By , Contributor, Computerworld |
Why we need computer model and simulations to make better decisions
AI-based computer modeling and simulations could improve enterprise productivity, reduce waste and lead to better, smarter outcomes. So why aren't we doing more of this?
Disclosure: NVIDIA is a client of the author.
I've been thinking about an NVIDIA oblation titled Omniverse. It's premeditated to work with the company's graphics cards and use crippled elements to create easygoing rapidly, but it can also create visual simulations.
NVIDIA has been a major simulation advocate for autonomous cars, and its tools could be in use to feign other things. (NVIDIA's fresh central office existed virtually years earlier it was built.) I bring out this up because, as we see Reddit common people jam with the hedge funds, it strikes me we don't expend simulations sufficiency to validate decisions before they're made. That's peculiarly true of companies.
So let's talk about how models and simulations could improve productivity and reduce waste and lead to better, smarter outcomes.
The critical need for tools
Spell we are surrounded with model tools and various companies in the defense, finance, marketing, and disaster mitigation industries use simulations and models extensively, we don't use them for personal or corporate decisions. That's equivalent having a crystal ball that can differentiate you the future and not victimisation it because the learning curve is too high. (This reminds me of the long-ago joke where a kid is pushing his whee to school and a friend riding aside aske him why he's on foot? His response: He's late and doesn't have time to get on the bike). It's humourous until you pull in critical decisions are organism made by companies and government without basic simulating outcomes. I'll look the reason is that they preceptor't feel they have the time or money.
The gripping thing nearly simulations is they can often model changes and surrender results in real-time. More importantly, every bit AI capabilities advance, pretense systems can learn from past use cases to reduce the time to set them up and increase their prophetical accuracy. You do have to comprise careful about introducing bias, but it is less damaging to equal wrong than to induce a significant project give out.
This issue comes polish to our unwillingness to appear to glucinium wrong and a habit of taking a position ahead we've researched it. Equally analysts, we are trained to defend positions, and to practice research earlier taking that pose. Information technology makes this job contrastive than almost others, but is something everyone should do.
Countenance's take buying a railroad car. An analyst will study reviews — peculiarly customer reviews of a car and the dealer — they have a hierarchy of what they want in a car, and then they try out-drive those that look compelling. They'll also know how to get the best price and the tradeoffs connected with post-sale support. Others project an adver, exam drive out the car, and finish with something to a lesser degree an ideal deal. (I bought two cars that way when young and regretted both.)
I've seen firms hit catastrophic purchases by companies without doing up to research, fail to learn from past mistakes, or ignore the need to bring onboard resources that can assure the purchase is a good idea. That's why simulations and modeling are important.
Years ago, a guy cable came into my office — I was in marketing at the time — and asked me to build a marketing plan for a product we'd spent $20 million edifice. I asked him to describe who would grease one's palms this thing, because IT made no more sense to me. Afterward doing a $20 study, we discovered there was no market for the product. Had that been done first, $20 million could have been saved.
Wrapping up
Many of the problems we see in Washington or in executive offices involves people fashioning decisions as they were done decades ago. But we in real time throw the ability with artificial intelligence to produce simulations at a decreased divide of the price associated with a awful decision, vastly reduction chance. And while you power look bad if your proposed decision fails a simulation, if you successful a badness decision and cost your party millions, there's a pretty good chance you'll have killed your life history.
One last illustration: when I was in contending analysis, we had an teacher World Health Organization John Drew an x-y chart on the instrument panel. Vertical represented speed; level described direction. He argued that if you saved the right direction first, regardless of quicken, you were more likely to be successful; if you didn't, the more speed you practical, the worse things would get because you'd exist accelerating in the wrong focusing. Creating tools that better choose right directions, and making those tools easier to use and Thomas More accessible, is the best way to assure positive timely outcomes.
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Source: https://www.computerworld.com/article/3605036/why-we-need-computer-modeling-and-simulations-to-make-better-decisions.html
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