Deep Learning and The Future Search Engine Optimization

Alex Hoffby Alex Hoff

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An innovative and emerging technology in the Artificial Intelligence (AI) field is changing the way we look at machine learning.  The concept of “deep learning” has been around for decades, but recent advancements in this particular field of AI is starting to cause a stir in tech companies around the world.  Google, Microsoft, Baidu, and other search engine companies all have their eyes on this groundbreaking advancement and its enormous potential.

However, this new approach to machine learning is also raising some important questions.  With the ability to more accurately emulate the human brain, many are wondering how it will affect our approach to SEO.  While we are only just starting to dip into this revolutionary concept, we can provide some insight into how deep learning may affect the digital landscape in the near future.

The History of Deep Learning
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boy learning

Humans were once limited to teaching a computer only what we already knew.  With the advancements in Artificial Intelligence, we can now take computer learning to the next level by giving it the ability to learn similarly to how humans do.  Let’s take a look at the birth of deep learning, how it works, and where it’s going:

What Is Deep Learning?
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For a long time, experts thought that artificial intelligence was nowhere near close to being able to mimic how humans learn.  However, recent developments in deep learning have changed the way scientists look at AI.  Deep learning is a machine learning technique that uses algorithms to recognize patterns in different layers of data (known as neural networks).  This technique is loosely based off the human brain and its neuronal structure.

When our brains receive information, it goes through a network of neurons that each process this information and communicate to each other.  Deep learning works in a similar manner.  On a basic level, this algorithm takes information, recognizes patterns, and makes its next decisions based upon these patterns.  This approach to AI was once an area that didn’t make much progress three decades ago.  Thanks to recent advancements in deep learning, the AI industry has experienced a large growth, as both researchers and big name companies invest heavily in this area.

The Beginning of Deep Learning
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deep learning

The start of deep learning can be found in the 1970s, but many attribute Canadian scientist Geoffrey Hinton with propelling the concept to the forefront of AI.  The 68-year-old is known as the “godfather of neural networks” and has been working on the technique for over three decades with his research team.  Their hard work was largely ignored by the academic world, even when in 2004, they introduced “backpropagation” algorithms that would contribute heavily to their research on neural networks and deep learning.  However, later that year, Hinton would receive funding to build a team of researchers that would revolutionize the way we view AI.

 The Deep Learning Revolution

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With the help of improved hardware and other advancements, Hinton and his team were able to realize many of their earlier goals and began having breakthroughs in some of the fundamentals of deep learning.  In 2013, Hinton was hired by Google as a Distinguished Researcher, and he has witnessed (and been part of) some extraordinary developments in AI from Google.

In 2012, Google created one of the largest neural networks using 16,000 computer processors and over a billion connections.  They then set it loose on the internet to browse for images of cats.  Over the course of three days, the algorithm successfully began to identify cats at a high level of accuracy.

Google also made headlines in March of 2016 when their AI machine called AlphaGo beat a world champion Go player in the first of five rounds.   What exactly is extraordinary about such feats?  For one, these demonstrations point to how deep learning uses a neural network to create an advanced search and optimization function, unlike any we have seen before.

Using this ability, we can solve more problems and create better experiences for customers in a variety of different industries.  While there are still some limitations in the field of AI, there is no doubt that deep learning has changed the digital landscape in a significant way in recent years.

How Companies Are Using Deep Learning
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While machine learning certainly isn’t new, deep learning is relatively new to many companies, and this technology is still in the early stages.  That being said, we are seeing many products and services hit the market that have been made possible by new AI advancements.  Here are a few examples of companies that are using deep learning:

Google:

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Google already made its entrance into the world of AI when it debuted RankBrain, a machine learning AI system that improves search results as part of the Hummingbird algorithm.  Google has also been working on furthering deep learning, but, unlike a lot of other companies, Google wants to share and exchange knowledge with everyone.  Thus, they created TensorFlow, open-source machine-learning software that is accessible to everyone.  Google already uses deep learning in their speech recognition systems, Google photos, search, and more.

Facebook:
torch

The biggest social media platform in the world has also gotten involved with AI and deep learning.  The Facebook AI Research (FAIR) is committed to furthering the field of AI, and has been quietly working to solve problems in AI, and now has three AI labs—two in the United States and one in Paris.

Enlitic:

enlytic

This machine-learning company has developed deep learning networks that improve patient diagnoses by correctly analyzing medical imaging data from x-rays and MRIs.  Enlitic technology has the ability to screen for hundreds of specific diseases within a patient’s medical data to give them better results.

Ditto Labs:

ditto labs

Ditto Labs has built voice recognition technology using deep learning networks in order to discover and tag company brands and logos that are posted on social media.  In addition, the technology can identify whether the brand is viewed in a positive or negative light based on the poster’s use of emoticons or environment.  Regular searches can miss a lot by searching only for hashtags and text, but Ditto’s technology can give search marketers important insight into consumer engagement.

