RankBrain, Programmatic Buying, Artificial Intelligence, Real Time Bidding, Algorithm Updates… Digital marketing these days is all about big words and the math behind them. How is machine learning actually impacting digital marketing?
That’s what I’m exploring in this series on ‘Machine Learning & Digital Marketing’. Although I’m not a machine learning expert, I’m trying to give you an insight on how the practice itself is changing the way we do (digital) marketing today and how we will do it in the future. In the next episodes, we’ll be covering SEO, SEA, Media Buying and Analytics. But first, in this intro, let’s take a look at machine learning.
What is machine learning?
First things first! You’ve probably already heard about these 3 terms:
- Artificial Intelligence
- Machine Learning
- Deep Learning
It’s good to know that there’s a difference between those 3 terms. In fact, Nvidia wrote a great blog about this subject. In short:
Artificial Intelligence is human intelligence exhibited by machines. Machine Learning is an approach to achieve artificial intelligence. Deep Learning is a technique for implementing machine learning.
For example with what is called “narrow AI” we can ask a machine to do a very specific task, like ‘beating a human at chess’ or ‘given a certain word, returning the most relevant page of a website’. Notice how the AI doesn’t need to understand Alexis de Tocqueville’s view on democracy. It doesn’t need to mimic the human brain, just do what is needed to perform te task at hand.
Artificial Intelligence: The art of beating a human at chess
There are lots of ways to make a computer beat a human at chess:
- Ask expert chess players for their strategy and implement it as a combination of ‘If this then that’-rules.
- Gather data on every chess game between two humans. For every situation, plan out the possible actions and the probability of winning the game for each action. Let the system always choose the action that gives the highest probability of winning the game.
You might have noticed that there’s a problem with these two solutions. If the data the AI is based on, is static, the AI becomes very predictable. Even though it might beat humans a few times, once the human gets the decision-making performed by the AI, it should never win a game again considering the human is then able to a develop a counter-tactic against the ‘highest probability’-choices. The AI will not be changing its strategy. So we need a new way.
Machine Learning: The art of beating a human at chess again and again
The new way would be something like this:
- Let the computer play millions of games and gather data on winning probabilities for every action in every possible situations. Make it constantly learn to adjust its own choices, including as many hand-written parameters you can imagine.
This last part, machine learning, ensures that the AI will be able to keep beating humans in chess in the future. Keep in mind that it will need to make mistakes and lose games to be able to learn how to win them. It will not go on a winning streak of 100% starting at its first win. (There is a very good life lesson in this paragraph. 😉 )
To get back to our example, the chess computer might probably learn that the hand-written parameter of ‘randomness’ is important. If it doesn’t want to be perfectly predictable, the AI might want to sometimes pick ‘the second highest probability move’ to challenge the human’s processing capacity. But excellence will be in the balance. It should not lower its chances of succes by too much.
Artificial Intelligence: Simulating a game of football
The one thing that started my interest in AI is gaming. Most of all sim(ulation) gaming. For example (and I’m sorry non-football lovers) the game Football Manager.
It essentially mimics the game of football being played in the real world, with excellent precision. The game seems simple:
- You have a club with a group of players, each of them having their own set of abilities. For example: Scott Davidson, a central defender at the Scottish League Two club Stirling Albion:
- When playing a match, Scott is put in a line-up, combined with some high level strategy decisions that will guide his decision making:
- In-game these players are constantly making decisions. For example, Henderson in this case gets the ball and should decide what to do:
And this is when it becomes interesting: Henderson has to make a decision, which is based on his parameters (Vision, Anticipation, Decisions…), tactical guidelines (‘Pass Shorter’, ‘Take No Risk’) and many more factors. Once he has made his decision, the execution of his action is also based on factors like his parameters (Passing, Technique, Dribbling, …), the pitch quality, his fitness level…
Machine Learning: Keeping the game interesting to play
This would be (and for most people: is 😀 ) a very boring game if there was one tactic that would win every game. The thing with the game is that, given certain limitations, the ‘other coaches’ are adapting their tactic to what you are doing.
This ensures that you’ll have to keep changing your tactics to keep winning games. Makes it a very frustrating game at times, but in essence, makes this game endlessly playable (and some of us do…).
So, now we know what Artificial Intelligence and Machine Learning are, what’s this deep learning thing?
Deep Learning: Mimicking neural networks
Then for the absolute abstract part of this. What deep learning is actually doing is very close to how the think our brains work: through neural networks:
There is a certain amount of input that is being divided over different nodes. This input is getting transformed in different hidden layers of nodes. The amazing thing is that the nodes are connected give their ‘transformed input’ and a weighting of their own input (considering the output) to the next layer.
Given the rising processing capacities and math innovations that science has created in the latest years, we are capable of doing ‘sort of what the brain does’ on a smaller scale.
Dr. Pete Meyers actually explained this brilliantly simple on MozCon 2016:
The way a neural network works is: We have these [layers of] inputs and we have these [layers of] outputs we want to achieve. […] So we’re trying to put something in between that can model that [input to output]. […] We put in this data to train it, but then the machine itself can handle new inputs that’s never seen before.
So actually, by letting the machine learn backwards from the output to the input, we create Artificial Intelligence that processes new input into the desired output. This allows us (bearing in mind the quality of the training data, processor capacity…) to build better data processing tools then our mind is consciously facilitating us to do by hand. That’s crazy.
And this is and will be impacting the world in general and digital marketing in specific. In the next episodes we will be discussing the impact this has on SEO, SEA, Media Buying and Analytics. If you have any other ideas on this, be sure to let me know!
Also published on Medium.