Artificial Intelligence In Digital Marketing MRR Ebook

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Machine Learning

Machine learning on the other hand works differently. Machine learning utilizes huge data sets in order to gain surprising and almost frightening capabilities at times.

Machine learning essentially allows a piece of software to be “trained.” An obvious example of this would be computer vision.

Computer vision describes the ability that some machines have to understand visual information. An example is Google Lens, which can tell you what you’re pointing your phone’s camera at, whether that’s a type of flower, or a product you can buy in stores. Computer vision is necessary for self-driving cars to successfully navigate their environments, and it’s used by apps like Snapchat which use filters to change people’s faces.

How do these work? By looking at thousands and thousands of pictures of every type of object. While the machine learning algorithm will never understand what it is looking at, it can look for patterns in the data which will then be useful to identify those objects in future. For example, it might notice that faces are typically oval in shape, with a dark patch of hair on top. It then knows that if it sees an oval shape with a dark patch at the top, it’s possibly looking at a face.

Machine learning has HUGE potential in just about every field. In future, it can be used to diagnose disease more accurately than a human doctor, to advise on financial decisions, to identify fraudulent bank transfers, and much more.

All of this has HUGE potential implications for internet marketing, and that’s what we’ll be exploring in the following chapters.

A while ago now, Google announced that it had become an AI-first company. While that might sound like meaningless marketing babble, the truth is that this determination actually has HUGE potential repercussions for marketers, businesses, and SEO.

Firstly, what does Google mean by this?

Meet the New, Smarter Google

You might think of Google as a search-first company. The first product that Google provided was a search engine and this is still what most of us associate with the company.

Traditionally, Google’s search engine did not work much like an AI. Rather, search worked by attempting to match search terms with the content in an article. This is why the advice for SEOs was to insert lots of key phrases into their articles, so that Google’s spiders could read that content and quickly identify that it would be a good match for what the person was searching for.

As we all know, this didn’t work out perfectly for Google. Lots of unscrupulous “marketers” abused the system by inserted hundreds of search terms into every article, which in turn meant the content Google would show to the user would be garbled and unreadable.

That’s why, over time, Google has begun to work more and more like an AI. Now, Google no longer attempts to look for exact keyword matches. Instead, Google tries to answer questions that you ask it. It does this by trying to understand what the user is looking for along with the context, and then to provide relevant answers through its search.


Google is able to do this through machine learning. Specifically, it uses a form of natural language processing, which Google refers to as RankBrain.

RankBrain is at least partly responsible for helping Google to cope with phrases and words that it hasn’t seen before. If RankBrain identifies a word it isn’t familiar with, then it can “guess” what it might mean based on context and based on its usage elsewhere. This helps Google to deal with unusual searches that it hasn’t seen before, without simply matching search terms to content in articles. Search queries are turned into “word vectors”, called “distributed representation.” These are words and phrases that are close to each other in meaning and context. RankBrain will then try to map the query into words it understands, or clusters of similar words. From there, it insinuates what the searcher actually means and is looking for, and provides results on that basis. RankBrain also understands the relationships between words, and the way that they work together.

At one point, joining words such as “the” or “and” were ignored by Google. Now Google understands the importance of these phrases and the way in which they impact on the intent of the user. Like all the best machine learning algorithms, RankBrain attempts to improve over time and adapt to users. It can see which results get clicked the most and thereby know when it is doing well and when it is getting things wrong. As such, it is able to improve search results for any given keyword quickly through algorithmic testing, which is helping to weed out low quality content that attempts to game the system.

RankBrain works using a Tensor Processing Unit (TPU), which is an AI specific piece of hardware stored in Google’s data centers. This is a specific chip that is better able to handle the specific challenges of machine learning tasks.

Google’s Further Plans

Over the past few years, Google may have seemingly diversified. It now makes smartphones, it now makes self-driving cars, and it now makes apps like Google Lens.

But at the heart of all of these initiatives is some form of AI or machine learning. Google Lens uses machine learning to identify objects in a scene and allow users to that way “search” the real world around them. Self-driving cars of course are highly reliant of various forms of AI.

And the Google Pixel Phones? Arguably, their main focus is putting Google Assistant in everyone’s pockets.

And this is the real clue as to what Google is up to. Google Assistant is an AI and virtual assistant that users can use to get weather reports, to book taxis, to play music, and much more. Google Assistant uses a combination of machine learning (to detect human language for example) and AI in order to provide useful results and speak in a natural manner.

Google Assistant is closely integrated with Google search. You can ask Google Assistant a question like “who starred in Iron Man?” and it will give you a natural answer. It does this by first using machine learning to turn your speech into a string, then by using Google Search in order to look up useful answers (which involves machine learning in the form of RankBrain), then by using narrow AI to extract the most useful answers from the best web pages, and then by using another form of narrow AI to provide the response in a natural-sounding manner (which is designed to appear like general AI.) Much of this is carried out not on the device that you’re speaking to, but on Google’s TPUs located in the cloud.

What Does All This Mean for Marketers?

So what does all this mean for marketers? Simple: it means that Google wants to be able to understand your content and extract the most useful information. It no longer wants you to use rigid keywords, and it wants you to prepare for a more voice-driven form of search.

Google is betting big on AI and machine learning. It believes that in the future, AI assistants will be HUGE and it wants Google Assistant to be number one. It envisages a future where we spend less time staring at our devices and instead get the information we need by asking our phones or our Google Homes. We’ll speak naturally to these devices, and they’ll provide us with handy answers.

Whether Google Assistant eventually becomes the ubiquitous tool that Google wants it to be or not, the fact remains that Google wants search to become increasingly more natural and human. It already has in many ways.

That means that marketers and website owners need to make some changes to the way they do things. It’s no longer enough to find a keyword and repeat it a whole lot, you now need to work as though you’re speaking with an AI. And that means a couple of things.

LSI: Latent Semantic Indexing

Latent semantic indexing is one of the most important things to consider if you’re interested in improving your SEO and getting to the top of Google. It’s even more critical if you hope to be ready for Google’s AI-driven future. Not only is it a powerful concept in itself, but it is also an important microcosm of the broader changes that we are seeing to SEO today.

Search engine optimization is a big and very important part of digital marketing and if you want to drive the maximum number of people to your website or blog then it’s absolutely essential that you have the search engines on board.

In the past, SEO has largely relied on creating tons of content around a certain topic and repeatedly using a set number of keywords or key phrases in that content in order to help Google identify the subject and help the right visitors to find your pages. Unfortunately, a few people began to take advantage of this system and began ‘keyword stuffing’ by using the same keywords over and over again to the point of distraction. Google had to get smarter and so it did.

Today, using the same keyword too much will get you into trouble. So what does Google do instead? It looks at context and the broader subject of the article. In other words, it looks for synonyms and related terms and this also gives it the ability to better understand what your page is about.

For instance, if you had written an article about “decision trees,” then in the past Google could theoretically have gotten confused and brought your site up as a result when someone searched for trees. It may have thought you were talking about decisions about trees!

Other Details

- 20 Articles (TXT, DOC)
- 1 Ebook (PDF), 25 Pages
- 5 Graphics (PNG)
- 1 Salespage (HTML)
- 10 Ecovers (JPG)
- 2 Promotional Ad Materials (Emails, Banners)
- Resource Report, Cheat Sheet, Mindmap, Social Media Images, Legal Page, Sales Video, Email Swipes
- Year Released/Circulated: 2019
- File Size: 199,841 KB

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