Indico:

indico

The Indico tool allows you to analyze images and build text using neural networks.  The software works in real time and allows you to identify people’s reactions through facial emotions, automatically sort photos, and filter NSFW content.  This type of product can be an enormous help to businesses and allow them to make more informed decisions.

The Future of Deep Learning
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Today, we have seen enormous improvement in the field of Artificial Intelligence, thanks to scientists such as Hinton.   However, there has also been some concern expressed over the development of AI.  Bill Gates, Elon Musk, and Stephen Hawking have all made comments related to the potential threat that AI poses to humanity and have warned the scientific community to be careful as they move forward.

The recent advancements have also made some worry about their jobs being replaced by Artificial Intelligence.  While some companies are now looking to utilize deep learning to improve their customers’ experiences, others are concerned that it will render them useless in the future.  Where exactly does search engine optimization fit into this equation?

What Does This Mean for SEO?
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Many companies—both big and small—see the potential of deep learning, and how it can help provide better products and services to customers.  On the other hand, some companies and SEO implementers are a little nervous at the idea of technology that may be able to conduct a search possibly before the person even knows what they’re looking for.

However, many SEO marketing teams can recall when Google’s Hummingbird algorithm was released and made a similar stir.  The algorithm was unique in that it made search engines “smarter” by recognizing the intent of a searcher’s query, rather than a string of keywords.   In the end, Hummingbird didn’t really change much in SEO strategies.

Google, Apple, Facebook, and Microsoft have been using machine learning for years to improve their products and services for users.  Panda and Penguin, both of which use machine learning to work, were also once considered game changers for those using SEO.  In this sense, deep learning is simply another machine learning technique that SEO implementers will need to adapt to.  With that being said, here are some ways in which those who use SEO can prepare for the future:

Focus on User Experience
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All search engine optimizers know that the rules are constantly changing.  However, one thing has remained consistent, and that is the experience of the user.  Every update that Google churns out is all to provide a better experience for the user.  With search engines becoming smarter, this focus on the user’s experience will be even greater than before.  In this case, users of SEO will need to be at the top of their game to attract visitors and keep them returning to their site.

In order to achieve this, it is critical to put yourself in the consumer’s shoes and correctly identify any problems with your site.  Could your page load faster?  Is it completely optimized for mobile?  Is your content relevant?  As search engines become smarter, focusing on the web user’s experience will only continue to grow more important.

Accommodate Different Ways of Searching
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The number of people who are using voice recognition technology to make searches is growing, and those using SEO would be wise to pay attention.  With voice searches, people tend to be more conversational in their queries, and this will require site owners to optimize their sites differently.

While keywords are still important, over-optimizing your site with them will certainly get you penalized by Google.  Implementing long tail keywords to accommodate this new way of searching will be important moving forward.  This may require site owners to delve deeper into the psychology of their customers to find out what questions they are asking and the answers that they want.  Site owners that accommodate this new technology will be the ones who come out on top.

Beef Up Your Mobile Strategy

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After preparing for voice searches by changing your keyword strategy, it’s time to make your site as mobile-friendly as possible.  Voice searches are mainly performed through mobile phones, and it is critical for websites to enhance their mobile strategies for the best results.  This means not only testing your website to make sure that it is mobile friendly, but also indexing your apps.  Now that apps can compete with websites for the same ranking, it is more important to stay on top of your mobile strategy and adjust accordingly.

Use Video and Image-Based Content
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With image recognition rapidly gaining traction, visual content will be key moving forward.  Internet users today are increasingly attracted to visual content such as images and videos, more so than ever before.  In order to get an edge in the competition, search companies will need to provide visual aids to their content and engage visitors.

Keep Churning Out Quality Content
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content

Well-written and original content is critical for attracting visitors to your webpage and keeping them coming back for more.  Google’s updates to its search algorithms over the years have significantly shaped our SEO strategies as it attempted to filter out low-quality content.

Many things may change the SEO landscape with the advancement of deep learning, but the emphasis on quality content will be as important as ever.  Computer learning in general has provided visitors with better experiences, and companies will need to provide users with superior content and SEO in order to remain competitive and relevant to users.

Conclusion
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There is still much that we don’t know about deep learning.  What we do know is that this has and will continue to shape the digital marketing realm.  In order for businesses to stay ahead, SEO strategies may need to change.  Regardless of how this new approach to machine learning affects search results, the emphasis on certain SEO tactics will remain the same.

In order to prepare for new developments in the field of AI, SEO implementers should put a greater focus on the end user’s experience by providing them with superior sites, engaging visuals, and captivating content.  No matter what changes occur as a result of deep learning algorithms, these SEO practices will continue to play a significant role in attracting users and keeping them coming back for more.

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