Recent years have seen a rapid advancement in artificial intelligence (AI) and machine learning (ML). They are reaching the mainstream and affecting all types of businesses. Inevitably, therefore, many companies have discovered ways to benefit from machine learning and artificial intelligence within their advertising and marketing.

For example, one significant area of growth has been the use of chatbots. These combine elements of artificial intelligence and machine learning with the communication platforms that people use daily. People can communicate with chatbots via instant messenger, in a relatively normal conversation, with the chatbot reacting similarly to how a human would talk.

We often hear the terms AI and ML used interchangeably. And yes, they are indeed related. But they are not identical. In this post, we will examine both the similarities and differences, looking at things from a marketer’s perspective.

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Artificial Intelligence (AI) and Machine Learning (ML) – a Guide for Marketers:

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Artificial Intelligence (AI)

Artificial intelligence (AI) is rapidly becoming one of this year’s buzzwords. Indeed, we have even predicted our Top 10 AI Trends That Will Transform Businesses. 

But what is AI? If we were to believe the movies it involves sentient robots secretly plotting to break Asimov’s Three Laws of Robotics (no robot should harm a human, a robot shall obey any instruction given to it by a human, and a robot shall avoid actions or situations that could cause it to come to harm). But, as with most things, Hollywood is far from the truth when it comes to AI. John McCarthy, the father of Artificial Intelligence, describes AI as being, “the science and engineering of making intelligent machines, especially intelligent computer programs”. You can use AI to describe any situation where a machine becomes capable of mimicking the (cognitive) functions of a human being.

Yes, that can include robotics. Many robots take on tasks previously performed by humans. For example, most modern car manufacturers use industrial robots to assemble their vehicles. Many of these robots can monitor their own accuracy and performance. They can also automatically detect faults, and even notify when repairs are necessary.

Despite Elon Musk claiming that “AI is far more dangerous than nukes”, we are not yet at the point where this may become a concern. 

As the name indicates, artificial intelligence is any situation where a device takes on a task that would normally require human intelligence. We already experience AI daily, in such devices as Siri and the facial recognition your phone may use as a security measure. Instagram even uses AI to consider your likes and the accounts you follow when determining the posts to show you on your Explore tab.

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Machine Learning (ML)

Machine Learning (ML), on the other hand, is merely a subset of AI. The term refers to how a device (a “machine”) can learn from data, without being explicitly programmed to do so. Machines learn from data and make predictions accordingly.

Netflix provides an excellent example of machine learning at work. Every time you go to Netflix, they show you a different combination of movies and television shows. However, if you think about it, they typically offer you a good approximation of your tastes. And over time, Netflix’s suggestions appear to become better at reflecting what interests you. 

How does Netflix manage to do such a good job of selecting movies and TV shows for you? Surely it can’t just be a fluke. Of course not. Netflix uses advanced machine learning and intelligent automation software to improve your customer experience. This allows them to provide personalized service on a mass scale.

Another common use for machine learning is email spam filters. More advanced filters, such as those used by Gmail and Yahoo, don’t just rely on pre-existing blacklists of dangerous email addresses. Instead, they include code to generate new rules based on emails as they arrive. They look for patterns that could signify spam, and then quarantine or otherwise protect against suspicious emails.

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AI and ML are Inter-Related

Technically, all machine learning is also artificial intelligence. Think of AI as incorporating the whole field of software that attempts to mimic human behavior. Machine learning uses artificial intelligence to allow computers to learn independently of direct programming. By definition, machine learning implies that you haven’t preprogrammed your machines to do every task, they “learn” some of them as they perform them.

You can look at an AI system, incorporating ML, as having four basic steps:

1. You build an AI system.
2. It incorporates ML models that take data generated by the system and look for patterns.
3. Your data scientists optimize their ML models based on patterns in the data.
4. This process continually repeats, constantly refining the models’ accuracy.

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Differences Between AI and ML

We can sum up many of the main differences between artificial intelligence and machine learning as follows:

Artificial intelligence (AI) covers all cases of machines simulating human behavior, both pre-programmed and non-pre-programmed (ML) situations.

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Not All Artificial Intelligence Includes Machine Learning

Although we look at ML as being a subset of AI, the reality is that most times we refer to AI we are talking about examples that incorporate ML. This is probably because artificial intelligence use cases that involve technology adapting to the conditions around it sound “sexier” and more futuristic. 

