Why you need to automatically stop thinking and start learning

Learn fast enough? To be successful in the future, it is not enough to just read and write documents. You need to quickly detect signals and anomalies in data, discover hidden knowledge from unstructured sources such as text and images, create accurate models that can be generalized from the existing data, and publish your results, so that others can learn from it. Quick learning is not easy, it requires constant vigilance and conscious effort, and why? Because man is a slow learner by nature. We tend to get stuck in our old habits and routines, which causes us to rely on our intuition when we should rely on experiments instead; that we trust our first impressions, when we should rely on objective measurements; that we place excessive emphasis on trivial details; that we should instead focus on critical concepts; that we rely on our memory, if instead we should rely on written records; that we trust our own words more than those of other people; that we trust ourselves more than other people; that we trust common sense more than contraintuitive phenomena… The list goes on and on, as Albert Einstein said: “We

Why you need to stop thinking and start learning

We are often so absorbed in our thoughts that we completely forget to experiment and measure. We are so busy thinking about how things are and how they should be that we forget to pay attention to what is actually happening. A great experiment allows us to take a look at the black box of our systems, understand how they work, recognize their current limits and discover potential opportunities for improvement. Automated experiments can help us focus on what is happening right now, right now, and show us the results immediately. And that’s one of the reasons why you have to stop thinking and start learning.

How to stop thinking and start learning

– Keep track of your experiments – Record your observations – Measure your results. – Record your conclusions – Share your discoveries – Evaluate your results – Repeat the process. – Practice makes perfect – Be open-minded – Avoid common pitfalls.

Try not to understand, but to build something.

The best way to learn is to build something. When you build something and see your ideas become reality, you can internalize the knowledge better than if you only read what others know. So next time you’re stuck reading, try building something and see how it helps you – start small. Start with something you can understand well, such as a small data set on a problem you care about or want to solve. – You don’t have to understand everything to start developing, you can’t. So start with the parts you understand and find out the rest gradually. – Your first implementations don’t have to be perfect, they just have to work. – What you are developing does not necessarily have to be ready for production. – If you get stuck, take a break – you don’t have to understand everything to build something. – You don’t have to know what you’re doing to create something – you don’t have to be perfect to finish something.

Don’t ask why, ask what?

The question of “why” can lead you to understand only the “why” but never the “what”, replace the question of “why?” by asking “What?” and you can focus on the essentials – what you really want to understand. – An analyst who wants to know why a forecast gave him a certain result might ask, “Why did the forecast turn out wrong?” while a practitioner who wants to know, “What information did I miss?” , would ask, “What are the reasons why the forecast was wrong? – When writing down your thoughts, start with the “what” and only move on to the “why” when you have a clear understanding of the “what” you are trying to do. – The “what” is the actionable point, the “why” is your justification for action. It is only relevant when you have a clear understanding of the “what” – the “what” is something that can be measured. The “why” is something that cannot be measured.

Don’t ask what, ask who!

When we start asking “who?” we focus on the people who do the work and the things they do by asking “who?” we can learn a lot – if you want to understand what a piece of code does, you can ask who wrote it. – If you want to know why an algorithm makes a certain prediction, you can ask who developed it. – If you want to know another person’s point of view or workflow, you can ask “who” used them. – If you want to know how a particular team or organization works, you can ask who leads it. – If you want to understand the advantages and challenges of a particular methodology, you can ask “who” applies it. – If you want to know the reasons for a particular decision, you can ask “who” made it. – If you want to know how a certain technology works, you can ask “who” uses it.

Do not ask “who” and “why”, but “when”.

“Why” and “when” often lead to generalizations, while “what” and “who” tend to lead us to peculiarities. – If you want to know why a decision was made, ask when it was made and why. – If you want to know what a certain technology does, ask when it was invented and what it does. – If you want to know who made a particular decision and why, ask when it was made and why. – If you want to know what a piece of code does, ask what it does and when it was written. – If you want to understand the benefits of a particular method, ask when and why it was applied. – If you want to know what a particular dataset contains, ask what it represents and when it was collected. – If you want to know what a particular problem looks like, ask who is affected by it and when he experienced it.

Do not try to predict the future – there is no future!

You don’t need to know what will happen in the future, you just need to understand what is happening right now. – Imagine you are an analyst and trying to predict the demand for a particular product. You may want to know if the supply chain will be able to meet demand, or if the marketing team can deliver the right message. – If you try to predict the future and see details like “the marketing team will not be available during the holiday season”, you might end up making a wrong forecast because you are thinking of a time when your team is unavailable. – Instead, focus on what is happening now. Find the bottlenecks in the current system and predict their impact on the forecast.

Conclusion

Learning is a lifelong process, and the sooner you start it, the better. The key to rapid learning is to start small, focus on the present and build on the knowledge you acquire. As long as you experiment, measure and build things, you learn effectively. With the advice “read, read, read”, “write, write, write” and “build, build, build” you can not go wrong – they will take you far. It may not be easy, but it will be worth it.