I recently wrote about how to statistically model any given set of search results, which I hope gives marketing professionals a glimpse into how rapidly the SEO industry is currently changing in 2015. In that article, I had mentioned that the search engine model should be able to self-calibrate, or take its algorithms and weightings of those algorithms, and correlate the modeled data against real-world data from public search engines, to find a precise search engine modeling of any environment. <\/p>\n
But taking thousands of parameters and trying to find the best combination of those that can curve fit search engine results is what we in computer science call a NP-Hard problem. Its astronomically expensive in terms of computational processing. Its really hard. <\/p>\n
So how can we accomplish this task of self-calibrating a search engine model? Well, it turns out that we will turn to the birds yes, birds to solve this incredibly hard problem. <\/p>\n
Full Disclosure: I am the CTO of MarketBrew, a company that uses artificial intelligence to develop and host a SaaS-based commercial search engine model. <\/p>\n
I have always been a fan of huge problems. This one is no different, and it just so happens that huge problems comes with awesome solutions. I turn your attention to one such solution: Particle swarm optimization (PSO), which is an artificial intelligence (AI) technique that was first published in 1995 as a model of social behavior. The technique is actually modeled on the concept of bird flocking. <\/p>\n
An Example Performance Landscape of Particle Swarm Optimization in Action <\/p>\n
The optimization is really quite remarkable. In fact, all of our rules-based algorithms that we have invented to-date still cannot be used to find approximate solutions to extremely difficult or impossible numeric maximization and minimization problems. Yet, using a simple model of how birds flock can get you an answer within a fraction of time. We have heard the gloom and doom news about how AI might take over the world some day, but in this case, AI helps us solve a most amazing problem. <\/p>\n
I actually have been involved with a number of Swarm Intelligence projects throughout my career. In February 1998, I worked as a communications engineer on the Millibot Project, formerly known as the Cyberscout Project, a project utilized by the United States Marines. The Cyperscout was basically a legion of tiny little robots that could be dispersed into a building and provide instant coverage throughout that building. The ability of the robots to communicate and relay information between one another, allowed the swarm of robots to act as one, effectively turning a very tedious task of searching an entire building into a leisurely stroll down one hallway (most of these tiny robots each had to travel a only few yards total). <\/p>\n
The really cool thing about PSO is that it doesnt make any assumption about the problem you are solving. It is a cross between a rules-based algorithm that attempts to converge on a solution, and an AI-like neural network that attempts to explore the problem space. So, the algorithm is a tradeoff of exploratory behavior vs. exploitative behavior. <\/p>\n
Without the exploratory nature of this optimization approach, the algorithm would certainly converge on what statisticians like to call a local maxima (a solution that appears to be optimal, but is not optimal). <\/p>\n
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Go here to read the rest:<\/p>\n
Using Artificial Intelligence to Solve #SEO by @scott_stouffer<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" I recently wrote about how to statistically model any given set of search results, which I hope gives marketing professionals a glimpse into how rapidly the SEO industry is currently changing in 2015. In that article, I had mentioned that the search engine model should be able to self-calibrate, or take its algorithms and weightings of those algorithms, and correlate the modeled data against real-world data from public search engines, to find a precise search engine modeling of any environment. But taking thousands of parameters and trying to find the best combination of those that can curve fit search engine results is what we in computer science call a NP-Hard problem. Continue reading