Disclaimer: Much of what you're about to read is based on personal opinion. A thorough reflection about RankBrain, to be sure, but still personal — it doesn't claim to be correct, and certainly not "definitive," but has the aim to make you ponder the evolution of Google.
Introduction
Whenever Google announces something as important as a new algorithm, I always try to hold off on writing about it immediately, to let the dust settle, digest the news and the posts that talk about it, investigate, and then, finally, draw conclusions.
I did so in the case of Hummingbird. I do it now for RankBrain.
In the case of RankBrain, this is even more correct, because — let’s be honest — we know next to nothing about how RankBrain works. The only things that Google has said publicly are in the video Bloomberg published and the few things unnamed Googlers told Danny Sullivan for his article, FAQ: All About The New Google RankBrain Algorithm.
Dissecting the sources
As I said before, the only direct source we have is the video interview published on Bloomberg.
So, let's dissect what Jack Clark, reporter of the Bloomberg said in that video and what Greg Corrado — senior research scientist at Google and one of the founding members and co-technical lead of Google's large-scale deep neural networks project —came others said to Clark.
RankBrain is already worldwide.
I wanted to say this first: If you're wondering whether or not RankBrain is already affecting the SERPs in your country, now you know — it is.
RankBrain is Artificial Intelligence.
Does this mean that RankBrain is our first evidence of Google as the Star Trek computer? No, it does not.
It's true that many Googlers — like Peter Norvig, Corinna Cortes, Mehryar Mohri, Yoram Singer, Thomas Dean, Jeff Dean and many others — have been investigating and working on machine/deep learning and AI for a number of years (since 2001, as you can see when scrolling down this page). It's equally true that much of the Google work on language, speech, translation, and visual processing relies on machine learning and AI. However, we should consider the topic of ANI (Artificial Narrow Intelligence), which Tim Urban of Wait But Why describes as: "Machine intelligence that equals or exceeds human intelligence or efficiency at a specific thing."
Considering how Google is still buggy, we could have some fun and call it HANI (Hopefully Artificial Narrow Intelligence).
All jokes aside, Google clearly intends for its search engine to be an ANI in the (near) future.
RankBrain is a learning system.
With the term "learning system," Greg Corrado surely means "machine learning system."
Machine learning is not new to Google. We SEOs discovered how Google uses machine learning when Panda rolled out in 2011.
Panda, in fact, is a machine learning-based algorithm able to learn through iterations what a "quality website" is — or isn't.
In order to train itself, it needs a dataset and yes/no factors. The result is an algorithm that is eventually able to achieve its objective.
Iterations, then, are meant to provide the machine with a constant learning process, in order to refine and optimize the algorithm.
Hundreds of people are working on it, and on building computers that can think by themselves.
Uhhhh... (Sorry, I couldn't resist.)
RankBrain is a machine learning system, but — from what Greg Corrado said in the video — we can infer that in the future, it will probably be a deep learning one.
We do not know when this transition will happen (if ever), but assuming it does, then RankBrain won't need any input — it will only need a dataset, over which it will apply its learning process in order to generate and then refine its algorithm.
Rand Fishkin visualized in a very simple but correct way what a deep learning process is:
It interprets languages and interprets queries.
Paraphrasing the Bloomberg interview, Greg Corrado gave this information about how RankBrain works:It works when people make ambiguous searches or use colloquial terms, trying to solve a classic breakdown computers have because they don’t understand those queries or never saw them before.
We can consider RankBrain to be the first 100% post-Hummingbird algorithm developed by Google.
Even if we had some new algorithms rolling out after the Hummingbird release (e.g. Quality Update), those were based on pre-Hummingbird algos and/or were serving a very different phase of search (the Filter/Clustering and Ranking ones, specifically).
Credit: Enrico Altavilla
RankBrain seems to be a needed "patch" to the general Hummingbird update. In fact, we should remember that Hummingbird itself was meant to help Google understand “verbose queries.”
