “We said we should at least try this and see. Is there any gain to be had?” The experiment his team had in mind turned out to be central to Search: how well a document in the ranking matches a query (as measured by whether the user clicks on it). “We sort of just said, let’s try to compute this extra score from the neural net and see if that’s a useful score.”
It turned out the answer was yes, and the system is now part of Search, known as RankBrain (went online in April 2015).
“It was significant to the company that we were successful in making search better with machine learning,” says Giannandrea, another key figure in Google’s early transition to a machine learning-first company.
Gianandrea was the founder of Metaweb and Freebase (acquired by Google), the first bricks on which the Google Knowledge Graph would be created, and strongly supported, until 2018 when he moved to Apple, the spread of machine learning. Machine learning that we could define as “programs that generate programs” (or algorithms that generate models made from data and rules to generate new data) caused a somewhat disruptive impact software engineers used to complete control of what they made machines do through their code: a revolutionary transformation of the mindset went through Google.
It was precisely to support its engineers that Google’s Brain Team created TensorFlow, releasing it to the public in November 2015, and making even processors dedicated to running Machine Learning models, the Tensor Processing Units.
From then to now, the evolution has been exponential and the progressive introduction of MUM, announced at the Search On streaming event on 29 Sep 2021, marks a further shift towards multimodality.
During the event, Google unveiled a slew of new features that, taken together, represent the company’s most ambitious efforts yet to persuade users to do more than input a few phrases into a search box. The company intends to start a virtuous cycle using its new Multitask Unified Model (MUM) machine learning technology to deliver more depth and context-rich answers. In turn, users will ask – it hopes – more detailed and context-rich queries.
What Prabhakar Raghavan, Senior Vice President of Google, is seeing is a profound change not only in technology-driven by Google’s increasingly complex AI but also in ours search habits; and in the daily practices of us SEOs. The approach to content marketing and the investments of companies will also have to change. They will have to produce more and more quality multimedia content: part of a wider ecosystem able to cover extensively the topics on which companies want to establish their authority in the eyes of Google and their potential clients’.
We would like to sincerely thank some of the most relevant SEOs in the international community for contributing in various ways to the creation of this Timeline: Dawn Anderson, Jason Barnard, Kevin Indig, Cindy Krumm, Bill Slawsky, Andrea Volpini.
Together with them, we will accompany you in this journey that starts from the establishment of the Brain Team and follows the path of Google towards a Semantic, Multimodal and an AI-powered Search Engine.