Information, computation and learning – is this a recipe for an intelligent an artificial manager?

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Have you wondered what is the difference between an intelligent and a non-intelligent machine? When can a robot be called intelligent, and when is it simply programmed to react in a certain way in certain situations? Today I will tell you about the 3 elements of artificial intelligence that must occur for a machine to have a chance (not a certainty, but a chance) of winning the Turing test.

So let’s make a smart kettle instead of the one you have at home. It will be quite simple – two elements of the three necessary for intelligence are already there. Using your kettle, you pour water into it, press a button, wait a few minutes and water for tea is ready. But what if the kettle knew if you want to drink, how warm you like your tea and when you usually drink it? Here goes.

The first element – information.

In physics, energy and matter are considered the two basic elements. But increasingly, scientists see information as the foundation of all existence. Whatever we want to accumulate knowledge about, we must first be able to record information about the phenomenon. Let me take this opportunity to clarify a misunderstanding that is prevalent in the management sciences. Well, in these sciences it is considered correct to have this sequence of knowledge creation: data, information, knowledge. However, this is not the case.

Information comes first – in simplest terms, it is a representation of the occurrence of a given fact in reality, such as the temperature of water in a kettle. Data is the arranged information according to a certain structure, for a kettle the data would be, for example, a function of temperature from the time of boiling water (it can be a discrete or continuous function). The knowledge a kettle could gather is the context of the data it records – your behavior, mood, time of day, etc. A kettle can record information – the temperature of the water via a thermostat, and more advanced kettles can count down the cooking time. We’re still a long way from knowing, but we’re well on our way to artificial intelligence.

The second element – calculations.

Calculation is the most common association regarding mathematics. Have you noticed that children treat mathematics like a calculator? Many times I explain to students and even scientists that mathematics is not a calculator, but the structure of the world. However, let’s accept that doing calculations has some advantages, and when our kettle can do it, we can find out how much time is left to boil water for tea. This is a really big skill. If the kettle could still record other parameters – how much water we use each time for tea, how long it takes us to pour it, how often we do it – think how complex calculations it could perform to make it easier for us to use the device. Again, we would be closer to a smart kettle. What road still needs to be traveled?

The third element – learning.

The kettle would have to learn to learn. Learning is the simplest manifestation of an object’s adaptation to reality. If the kettle – recording all the important parameters of water, our behavior, the situations in which we drink tea, etc. – could calculate other parameters and learn from those calculations, we would have an intelligent kettle. The kettle would have to create certain behavioral patterns of us in the situation of boiling water for tea. What if it learned something else?

I’ve told you how to make a smart kettle. Now turn the kettle into a management machine. What does it need? Information about organizational reality, making calculations based on that information and the ability to learn, i.e. adapt to new situations. And that’s the recipe for an artificial manager. You can find the details in this blog.