However, as Danny Sullivan wrote in the above mentioned FAQ article at Search Engine Land, RankBrain is not a sort of Hummingbird v.2, but rather a new algorithm that "optimizes" the Hummingbird work.
If you look at the image above while reading Greg Corrado's words, we can say with a high degree of correctness that RankBrain acts in between the "Understanding" and the "Retrieving" phases of the overall search process.
Evidently, the too-ambiguous queries and the ones based on colloquialisms were too hard for Hummingbird to understand — so much so, in fact, that Google needed to create RankBrain.
RankBrain, like Hummingbird, generalizes and rewrites those kinds of queries, trying to match the intent behind them.
In order to understand a never-before-seen or unclear query, RankBrain uses vectors, which are — to quote the Bloomberg article — "vast amounts of written language embedded into mathematical entities," and it tries to see if those vectors may have a meaning in relation to the query it's trying to answer.
Vectors, though, don't seem to be a completely new feature in the general Hummingbird algorithm. We have evidence of a very similar thing in 2013 via Matt Cutts himself, as you can see from the Twitter conversation below:
At that time, Google was still a ways from being perfect.
Upon discovering web documents that may answer the query, RankBrain retrieves them and lets them proceed, following the steps of the search phase until those documents are presented in a visible SERP.
It is within this context that we must accept the definition of RankBrain as a "ranking factor," because in regards to the specific set of queries treated by RankBrain, this is substantially the truth.
In other words, the more RankBrain considers a web document to be a potentially correct answer to an unknown or not understandable query, the higher that document will rank in the corresponding SERP — while still taking into account the other applicable ranking factors.
Of course, it will be the choice of the searcher that ultimately informs Google as to what the answer to that unclear or unknown query is.
As a final note, necessary in order to head off the claims I saw when Hummingbird rolled out: No, your site did not lose visibility because of a mysterious RankBrain penalty.
Dismantling the RankBrain gears
Kristine Schachinger, a wonderful SEO geek whom I hold in deep esteem, relates RankBrain to Knowledge Graph and Entity Search in this article on Search Engine Land. However — while I'm in agreement that RankBrain is a patch of Hummingbird and that Hummingbird is not yet the "semantic search" Google announced — our opinions do differ on a few points.
I do not consider Hummingbird and Knowledge Graph to be the same thing. They surely share the same mission (moving from strings to things), and Hummingbird uses some of the technology behind Knowledge Graph, but still — they are two separate things.
This is, IMHO, a common misunderstanding SEOs have. So much so, in fact, that I even tend to not consider the Featured Snippets (aka the answers boxes) part of Knowledge Graph itself, as is commonly believed.
Therefore, if Hummingbird is not the same as Knowledge Graph, then we should think of entities not only as named entities (people, concepts like "love," planets, landmarks, brands), but also as search entities, which are quite different altogether.
Search entities, as described by Bill Slawski, are as follows:
- A query a searcher submits
- Documents responsive to the query
- The search session during which the searcher submits the query
- The time at which the query is submitted
- Advertisements presented in response to the query
- Anchor text in a link in a document
- The domain associated with a document
The relationships between these search entities can create a "probability score," which may determine if a web document is shown in a determined SERP or not.
We cannot exclude the fact that RankBrain utilizes search entities in order to find the most probable and correct answers to a never-before-seen query, then uses the probability score as a qualitative metric in order to offer reasonable, substantive SERPs to the querying user.
The biggest advancement with RankBrain, though, is in how it deals with the quantity of content it analyzes in order to create the vectors. It seems bigger than the classic "link anchor text and surrounding text" that we always considered when discussing, for instance, how the Link Graph works.
There is a patent filed by Google that cites one of the AI experts cited by Greg Corrado — Thomas Strohmann — as an author.
In that patent, very well explained (again) by Bill Slawski in this post on Gofishdigital.com, is described a process through which Google can discover potential meanings for non-understandable queries.
