A labor of Love 36 years in the making. Continue reading Industry Teardown – The True Cost of Building the Best Content
Updated: August 13th, 2018
I have been busy over the weekend contacting additional webmasters, looking for patterns, still, aiming to nail down what exactly might be going on. I continue to discover new posts hitting Google’s product forums as examples of the global reach this update had. I doubt most webmasters are aware of this area to post issues likes this so by proxy seeing anyone comment here shows a global impact. Continue reading Goog Medica Update: Part 2
Product should overlap with Marketing
Several weeks ago marked the 12 year anniversary of Google’s IPO. It was valued at some 30 billion dollars then and today sits on top of the world at over 500 billion dollars. It’s no accident that Google has optimized its network to achieve this level of success when you consider the company’s historical past. Google has done a tremendous job monetizing its platform over the years and keeping webmasters on their toes with Google rankings.
I have attempted to document my take on key Google algorithm changes over the years and internal insights that I have garnered from working in the competitive SEO industry since 2005.
You might be saying, “But Brian, there are 185 posts already documenting Google’s supposed History. What’s new with yours?” Well most, if not all of them outside of a few, are devoid of any sort of filter/analysis from industry experts.
Machine learning at Google and it’s applications to search rankings and other product innovations isn’t something that is talked about much in the marketing blogosphere. It’s critical, however, to understand that Google is pushing this type of technology throughout it’s enterprise.
One strong sign that Google is years into machine learning is an open source software library they created for Machine Intelligence called TensorFlow, which they have given to the world to iterate on top of.
They have openly admitted they use TensorFlow in assisting with search results. Jeff Dean calls attention to it in this video at the 27 second mark.
Aki Balogh, the Founder of MarketMuse, helps define it further.
“Machine learning and artificial intelligence are technologies that rely on computers to notice patterns in data. When you combine this with large volumes of data (‘Big Data’), you get systems that are really good at specific tasks, because they can pick up on patterns millions of times faster than a human. Often purpose-built systems like MarketMuse or what Brian calls attention to below that Google might use, are called Artificial Narrow Intelligence (ANI). Combining the pattern recognition of an ANI system with the creativity of a human being is a winning combination.”
Machine learning could be applied at Google in the following ways. Let’s take a look further. Continue reading Machine Learning Applications Google Might be Using