Nvidia's so-called "Omniverse" is a very interesting technology. Using graphics cards and game elements, it quickly creates content as well as visual simulation. The tool from NVIDIA, a major player in the field of autonomous driving simulation, can also be used to simulate others. Nvidia's new headquarters, for example, already existed ``virtually'' a year before completion.
This is also the story I will talk about in this article. Looking at Reddit's numerous articles on hedge funds, I realized that we don't fully simulate and validate important decisions before making them. Here's a further discussion of how models and simulations can be used to improve productivity, reduce inefficiencies, and produce better and smarter results.
In fact, we are already surrounded by simulation tools. Simulations and models are widely used by a variety of companies in the defense, finance, marketing, and disaster prevention industries. However, it is not used for individual or corporate decision making. It's like having a crystal ball that tells the future, but it's not too difficult to use.
There is also a similar old joke. Seeing a child 'dragging' a bicycle and going to school, a friend who was riding a bicycle asks why he is not riding a bicycle. However, the child says he is late to school and has no time to ride the bike. This story is just fun. At least until you realize that when business and government make big decisions, the consequences are made without even simulating them. Perhaps they'll make weird excuses, like a kid dragging a bike for no money and time.
The most surprising thing about the simulation is that it can reflect changes in real-time and predict the results. In particular, with the advancement of artificial intelligence (AI) capabilities, simulation systems now learn from past use cases to reduce setup time and increase prediction accuracy. You have to be careful not to get in the way of preconceived notions, but there is less risk than a major project failing completely.
This issue is even more unfortunate as it overlaps with our general appearance, where we judge before we even review it properly, and as a result, the situation becomes entangled. But let's look at the analyst. In general, analysts are trained to rationalize themselves and to review them thoroughly before making any stance. That's why the analyst's job is harder than any other job, but when you think about it, it makes more sense for everyone to take this attitude.
For example, suppose you buy a car. If you're an analyst, you'll first look for reviews. Specifically, it searches for consumer reviews on cars and dealers. Everyone has a priority for what they want for a car, so after organizing it, pick a car that looks good and test drive it. We look for not only the best price but also the service after the purchase. On the other hand, in general, many people test-drive after seeing a car advertisement and eventually buy a car under conditions that are less than the best. I also bought two cars this way when I was younger, and all regretted it.
This is similar to corporate purchases. I've seen a lot of companies that make purchases comparable to catastrophe. They did not repeat past mistakes without sufficient review or use internal resources that could make a good purchase. This is where simulation and modeling are important and can play a role.
Years ago, a man came to my office. At the time, I was working in marketing. He commissioned a marketing plan for a building that was expected to invest $20 million. I asked him who his potential customers were. It was because the business didn't seem to be going well at all. After a brief $20 consulting meeting, he realized that the business was not profitable at all. Thanks to this short meeting, he could not fly 20 million dollars.
Many of the problems in Washington and in executive offices are related to people's decision making. This is what I've been doing in my head for decades. However, it is an era in which artificial intelligence can be used to simulate. It doesn't cost much. Risk can be drastically reduced with only a small fraction of the damage caused by bad decisions. Poor simulation results can be upsetting, but if the wrong decisions cost the company millions of dollars, it doesn't end there. Your entire career could be ruined.
There is one more example. When I was in the middle of my analysis-related studies, there was an instructor who explained by drawing an XY chart on the board. The vertical axis was speed, and the horizontal axis was the direction. "Regardless of speed, finding the right direction first increases your chances of success, but vice versa, the faster the speed gets, the worse the situation is, because you're running in the wrong direction faster." I totally agree. Now we need to make tools that can better choose the right direction and improve them to make them easier to use. That's the best way to produce positive results at the right time.
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