In the patent, huge importance is attributed to context and "concepts," and the fact that RankBrain uses vectors (again, "vast amounts of written language embedded into mathematical entities"). This is likely because those vectors are needed to secure a higher probability of understanding context and detecting already-known concepts, thus resulting in a higher probability of positively matching those unknown concepts it's trying to understand in the query.
Speculating about RankBrain
As the section title says, now I enter in the most speculative part of this post.
What I wrote before, though it may also be considered speculation, has the distinct possibility of being true. What I am going to write now may or may not be true, so please, take it with a grain of salt.
DeepMind and Google Search
In 2014, Google acquired a company specialized in learning systems called DeepMind. I cannot help but consider that some of its technology and the evolutions of its technologies are used by Google for improving its search algorithm — hence the machine learning process of RankBrain.
In this article published last June on technologyreview.com, it's explained in detail how not having a correctly-formatted database is the biggest obstacle for a correct machine and deep learning process. Without it, the neural computing (which is behind machine and deep learning) cannot work.
In the case of language, then, having "vast amounts of written language" is not enough if there's no context, especially if not using n-grams within the search so the machine can understand it.
However, Karl Moritz Hermann and some of his DeepMind colleagues described in this paper how they were able to discover the kind of annotations they were looking for in classic "news highlights," which are independent from the main news body.
Allow me to quote the Technology Review article in explaining their experiment:
Hermann and co anonymize the dataset by replacing the actors in sentences with a generic description. An example of some original text from the Daily Mail is this: "The BBC producer allegedly struck by Jeremy Clarkson will not press charges against the "Top Gear" host, his lawyer said Friday. Clarkson, who hosted one of the most-watched television shows in the world, was dropped by the BBC Wednesday after an internal investigation by the British broadcaster found he had subjected producer Oisin Tymon “to an unprovoked physical and verbal attack.”
An anonymized version of this text would be the following:
The ent381 producer allegedly struck by ent212 will not press charges against the “ent153” host, his lawyer said friday. ent212, who hosted one of the most - watched television shows in the world, was dropped by the ent381 wednesday after an internal investigation by the ent180 broadcaster found he had subjected producer ent193 "to an unprovoked physical and verbal attack."
In this way it is possible to convert the following Cloze-type query to identify X from “Producer X will not press charges against Jeremy Clarkson, his lawyer says” to “Producer X will not press charges against ent212, his lawyer says.”
And the required answer changes from “Oisin Tymon” to “ent212."
In that way, the anonymized actor is only possible to identify with some kind of understanding of the grammatical links and causal relationships between the entities in the story.
Using the Daily Mail, Hermann was able to provide a large, useful dataset to the DeepMind deep learning machine, and thus train it. After the training, the computer was able to correctly answer up to 60% of the questions asked.
Not a great percentage, we might be thinking. Besides, not all documents on the web are presented with the kind of highlights the Daily Mail or CNN sites have.
However, let me speculate: What are the search index and the Knowledge Graph if not a giant, annotated database? Would it be possible for Google to train its neural machine learning computing systems using the same technology DeepMind used with the Daily Mail-based database?
And what if Google were experimenting and using the Quantum Computer it shares with NASA and USRA for these kinds of machine learning tasks?
Or... What if Google were using all the computers in all of its data centers as one unique neural computing system?
I know, science fiction, but...
Ray Kurzweil's vision
Ray Kurzweil is usually known for the "futurist" facets of his credentials. It's easy for us to forget that he's been working at Google since 2012, personally hired by Larry Page "to bring natural language understanding to Google." Natural language understanding is essential both for RankBrain and for Hummingbird to work properly.
In an interview with The Guardian last year, Ray Kurzweil said:
When you write an article you're not creating an interesting collection of words. You have something to say and Google is devoted to intelligently organising and processing the world's information. The message in your article is information, and the computers are not picking up on that. So we would like to actually have the computers read. We want them to read everything on the web and every page of every book, then be able to engage an intelligent dialogue with the user to be able to answer their questions.
The DeepMind technology I cited above seems to be going in that direction, even though it's still a non-mature technology.
