What is SEO?
Search engine optimization (SEO) is a commonly-used acronym that describes a series of tasks used to improve a website’s ability to be indexed by search engines for inclusion in the search engine results page. (Source: SEOauditguide.com)
What is machine learning?
Machine learning is a division of computer science that studies pattern recognition. Using algorithms as guide rails, computers analyze data to identify patterns in datasets and then use what it learns to make predictions. (Source: Wikipedia.org)
Machine learning is such that computers do not need to be explicitly programmed everything that they need to know in order to perform a function. You no longer need to outline every step a computer takes in order to make it do what you want it to do.
What are examples of machine learning?
In 1956, Arthur Samuel wanted a computer to beat him at checkers, so he had the computer play against itself to achieve this goal. Without having to program the computer with instructions on how to beat itself, Samuel simply programmed the computer with the rules of the game and he had achieved his objective–the computer beat him every time. (Source: Jeremy Howard, TED.com)
Today, we have even more examples of the application of machine learning, including Google’s search engine and its self-driving cars. Google’s now suggests long-tail queries that it has learned are most common based on the few keywords I’ve entered in this example.
Without having to hit the “Enter” key, Google shows you the common queries suggested by its machine learning algorithm, RankBrain, that it believes are relevant to your search intent given what it knows about other users and their prior searches.
Baidu and Pinterest both use deep learning (a branch of machine learning) to power their algorithms. Jeremy Howard’s TED talk and this blog post from Pinterest’s engineering team demonstrate how they use deep learning to analyze uploaded images and show similar results for visual searches.
Because of deep learning, you can now select an item in a photo on Pinterest and Pinterest’s algorithm will show you a series of similar images, which increases the likelihood you’ll find a promoted pin or a buyable pin from a retailer.
What’s the future for machine learning?
Nick Bostrom believes that machine learning is the last human invention we’ll need because machines will be capable of learning and inventing solutions themselves, what he refers to as superintelligence. Machines will be better and faster at inventing than us. (Source: Nick Bostrom, TED.com)
What’s the future of SEO?
Today, search engines collect and analyze as much information as possible to get the mobile user the one right answer as quickly as possible. Algorithm changes since the inclusion of RankBrain, as well as the rise in voice assistants, our devices will continue to work hard to reduce the friction of search.
We’re on our phones, constantly searching. We want to know how our family and friends are doing. We want to know what happened on that date in history. We want to know what we should know (FOMO). Devices equipped with voice assistants make it such that we are less inclined to type. Siri and Cortana can find answers, set alarms, get directions, and much more.
We will be less dependent on search engine results pages and increasingly dependent on voice assistants. When you produce content, try to use natural language, long-tail queries, i.e. answer questions your audience asks. Voice search is radically different than a desktop or mobile search and natural-language queries is best practice today.
Additionally, you should consider a content audit to help you create a persona document for yourself, your brand, your blog, your business. What have you been writing about? Who is your target audience? What picture could be painted that describes your writing? This will help you better understand why your website ranks well for some queries and not for others.
What are the consequences of machine learning for digital content creators?
Humans are largely predictable, exhibiting the same habits and patterns day in and day out. (See this article about the average day in the life of the average American in the United States.)
If you write content to help other humans change their behaviors and habits, then you know how difficult change actually is.
Machine learning is the antithesis of behavior change. Algorithms are designed to identify patterns and make predictions.
The result for digital content producers? We’re still relying on humans to tell search engines that they want to change rather than the other way around. In the event that a human wants to improve how s/he budgets money, for example, s/he has to tell search engines, like Google, and voice assistants, like Siri, to find relevant results to help with this behavior change.
In the future, these predictions that computers generate will become suggestions. We’ll see the application of machine learning expand far beyond understanding historical patterns and become incredibly important to helping us see the consequences of our choices decades from now. Digital content creators can respond by being closely attuned to their audiences, providing with the exact content they need today while simultaneously supplying content that will help them make incremental changes in their lives.
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