I start a new series of posts called «ThoughtsWords» that are not exactly about Economics. 2017 was a great year and I feel I learnt a lot! (thanks in part to all those Retina Papers) I realized I can put in words some ideas while I try to shape up my English.

Oh John… Here I go.

I had a misunderstanding at work the other day… and I hate that. It was a minor thing, though. But I felt I was talking about oranges and my colleague kept insisting on the apple side of the question. And she called me to ask for advice…

I especially dislike those moments because I believe communicating effectively is so important in EVERYTHING we do. Who hasn’t engaged in a fight over who was supposed to stop and buy the groceries on the way home? Raise your hand. Thank you.

Companies should realize the importance of communicating (not just the department of Corporate Communication) because we live in a time where sharing information is vital. If we don’t know how to talk to each other, how are we going to communicate? My point is: people can get interested in lots of things if they get to understand them. We, for example, take for granted that the accountant manager won’t be thrilled to hear about the engineering department`s problems. Yes, I am fully aware we cannot get a degree in Physics in one day, but maybe we begin to grasp the basics and start from there if we feel a tingle in our stomach.

This first transformation of technical information into normal language is hard to do. Well, maybe not as hard as you imagine, it just takes more time and effort. On the bright side, it is also very creative! But then there is another problem: people tend to think that if they don’t use the proper words others might conclude they don’t know what they are talking about. Language is also used sometimes like an entrance barrier: only experts can discuss certain topics in this club, kid.

On the other hand, we are walking humans full of biases. It is good to know how constricted we are by this fact. Undetected biases can be weapons of mass destruction (using this battle jargon economists liked to use during the crisis) in companies and people’s lives. We should constantly think how we make decisions based on these biases. For example, if I asked a full room: “how many of you think you could understand how machine learning works?” I am almost certain that all non-science people in the room would say no. Of course! They are not used to thinking in computing terms, let alone in terms of robots and programming.

That would be a huge mistake and a very unfortunate bias, because we are very lucky to live in a time where all the information is available within reach. You can learn ANYTHING you want on the Internet. Think about all those millions of videos on youtube that explain how to cut your hair, repair a broken chair, bake a cake, fix your telephone, fill your tax form… When it comes to more complex and abstract stuff (although I believe cutting your hair is one of those) language is key. We need to communicate well. We need to lower the level and imagine we are sort of speaking with a Martian.

This is such a thrilling moment for those of us fortunate to live in the rich world. We could all transform into women and men of the Renaissance. Yes! Think about Miguel Angelo… Mmmm… I was going to say Picasso, my goodness. But see? Our brain is a machine learning! The moment I thought about Miguel Angelo, painting was next, and my brain started bombarding me with other names of famous painters: Picasso, Cezanne, Van Gogh, Velazquez… Because they are all related. It is like when you write a message on your phone saying: Happy A… The machine knows that a very frequent pair of words, one of them being Happy and the other one starting with an “A”, is Happy Anniversary. That is machine learning too.

We can grasp the basics of almost anything if we can rely to something that feels closer to us and language doesn’t make you run away to the moon. So, if I were to ask the same question in the same room with the same people, how many would change their minds and think maybe they can understand?

Hopefully I convinced a few. So, machine learning is the way computers find patterns. We feed them with lots of words, books, reports, Wikipedia entries… and let them figure it out. That is how many programs learn a language by themselves, for example. This is so useful and so fast which explains why everybody is using it. The more data you have, the better it is for the machine. But there could be a blind spot in this process. If machines (or algorithms, which in this case are also used to say the same thing) learn from what we humans write… will they be biased too? That is something we (maybe not WE but SOMEONE should be looking into).

Now you know something else about the world you live in. You have very basic knowledge of a topic you thought you would never understand. Next time you come across this term, you might engage in the reading.

I believe journalists have a huge job translating and simplifying difficult information, transforming it into basics. Imagine you just landed naked on the Earth. You would go to a store to get some clothes (yes, where did you get the money from? You had an apple pay in your space craft, ok? ). Well, what would you buy? I would probably start with cotton shirts, jeans, underwear… and then I might go for the leather jacket, the feathers and the sequins.

Information is just like that. We need the basics. Otherwise we might just be Martians wandering around with Danny Zuko’s jacket on. Nothing else.


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