A.I.’s Weather-Prediction Applications for Businesses

The Five Most Key Takeaways from This Blog Post

• Artificial intelligence for weather prediction and utility optimization (specifically, in figuring out ideal designs for and modifications to power-grid systems) is a growing field. 
• For weather events, A.I. is particularly adept at small-scale, short-term predictions of weather events. Long-term predictions tend to be a bigger challenge for A.I., which is why some efforts are made to combine A.I. with the longstanding general-circulation models, which typically have been expensive to run. A.I.’s implementation could potentially require less overall compute to get accurate weather predictions. 
• Worth noting is that A.I. could drive up energy usage to the point where its help in optimizing energy usage may not even offset the large increase in energy usage because of A.I. Much of this energy usage comes from the data centers that have been getting a lot of attention lately. 
• This has many applications in the business world. The insurance industry, for one, would very much like access to a technology that offers more accurate predictions of extreme weather events that could come with costly incidentals.
• For business owners, caveats to understand is that this is still an emerging technology, with some tools better than others. Also key to understand here is that the general rule for data and A.I. still stands, that being, bad data will most likely yield bad results. 

Applications for This Technology

With natural disasters like hurricanes consistently troubling the globe, a technology that can more accurately predict the coming of these extreme weather events would certainly be welcome. 

The private and public sector alike would welcome A.I. that can do this data-intensive, time-sensitive work. 

Governments would be able to better prepare for and map out e.g. evacuation plans for something like a hurricane blowing into town. 

The insurance industry got a mention in the fourth Key Takeaway section above, and for good reason. A significant chunk of this industry’s payouts go toward claims for property damage incurred by natural disasters. (However, as a report by 60 Minutes widely and publicly broadcast, some of the places most vulnerable to natural disasters tend to also be vulnerable to fraudulent and predatory practices by insurance companies seeking to avoid any such payouts beyond an insultingly small amount.)

A.I. can better identify for these companies the potential level of risk that a client may pose to the company. That, in turn, could lead to more rejections or higher premiums. 

But consider companies that, for instance, are involved in real estate or construction. With more reliable weather predictions, these companies could get a better sense of the kinds of investments that are worth making. 

Plus, those real-estate companies will also be contending with what are likely higher premiums to insure, e.g., a multifamily apartment complex because of insurance companies’ pointing to A.I.-generated data to support the higher prices. 

Another industry to consider is safety-equipment manufacturers that will be able to target with increased accuracy markets that will probably be most in need of things like disaster-relief safety equipment. 

Caveats About the Current State of This Technology

Some expanding on the fifth point of the Key Takeaways section will be useful here. 

Some of the big-tech companies’ weather-prediction software (see e.g. Prithvi, the foundational A.I. model that’s the result of an IBM x NASA collab) will be trained on many decades of weather data. 

Not all weather-prediction tools will have such a wide-ranging data set undergirding its predictions. For this reason, it will pay to investigate the data that weather-prediction platforms have been trained on. Because overall, it is the data that determines how well the platform functions. 

Also, since general-circulation models have long been the standard in weather prediction, it remains to be seen whether the best way forward will involve a combination of these models with A.I., or letting A.I. take the reins fully. In other words, it still remains to be seen what the best weather-prediction approach involving A.I. will be. 

Other Great GO AI Blog Posts

GO AI the blog offers a combination of information about, analysis of, and editorializing on A.I. technologies of interest to business owners, with especial focus on the impact this tech will have on commerce as a whole. 

On a usual week, there are multiple GO AI blog posts going out. Here are some notable recent articles: 

• For Businesses and Other Organizations, What Makes a Successful Chatbot?
• IBM Watson vs. ChatGPT vs. Gemini: How Will Each Affect Search Engines?
• Using A.I. to Find Resources for Business Owners
• How Would Restricting Open-Source A.I. Affect Business Owners? 
• The EU’s A.I. Act Has Become Law: The Implications for Business Owners (Especially American)

In addition to our GO AI blog, we also have a blog that offers important updates in the world of search engine optimization (SEO), with blog posts like “Google Ends Its Plan to End Third-Party Cookies”.