The search engines have matured much since their humble beginnings and the SEO strategies of the future must become fully aware of their increasing potential if they hope to stay in their good graces and be blessed with plenty of high-value traffic.
Last year saw the full performance of Semantic Search and the prospect of the Google AI behaving much less like a directory machine and more like an insightful guide. Rather than taking the search queries as mere entries to be matched with equal entries, this incredible thinking machine now has the capacity to look for meanings behind words and provide more meaningful results to their users.
This contributes to Google’s primary goal of providing their users with the results they are truly looking for. It was the creation of the Knowledge Graph database that set the foundation for Google’s latest Semantic Search technology, which allows the results provided to have more meaning by rethinking what truly is relevant to the query and possible interpretations of the query.
The Google AI will apply more than simply a massive database it contains on just about everything on the planet, Knowledge Graph, but more importantly, the many ways that the information it holds on all these topics is interconnected. This means that the results for a query about “Elizabeth Taylor” also includes notable and relevant details like her birthday, her unique eye colour or her first great performance.
Highly meaningful results will be the flavour of the day as the Google AI accesses more properties on the entries it contains. These results will be far more accurate to the user under the understanding that things are connected to other things in very important ways. It will be the subtle connections that will make or break the strategy.
What This Will Mean You When Planning Your SEO Strategies in 2018
The Google AI of the future will rely heavily on elements like anchor texts, meta tags, substantive content and links to form these precision results. But, the real trick this year will be learning how to make an advance in all directions.
It will be essential to consider SEO as a crucial weapon in a much larger arsenal of marketing efforts that will be used in combination to reach your goals. Then you must consider every small adjustment and action taken by your campaign as a part of a greater semantic identity you are creating as you go.
From the web design, to social networks to the blogs you choose to engage everything will be creating a credible and successful campaign in the future or a flop. Here we will see the essential importance of marrying your SEO and Digital Marketing efforts in one seamless strategy.
There will be three crucial areas of your marketing campaign you will need to focus on, creating Authority, building Trust and gaining relevance. The quality of the methods applied in boosting these three areas will decide the final success of your venture.
The media has not got a clue about artificial intelligence (AI). Or technology. ‘Robots are coming for your job’ is a popular cry, but the next day it’s fears about AI starting World War III.
Not only do robots and AI have very little to do with each other, but AI is at a very early stage. What’s more, it can be split into several separate technologies.
The masses are being misled into fearing automation and a nebulous super-intelligence, but it’s those with a working knowledge of how AI works – and how it can be exploited – that will be best prepared for the future of work.
What is AI?
There is no precise answer to this question, but it’s got nothing to do with robot overlords. AI is a field of computer science that examines if we can teach a computer to ‘think’.
AI as a phrase has been around since 1956 when it was coined by American computer scientist John McCarthy, six years after English mathematician Alan Turing had published a paper called ‘Computing machinery and intelligence’ in 1950.
AI is generally split into various subsets that try to emulate specific things that humans do. Speech recognition mimics hearing, natural language processing mimics writing and speaking, image recognition and face scanning mimic sight, and machine learning mimics thinking.
That’s a lot of different, often unrelated technologies; AI is an umbrella term, and certainly not a general purpose technology.
Why is AI so hyped up?
Research into AI is currently riding the wave of increased computing power and big data. Together they make AI both possible and imperative; as a society we now produce way too much data to ever process ourselves or get any insight from. Collected data is growing 40% a year, and it’s mostly going to waste.
The existence of all this data also means that AI software has enough information not only to work with, but to learn from. Is this AI’s big moment? Venture capitalists and technology giants such as Amazon, Google, Facebook, Microsoft and Apple think so, and are investing heavily in research.
It’s these companies that have unimaginably huge data sets collected in the last few decades, and a vested interest in automating tasks on that data. Together they’re becoming the arbiters of AI know-how, so it’s AI techniques developed by Google et al. that are being used by scientists to trawl through data to get new insights.
There’s about to be an AI-powered knowledge explosion.
Supervised machine learning
Machine learning is the act of computer scientists training a computer to do something. It’s about automating repetitive tasks, essentially training a computer to recognize patterns, and categorize data.
The classic example is image recognition or ‘AI vision’; give a computer a large number of images containing labeled objects, and the computer can learn to identify them automatically. The computer creates what AI researchers call a neural network; a virtual brain connection similar to a basic process in the human brain.
However, creating a neural network like this takes a lot of human labor, and also a lot of processing power. Google AI and the University of Texas recently used AI on a labeled data-set of signals from the Kepler space telescope to discover two exoplanets when astronomers had failed to find anything.
It’s also being used to identify cracks in reactors, and even help engineers at the UK’s Joint European Torus facility capture and deploy nuclear fusion energy.
This is supervised machine learning, and while it’s getting better at not forgetting, its usefulness at predicting patterns in data is hamstrung by the data it is fed.