If you ask a person why they can read the number “3” almost no matter how it is written by hand, then very few people can explain why they can identify what they see as the number “3”.

But we do not doubt that those we see are the number “3” and not “6”. Deep Learning works with the same kind of logic. Humans recognize the number “3” correctly because we use our intuition.

Intuition is defined as “the ability to understand something instinctively, without the need for conscious reasoning”. As humans, we use our intuition all the time.

Deep Learning works in ways that are similar to our intuition – It does not use logic as we know it from the IT systems we surround ourselves with today.

Deep Learning algorithms work similarly to human intuition. There are no rules in a Deep Learning system that determine how to define the number three written as handwriting.

A Deep Learning system determines that a given number is “3” based on the knowledge from an algorithm. This algorithm has gained this knowledge by learning perhaps a thousand different ways in which the number three can be written. And maybe 10,000 ways in which the number should not be written. So the AI makes a deduction. This number is “3” when confronted with a number “3”.

### Deep Learning can predict human behavior

Another great strength of Deep Learning is that it is a technology that can be brought much closer to our everyday lives than conventional IT can.

Conventional IT systems are designed as a math and logic-based tool. For them to work, they assume that mathematical formulas can describe the environment they are looking at. It is a useful approach if you develop spreadsheets or design a banking system.

But if you want to make services that interact with people on our terms. That is, speech, text and visual communication than the mathematical problem-solving approach will give you problems. Deep Learning is already technologically superior to all conventional IT systems in such areas.

Deep Learning can be made so advanced it could make predictions about human intentions and rational behaviour. Think about it and its significance. That’s a big deal. There are various examples in the book of how it would be practically possible to work with technology that way for you.

Our AI can make predictions by looking at patterns from past customers’ buying behaviour, and comparing them with our new customer’s behaviour.

The AI will look for significant patterns in how the customer behaved. The system will then make predictions based upon this knowledge. Such forecasts could include an expected output of your relationship with this customer. Such as how many visits you need before he buys from you. If you can make upsells or if he is sensitive to price or not.

This means that we can create a set-up where we are more knowledgeable of a specific customer’s buying preferences are than he is aware of himself. So we know more about how he will react to the meeting with us than he might have thought.

Your employees would thus be able to know which products the specific customer prefers without necessarily talking to the customer before.

This will change the premises for the level of customer service that you could provide. For example, your sales reps will be able to zoom in to your client’s needs quickly, and you will appear professional in the client’s eyes. Or maybe you could offer a digital sales experience that matched the level of service that personal sales could provide.

It is because of examples like this that some conceptually describe Deep Learning as systems with intuition. The system cannot explain why it thinks a given customer will be interested in a given product. However, if the system has enough data to learn, it will be able to predict a given customer’s behaviour with consistent accuracy.

So that AI can learn the meaning of behaviour, and make qualified proposals for actions based on the knowledge acquired is a big thing. How you could use this in practice is also a recurring theme in the book.