Things change pretty much on a daily basis in the world of SEO. Since the announcement of Google's AI machine learning algorithm – RankBrain – in 2015, one of the most discussed topics in SEO galleries is: With Google admitting RankBrain being one of the top three ranking factors, these discussions have become even more worthwhile. In past 3-4 months, we also saw a spike in the number of SERPed members asking the same question. And, multiple posts claiming 2017 as the year of AI and Voice Search, we think it is the right time to dive deeper to understand more about it. To get more clarity on this topic, we decided to go straight to the big guns and find out what they think about it. The responses from each expert are compiled below. Fasten your seat belts and get ready for an awesome ride. Albert Mora is the CEO and co-founder of Seolution, an SEO agency for Shopify e-commerce sites. He has been doing SEO from 1997 and has around 20 years of experience. Follow Albert on Twitter here. Since the beginning of the Internet, artificial intelligence has played a relevant role in the operation of search engines. Logically, the algorithms have been evolving, but the fundamental underlying principle remains the same: search engines want to deliver quality search results to the users. For this reason, if you want a long term sustainable SEO results, you must think about the users first, not about the search engines. Alex has more than 15 years of experience in Digital Marketing, and he is working online since 2002.

Machine learning couldn't be hotter. A type of artificial intelligence that enables computers to learn to perform tasks and make predictions without explicit programming, machine learning has caught fire among the hip tech set, but remains a somewhat futuristic concept for most enterprises. But thanks to technological advances and emerging frameworks, machine learning may soon hit the mainstream. Consulting firm Deloitte expects to see a big increase in the use and adoption of machine learning in the coming year. This is in large part because the technology is becoming much more pervasive.

Bots are popping up everywhere from Facebook to home personal assistants. Advances in natural language processing, machine learning and other AI technology created the foundation for bots, but the field has a long way to go before it reaches its full potential. The Alexas and Cortanas of the world do an effective job at accomplishing requested tasks as long as people present them one at a time. A multi-threaded version of these digital personal assistants would allow them to remember multiple situations. This use case is closer to how people actually want to engage with the bots.

Many people wish they could predict what will happen next in the world. Many predictions are assigned to the waste bin of time very quickly. With hindsight, unforeseen factors come into play that changed their'models'. It is because there were so many factors involved to predict. The ability of models to analyse and interpret means technology was not able to process, analyse and predict with a high degree of success.

Facebook's deep-learning artificial intelligence systems have learned to recognize your friends in your photos, and Google's AI has learned to anticipate what you'll be searching for. But there's no need to feel left out, even if your company's computers haven't learned much lately. A growing number of tech giants and startups have begun offering machine learning as a cloud service. That means other companies and startups do not need to develop their own specialized hardware or software to apply deep learning--the high-powered version du jour of machine learning--to their specific business needs. "Deep-learning algorithms dominate other machine-learning methods when data sets are large," says Zachary Chase Lipton, a deep-learning researcher in the Artificial Intelligence Group at the University of California, San Diego, who has examined cloud AI services from companies such as Amazon and IBM.

Amazon is a company that has gone from being an innovative online book retailer, to one of the largest ecommerce retailers in the world, and is now the largest cloud computing provider and a major player in IoT.In addition to their core ecommerce website, Amazon has a line of internet connected ebook readers, personal tablets, a smartphone, a smart TV device, and now their latest, an intellig Their new product is the Echo. It's a small, relatively discreet IoT connected speaker that works in much the same way as Apple's Siri, Microsoft's Cortana, and Google's Now services. Are there enough compelling features with the Echo to make it a breakthrough device in IoT mass adoption? More importantly, are there risks with having an always on, always listening, IoT device in the home? Although IoT devices around the world have now exceeded 5 billion, there are still billions of consumers that haven't seen the value, or even recognized the potential of a more connected home.

Natural Language Processing (or NLP) is an area that is a confluence of Artificial Intelligence and linguistics. It involves intelligent analysis of written language. If you have a lot of data written in plain text and you want to automatically get some insights from it, you need to use NLP techniques. These insights could be -- sentiment analysis, information extraction, information retrieval, search etc. to name a few. Machine Learning (or ML) is an area of Artificial Intelligence (AI) that is a set of statistical techniques for problem solving.

I love to write about face recognition, image recognition and all the other cool things you can build with machine learning. Whenever possible, I try to include code examples or even write libraries/APIs to make it as easy as possible for a developer to play around with these fun technologies. But the number one question I get asked is "How in the world do I get all these open source libraries installed and working on my computer?" If you aren't a long-time Linux user, it can be really hard to figure out how to get a system fully configured with all the required machine learning libraries and tools like TensorFlow, Theano, Keras, OpenCV, and dlib. The majority of the issues that get filed on my own open source projects are about how to install these tools.

I am spending some cycles on my algorithmic rotoscope work -- which is basically a stationary exercise bicycle for my learning about what is and what is not Machine Learning. I am using it to help me understand and tell stories about Machine Learning by creating images using Machine Learning that I can use in my Machine Learning storytelling. Picture a bunch of Machine Learning gears all working together to help make sense of what I'm doing, and WTF I am talking about? As I'm writing a story on how image style transfer Machine Learning could be put to use by libraries, museums, and collection curators, I'm reminded of what a con machine learning will be in the future, and how it will be a vehicle for the extraction of value and outright theft. My image style transfer work is just one tiny slice of this pie.

Feature hashing is a powerful technique for handling sparse, high-dimensional features in machine learning. It is fast, simple, memory-efficient, and well suited to online learning scenarios. While an approximation, it has surprisingly low accuracy tradeoffs in many machine learning problems. In this post, I will cover the basics of feature hashing and how to use it for flexible, scalable feature encoding and engineering. I'll also mention feature hashing in the context of Apache Spark's MLlib machine learning library.