However, AI comes at four capability levels:

1. Reactive machines – systems that simply react, not forming memories or using past experiences to make new decisions.
2. Limited memory – systems that reference the past, but this information is short-lived.
3. Theory of mind – systems able to understand human emotions and are trained to adjust their behavior accordingly.
4. Self-awareness – systems designed and created to be aware of themselves.

It is only AI in the higher capability levels that incorporate machine learning.

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Where Does Big Data Fit In?

The term “Big Data” has been bandied about since 1997, however, there has been no consistent meaning for it. It is a combination of the ability to collect a sheer mass of different types of structured and unstructured data, along with a change in view toward how firms can use this data. The collection of Big Data, allied with new capabilities for analysis, has provided management with added visibility into company operations and their client base like never before. Increased visibility leads to greater insight, which in turn, leads to better decision-making.

Big Data and the analytics you perform are business tools you can use to improve your future outcomes based on past events. It can make extremely accurate “best guesses” of future events, using a combination of the data collected in the past, and even real-time collection in the present. 

AI technology that engages in machine learning needs to make its decisions from somewhere – it does it based on these “best guesses” of future events generated from Big Data. Machine learning makes its decisions based on this historical data without any need for explicit programming, apart from the core programming of how to work with the data.

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What About Deep Learning?

Just as we can look at machine learning as being a subset of artificial intelligence, we can view deep learning as a subset of machine learning. Deep learning makes the process of collecting, analyzing, and interpreting large amounts of data (Big Data) easier. Deep learning can automate predictive analytics.

In traditional machine learning, the programmer has to be very specific when telling the computer what types of things it should look for. For example, in a facial recognition program, a computer “learns” specific faces, so it can recognize them in the future. However, the programmer first has to train the computer what a human face looks like – so it doesn’t mistake a dog face for a person, for instance. The computer’s success rate depends on the programmer’s ability to accurately define a feature set for a person – and then you have to distinguish between males and females, children and adults, Caucasians and Black people, for instance.  With deep learning, however, the program builds the feature set by itself. 

It will probably begin with training data, which the program will use to create a feature set for what it is targeting, for example, it may use the training data to build an initial view of a human face of a middle-aged white male. Over time, it uses deep learning algorithms to update its models repeatedly to be more accurate.

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Using Machine Learning and Artificial Intelligence to Personalize Customer Service

Businesses can improve customer service, and therefore, the customer experience, by combining historical customer data, complex algorithms, natural language processing (NLP), and even emotion analysis to better predict customer wishes.

Similarly, call centers are now using predictive analytics software to reduce the need for repetitive questioning and improve the customer experience. This software incorporates ML and continually adapts, providing agents with up-to-date, relevant information to improve call quality and improve customer outcomes.

Retailers can use machine learning to speed up their reactions to external events. For example, Walmart uses artificial intelligence models to predict the optimal inventory mix for a particular store on a given day. Walmart can predict when to ship specific items from their distribution centers to a store, without waiting for orders to come through.

And of course, Amazon is probably the most visible example of a retailer extensively using artificial intelligence and machine learning. Every time you return to their site, they suggest items they think you would like to buy. In most cases, their predictions match your tastes accurately, and budget permitting you are likely to consider their offers.

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How Can AI and MI be Used for Customer Segmentation and Targeting?

Traditional customer segmentation tends to be broad, dividing an audience by age, sex, personal interests, socioeconomic background, geographical location, etc. It might go slightly further, for instance separating first-time customers and repeat customers. However, that is about as in-depth as most customer segmentation goes.

The software development company, rinf.tech, argues that we can perceive segmentation as “macro-segmentation” and personalization as a “micro-segmentation.” And, we only achieve true personalization when we deploy AI and ML algorithms into marketing campaigns. Only personalization can offer unique messages and value propositions that exactly match many individual customers’ needs, preferences, and desires.

Powered with machine/deep (ML/DL) learning algorithms, you can use AI to analyze customer data more thoroughly and generate in-depth results about targeted segments. From this, you can automate personalized marketing campaigns for each group.

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Wrapping Things Up

Artificial intelligence (AI) is rapidly finding a place in marketing. That is why we now devote an entire section of the Influencer Marketing Hub to presenting the latest AI Marketing news, tools, and resources to enable businesses and marketers to connect and harness the power of AI Marketing.

However, it is full of buzzwords and can be confusing to newcomers to the field. Some, but not all, AI incorporates machine learning (ML). In turn, some, but not all, ML incorporates deep learning (DL). And all these work on the collection and then analysis of Big Data (BD).