The biggest problem, though, is not really being able to read billion of documents, because Google is already doing it (go read the EULA of Gmail, for instance). The biggest problem is understanding the implicit meaning within the words, so that Google may properly answer users' questions, or even anticipate the answers before the questions are asked.
We know that Google is hard at work to achieve this, because the same Kurzweil told us that in the same interview:
"We are going to actually encode that, really try to teach it to understand the meaning of what these documents are saying."
The vectors used by RankBrain may be our first glimpse of the technology Google will end up using for understanding all context, which is fundamental for giving a meaning to language.
How can we optimize for RankBrain?
I'm sure you're asking this question.
My answer? This is a useless question, because RankBrain targets non-understandable queries and those using colloquialisms. Therefore, just as it's not very useful to create specific pages for every single long-tail keyword, it's even less useful to try targeting the queries RankBrain targets.
What we should do is insist on optimizing our content using semantic SEO practices, in order to help Google understand the context of our content and the meaning behind the concepts and entities we are writing about.
What we should do is consider the factors of personalized search as priorities, because search entities are strictly related to personalization. Branding, under this perspective, surely is a strategy that may have positive correlation to RankBrain and Hummingbird as they interpret and classify web documents and their content.
RankBrain, then, may not mean that much for our daily SEO activities, but it is offering us a glimpse of the future to come.
Really great writeup and analysis Gianluca - always appreciate your great contributions here on the blog.
One thing I want to take issue with, though... I disagree that we cannot optimize for Rankbrain or for any machine-learning/AI-based algorithm in search rankings. We can, today, take a really informed guess at what the inputs are that predict a training set will be said, by Google, to be "good" vs. "bad." Those inputs are things like:
If we assume these inputs, when positive, indicate results a machine-learning system is trying to prefer, then we can optimize our pages and sites to better earn them. The best part is that these are things we already cared about (though perhaps we didn't make them as important as we should have compared to signals like keywords and links).
Thus, IMO, Rankbrain is emminently targetable as something to optimize for; we just have to keep paying close attention to how Google might choose what constitutes a "good" vs. "bad" set of SERPs in the future.
Aren't those inputs worth improving anyway? They're not necessarily RankBrain specific, but inputs that inform high vs. low quality SERPs of varying query complexity.
Indeed those factors are not RankBrain specific, on not just RankBrain specific.
But they are not the typical kind of factors we usually have considered, and surely are metrics that needs strong computing and machine learning (maybe deep learning) in order to be processed and fully understood in all their potential degrees and combined relations.
For instance, Time on page/site, browse rate, click depth, etc. may have different relative positive values depending on the kind of search, intent and even niche.
This means that it cannot be something humans can calculate, but a machine/deep learning system yes thanks to a well annotated dataset and iterations.
For this reason, that kind of algorithm would be different in nature from the actual one, and that's why Rand usually talks of the possible coexistence of two algos.
Mmmm... First of all, thank you Rand for your comment.
Secondly, I think I was misunderstood when I was saying that I consider useless to optimize for RankBrain.
I consider it useless if we narrowly look at the intent RankBrain has (aka: to understand too ambiguous queries), because the queries it targets are the "never seen before" and the ones written/spoken with colloquialisms. Therefore, if optimizing for RankBrain would be thought as trying to target the "unknown", that would be fool like trying to target every long long tail with a dedicated web-page.
On the other hand, though, I added that if we work on semantically optimizing our web site as we should be already doing (eg.: structured data - especially using the "sameas" property, wise use of TF-IDF, good ontology/taxonomy et al), then it would possible to even earn visibility for those kind of queries RankBrain targets. Why? Because we would have provide Google with enough, clear and not ambiguous information about the entities and concepts our site is all about. In other words, we could see our content used as a source for creating vectors.
