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Hello everyone. Welcome to the 5 Minutes Podcast. Today I'd like to talk about the Bayes Theorem, but please, I'm not talking about formulas; I'm not talking about statistics. I just want to talk about the principle that is behind that is one of the most powerful principles. And this principle says that when you receive additional information, you must revisit your core beliefs. Let me give you an example because, for me, this example is one of the best examples I have ever seen. And I saw this when I was learning, and I watched a video, I think from Discovery, and the video was talking about an example like this one. What is the probability of you seeing an elephant in front of your house tomorrow morning? Probability is always the chance, and if you know Project Management, if you know risk management, if you know probability, you will never say zero. What you will say is it's close to zero. It's 0.000, or it's very, very, very low, whatever, but it's not zero. And this is your belief with your current information. However, I am sharing with you additional information I share with you and say in your neighborhood; a circus just arrived; this circus is based on Animal Place, and they have an elephant. What happened when I provide to you with this additional information? At that same moment, your probability of seeing an elephant in front of your house increased. Right? Maybe it is still close to zero but increased because now. Okay, there is a circus around me, and then you wake up in the morning, and you are watching the news and breaking news.
An elephant that escaped from the circus. What happened? You need immediately to say, okay, now my probability it's much bigger. That's something that may happen in front of my house. Why I'm saying this because every single additional information you receive changes your perception of the risk. I don't want to calculate that. Okay. I have a YouTube video explaining how this calculation is made. But I just want to let you understand when you receive additional information when something happened or did not happen, this affects your future perception of risk and why I'm saying all of this because I want to make sure that, you know, and you use very well the concept of a trigger. The trigger is the best response for you to identify and analyze the probability of some events happening. What is a trigger? A trigger is something like if that event happens, then the chances of the following event happening, like a secondary event, increases dramatically, and you monitor that trigger. You don't monitor the final risk, but you monitor. For example, it's like our thermometer, for example. When you want to measure fever, you use a thermometer, but the thermometer does not tell. Oh, it's, you know, you have a sore throat. No, it just says something is not good. Something is not good. Something needs to be checked, and then you need to do additional exams. But that one is a trigger to call you for an action. Take to the doctor, take to the hospital.
Right. And this is the rational analysis and what is called rationalist analysis. And this is the Bayesian analysis. And the Bayesian analysis wants you to implant triggers on specific events. Another example let me give you an example of a supplier; your supplier calls you close to the end of the year and say, will you have holidays during the end of the year? And then you say no, or maybe yes, and then you just hang the phone. Why? It's very important. If you want to manage projects properly, you need to have the feeling and say, what is behind that talk? What is the rationale for my supplier to talk and to call me and discuss? This is just because he or she is interested in my well-being, my holidays, my vacation, or they are concerned about not delivering that critical equipment. And by having these holidays, they will have additional days to work. And this could trigger a reaction from my side. I would bet, based on my almost 30 years of experience, I would say there is. A strong probability there that it's something like that. There is a very high probability, and you need to understand and immediately go back and say, what's the chance of late delivery of that equipment? You need to investigate. This is a call for action on your side. And this is why the concept of the Bayesian statistic is so deeply rooted in all of us. I do this all the time.
Let me give you a final example for you, AI and machine learning, what I am doing, what I'm doing. I have several triggers in my professional life and things that I monitor because I want to know. So I subscribe to several newsletters. You know, I'm trying to understand what is going on with the volatility of the world. And based on some new information I receive, I can update my beliefs and say, Oh, this product I'm planning to release is a good product or this product. Maybe it's not very good, or this whatever. It's better, or this is not so good. What happened in December last year when I got the chance to go for the first time to Chatgpt, and I started writing and testing this immediately released a trigger and I said, Wow, this is very different from what I was expecting from a machine learning because I was expecting machine learning to help me with calculation data analysis or even to, you know, to improve a chess game or even to win the goal game like DeepMind, AlphaGo did in a brilliant movie of 2016. But I said, No, this is very different. This is opening a space for us to talk about images created by computers, videos, music, songs, lyrics, you know, design books, essays, whatever, all created using this kind of technology. And what happened to me? I said, Wow, this is a super big trigger. I immediately went back to my plan, my working plan for this year, and I went to each of the items I was planning to do, and I said, With this new information, the probability of this product being successful.
How is it And this one and this one and several new projects just arrived. And I need to be honest with you today; I'm spending maybe 30 to 40% of my time today on things that I wasn't thinking about at the end of last year. And why this? Because events changed my view of the probabilities of everything I do and say, is my job at risk, or is the job of my colleagues at risk? Is project management at risk? Is writing at risk? What is at risk? What is not at risk? What I need to review and this is a perfect example of myself trying to mitigate any threats and trying to benefit from the good side by understanding how events affect my perception of the probability of success or failure in the future. And this is the route of the Bayes theorem. So today, you need to understand there are triggers being released at every single second everywhere. Are you using this to reevaluate your professional strategy or project strategy vis this new information? Or are you waiting for the tsunami to hit the ground while you are not prepared for it? So think about that and use the concept of the triggers. This is what Reverend Thomas Bayes would tell you to do, and he did that in the 18th century. Think about that, and see you next week with another 5 Minutes Podcast.