Aha moment symbol6/10/2023 ![]() The author explores 10 different instincts and explains, using many examples and great visuals, how these instincts lead us to draw false conclusions about the world. My second "aha!" moment of the year was learning about the 2018 book by Hans Rosling called Factfulness: Ten Reasons We're Wrong About the World-and Why Things Are Better Than You Think. Today, however, with the overwhelming amount of information that we process, these instincts can lead to serious biases, misinterpretations and falsely drawn conclusions. Looks like the end of our world is pretty close, huh? We likely developed these primal reasoning instincts to help us survive in the early hostile environment. If someone told us that the global population increased by 6.2 billion people between 19, we tend to subconsciously multiply in a linear way: If there are 7.8 billion people in 2020, that's roughly 14 billion by 2140 and 20.2 billion by 2260. ![]() Another example is our tendency to extrapolate data in a linear fashion. When we watch the news, which typically comprises attention-grabbing, tragic or sensationalized information, we tend to infer pessimistically that the whole world is tragic and constantly degrading. When they're processing information, whether at work or in their private lives, many say humans are restrained by a number of primal instincts that do a great job of tricking us into misinterpreting data and thus drawing false conclusions in general. At the same time, skills that are relatively new to humans - like solving complex logical tasks and analyzing multidimensional data - are difficult for humans but relatively trivial for machines. It states that tasks that likely took humans hundreds or hundreds of thousands of years to master through the process of evolution - like walking, running or sensing - are easy for us but difficult for machines. Why is it that we remain unimpressed when our computers crunch through terabytes of data, which is simply impossible for us, but marvel breathlessly when a robot manages to jump on a box without falling down, a task easily performed by a three-year-old human? This observation is called Moravec's paradox (named after AI researcher Hans Moravec). Interestingly, it soon became obvious that machines are great at solving logical problems, yet they struggle tremendously with performing tasks that require physical activity. But it is the amount of data that created the specific need to apply algorithmic help in search of insight. Naturally, this ever-increasing computational power, together with steadily decreasing costs as well as the democratization of analytical tools, allowed more people to explore the potential of AI. – Sally R.A turning point when the concepts of artificial intelligence (AI) and the underlying machine learning algorithms gained popularity in the business world occurred when the amount of data generated by companies started to surpass the analytical capabilities of their employees. The following week I saw a doctor and got the help I needed. I finally understood the cause of the turmoil in my life – strained relationships, the difficulty I had keeping up with my work and taking care of my family. I looked at John, pan in one hand, package of meat in the other, with tears rolling down my face. We need to put all this stuff away so it doesn’t go bad. Do you see all these groceries? We have milk, eggs, frozen food, meat, and everything else. ![]() “What are you doing?” When I tried to explain myself, he said, “Look around. I thought, “I’ve got to make tacos now or that meat’s going to go bad.” So I grabbed the meat and a pan and headed for the stove. I was on my second bag, putting away vegetables, when I noticed a package of ground meat in the refrigerator. The counters were lined with bags – probably 20 in all. My husband, John, and I had just returned from grocery shopping. ![]()
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