I added, then, that we should work on optimizing our site considering the combination Google does with its Search Entities, which are strictly related to search personalization. I consider, in fact, that the Probability Score the combined use of the Search Entities generates is key in how RankBrain works. In simpler words, if a user performs a query that Hummingbird cannot understand, then RankBrain enters in action and it is quite probable that it will try to understand what the user means analyzing his search history, what web documents were present in the SERPs he saw previously, what was sites was about, etc. etc.
That's why, I was ending suggesting branding as a good strategy (not the only one, obviously), because branding - from an SEO perspective - may help a lot the personalization aspect. Branding, at least for me, also mean being able to conquer the trust of the users, therefore earning good metrics for values like:
Said that, and probably it's here where we may have a slightly different opinion, I see these factors more as "correlated factors" than "causal factors" in the specific of RankBrain.
But what I consider the most relevant thing about RankBrain is that it shows us clearly the direction where Google is going and its ultimate desire: to get rid of humans inputs for creating a Search Algorithm based on Natural Language understanding.
The best thing about machines is they're logical. If we can understand each other - as illogical and irrational as humans are - in the myriad relationships we're a part of every day, then we can understand and optimize for something as logical as an algorithm that determines what a good web page should be comprised of. Even if that might look different to each of us, as long as we're seeing our own personalized results we can be "optimized for" as ideal customers, cohorts, segments and personas (i.e. traditional marketing).
Gianluca, this is a fantastic post! Bravo! I am going to read this several times, and will probably be inspired by something new each time.
I wonder if "vectors" (these "vast amounts of written language embedded into mathematical entities") have more to do with processing speed and bandwidth (sending less information) than a "higher probability of understanding context and detecting already-known concepts". In graphic design terms a "raster" image (a 1-pixel thick line, x-pixels long) is one in which every pixel from point A to point F needs to be filled in. Following the same example, that's information in points/pixels A, B, C, D, E, and F. Six bits, if you will. But the same "vector" image would only need 2-bits of information: A and F (color in every pixel from A to F).
Human neural networks have vectors too. They're called "cognitive heuristics" as defined below:
"cognitive heuristic CONCEPT. are simple, efficient rules, hard-coded by evolutionary processes or learned, which have been proposed to explain how people make decisions, come to judgments, and solve problems, typically when facing complex problems or incomplete information." (source)
That's really all I wanted to add, but let me ramble for a moment... The more neural paths we have pointing to any one "event" in our memory, the quicker and more readily we can access it. They're all in there, but some have more shortcuts and access points. And what is a memory, but a cross section of people, places, and things (i.e. entities) as events in time?
I remember when ENT1211 (entity type = person) wrote a great, thought-provoking post on the ENT2776 (entity type = Thing & schema = Organization) website titled ENT8989 (entity type = Thing & schema = Creative Work).
...Maybe that's how our great grandchildren will talk.
whoa! Thank you Everett
There is a lot of noise out there about the dangers of artificial intelligence and the singularity and up to this point I have not been too concerned that we are heading towards a Skynet inspired judgement day.
That is until I read this:
"Using the Daily Mail, Hermann was able to provide a large, useful dataset to the DeepMind deep learning machine, and thus train it. "
If any artificial intelligence is using the Daily Mail as a starting point then we are truly screwed! :)
Really, my takeaway is that we just need to keep doing solid work. Good quality writing that is readable for humans and understandable for search engines is as important now as it has ever been.
Business as usual!
Correct Marcus, and that is why I ended my post saying that RankBrain itself does not mean that much a change in our SEO daily work.
Ask Google to translate "e-tail" into Spanish and you'll see we're still along way away from intelligent results. Exciting times though. Un saludo
Google Translate is a totally different thing than Hummingbird and RankBrain :D... remember that.
Well said.
Great post Gianluca. Thanks for sending us RankBarain.
Gracias a ti Alberto!... wait? RankBarain? :-P
Es una nueva herramienta que voy a sacar para la gente que se confunda al escribir RankBrain ;) jajaja
RankBrain or SkyNet?
Great Post Gianluca Fiorelli.... Yes, this RankBrain is nothing but the mix of all the algorithms came before, Now it is coming as Machine learning......... Definite it will mix patch of Google Hummingbird, Phantom the Quality Updates, Panda, Penguin, Top Heavy & Pigeon Updates. If any new thing Google will add means we have to update with the same ....... :)
Hi Das,
thanks for the comment, but - please - do not consider RankBrain as "nothing but the mix of all the algorithms came before", because it is not (and I didn't say that). RankBrain is a totally new algorithm and serves a purpose that nothing as to do with Panthom, Quality Update, Panda, Penguin and all the others algorithms that complements the overall Hummingbird one.
Interesting, when they use word "vector" they mean "string" of N variables, V(a1,....aN) or it is just colloquial use for what in math really called "Tensor"(basically matrix NxM variables)?
Superb article Gianluca, I personally suspect that this Rank Brain Algorithm would certainly be impacting SEO in the future. As we know its the machine learning addon, and it will continue to enhance itself within some years and it would make life tougher for SEOs.
Thanks for your research on this topic! I'm super interested in semantic search thanks to David Amerland. I'm wondering if you could please explain further or provide an example what you mean about how to use n-grams within search from your statement: In the case of language, then, having "vast amounts of written language" is not enough if there's no context, especially if not using n-grams within the search so the machine can understand it.
Thanks!
Thanks Gianluca, great post. I wonder, whether the speed on Google's side is language specific, e.g., RankBrain starts with (American) English and later gets to Spanish, French, German etc. - what do you think?
Greg Corrado explicitly said that RankBrain is worldwide ready already, and that it interprets languages, therefore that means it rolled out at the same time in every regional Google and not just in Google.com, and that it working on all languages, not only American English.
And that was how Hummingbird was released too.
I really agree with you Gianluca
Thanks! I missed that.
But this will mean it will evolve in a different speed or not? Or will Google pick up the learning from different languages and translate it then?
No, Google is working on it for all languages and regions at the same speed and at the same time.
It doesn't need to translate anything, as Google index without issues everything in every language as it does, for instance, with the many country targeting versions of your Springer company sites .
With respect to "speed" RankBrain has been implemented for all languages and regions at once. However, the "quality" of RankBrain enhanced search results probably differs across languages and regions (e.g., if the underlying algorithms depend on factors like language specific syntax or number of search queries analyzed). Just a guess.
I think RankBrain was the inevitable direction after Hummingbird.
Well... if we consider RankBrain has a patch to the general Hummingbird algorithm, then, yes, it was something inevitable.
Said that, remember always that RankBrain is not a new version of Hummingbird, simply a new algorithm meants to optimize a specific facet of how Hummingbird works.
RankBrain is the start of a new of looking at SEO and search. What this means is that producing high quality, information focused content is now more important than it ever was! From an SEO perspective, very little has changed. We still need excellent on-site SEO, white hat link earning, and a great social media strategy. However, content will continue to be a key factor as the RankBrian algorithm continues to evolve.
Gianluca thanks for this awesome class, I always learn from your posts. And I wonder, could you mention a website that meet the requirements for this algorithm?
Thanks for this awesome post Gianluca
Will be checking it out soon, gj!
Now I crack this Algo and implement these factor in SEO to rankup the services. ;)
Thanks for the details!
Great Post Gianluca.
Working on a project for a month, I now have a lot of catching up to do with everything search related. Thanks Gianluca for speeding things up with this one :)
Also, cool to see more people mentioning a Wait But Why article in their work.
Rank Brain Is 3rd important signal of Google?
And, What's The most important signal first and 2nd ?
LOL :D
Great article - I realized this on my last website build that all I have to do is to work out the intent of the searcher. Firstly give your customer what they are looking for :)
Great article. Rankbrain adds to the mystique behind Google. I suspect they want SEOs to think they know everything about our sites. That's a philosophy that IT likes to promote.
Gran mensaje!, Nuestra tienda en línea tiene que actualizarse constantemente en SEO y estos post hacer la tarea más simple, muchas gracias!
Un saludo desde España :)
Great article Gianluca. I have one question though. LSI keywords and general meaning of the content will still remain important part of on-page optimization. But, do you think that people will start implementing some other tricks to attract visitors and become more relevant for ambiguous queries?
Gianluca, I just re-read this after Google announced that content, links, and Rankbrain are the 3 largest ranking signals (which we knew content and links, right?). Rankbrain as described helps in those hard-to-understand queries, but it sounds like the depth of Rankbrain is more geared to understanding the intent and substance of all queries based on this recent announcement. SEO and search has been evolving from a text-matching solution to contextual understanding solution for some time now, and it sounds more and more like Rankbrain is pushing the envelope in that understanding on a broader level. As Rand pointed out above, I think we can optimize sites to match queries with user intent by the focus and depth of content. Delivering a good resource for the end user is what Google wants, and Rankbrain appears to be helping bridge the gap in understanding the connection between the query and the best matching results. Thoughts?
Business. As. Usual. Great info on RankBrain though for sure!
Numerous great comments here. And the article is rich in depth and industry thoughts. AI on any level is a marvel in academic achievement. Whether we call RankBrain a patch for Hummingbird or a new creative solution for an AI interpretive we can only speculate and study how we as the users, marketers, businesses, and so on actually use the following and what is done.
Thanks for such a detailed analysis on RankBrain, Gianluca. Perhaps this was an initiative from Google started long back before they acquired Deepmind. You should see this on how they discussed on interpretation of long tail queries and the proposed strategy for deciphering first 10 terms a user types in their search results: https://youtu.be/JtRJXnXgE-A
Maybe we cannot optimize specifically for this but keeping up with entity optimization will help us optimize for RankBrain. IN the video above, they also discuss strategies to deal with mis-spells. This should deal with colloquial search queries that are target of RankBrain algorithm. What do you think?
thanks Mr.Gianluca shear your great post
Fantastic write-up Gianluca.
I agree 100% with your conclusions- how do you optimize for this? You don't! You accept that Google's background toolset and "situational awareness" is continuing to improve, and you ignore the past few months of SEO clickbait. :)
Thanks for the write-up Gianluca. You made some interesting points I hadn't thought of before reading this.
Great post Gianluca!
Thanks Gianluca you have added to my growing admiration of things Google. In my work on NLP I am at the stage of having to build a knowledge base. An alternative as. Implied here is to build a query base with a summary of concept answers. 'Google's, ergo sum'.
of course no impact on our SEO works at the moment. but we will come contribution allows us to see.
I appreciate the Rankbrain theories and research. Thanks for staying on top of latest algorithim changes.
Thanks Gianluca you have added to my growing admiration for things Google. In my work on NLP ( understanding text) I am at the stage of needing to construct a knowledge base, one alternative is why bother, just use google searc. Instead, build up a query database with keyword concepts ( mathematical entities?). 'Google's, ergo sum'.
Thanks for such an in-depth information about RankBrain...Looking forward for more update about how we, digital marketing consultants can adopt ourselves to this new algo...
I believe that we would be able to optimize RankBrain. it would required to understand some irrational.
Thank you for this incredibly well structured, referenced and considered article. I'll be reading this several times over too!
Wow - so, do you think it was Rankbrain, wich answers to may question: "Is it funny to let people walk the plank?" (in my language). The answer was: "without sharks it is half so funny". Or is Google just a funny search engine? Pls: Don't ask why I was searching for that stuff - thx
Day 2, read that post for the third time and a lot great comments like that one by Everett Sizemore, Optimizing for Rankbrain is imo like gianluca thinks pretty useless when we see that these questions are never asked before. Thats a bit optimization for no target. But optimizing for an IA would be pretty interesting. But Pogosticking, CTR, Time on Page - I think thats just not everything (and something we all optimize for I bet/hope). Do we need to learn more about Human neural networks in SEO Future? Arghhh.
Thanks for the write up.