Ever heard of generative artificial intelligence (GenAI)? It has been making lots of attention grabbing headlines. It’s a content creation system, best known for ChatGPT, (i.e., term paper writing). Let’s not go there. GenAI does so much more. Sticking with the technology, GenAI is part of the machine learning cluster. It utilizes sophisticated AI-based algorithms to generate content (e.g., text, imagery, video, and audio clips). I must confess I’m using GenAI.
I have been experimenting with a GenAI application associated with imaging. I’m using it for restoring old photos of ancestors dating back well over a hundred years. Previously, I had to do this process manually. I’d make adjustments with slide controls and watch the effects they had on the faded images. It was a very slow process that took hours per photo. Last year my digital photo processing suite was updated with GenAI features.
The restoration process took a quantum leap in its abilities for retouching, colorizing, and other corrections to the old photos. If the damage to the photo is extensive, however, it still needs the human touch. Overall, the GenAI infusion helps. Minor restoration techniques are fast, and the automatic features are the way to go. For major repairs it needs me interacting with the program. The GenAI addition is a big plus and I’m not going back to that old system! GenAI tools are also getting attention on the power grid.
A Ticking Time Bomb
Have you ever heard of the COBOL (Common Business Oriented Language, circa 1959) computer language? It was all the rage 60 years ago, and amazingly it’s still alive and well today. COBOL has been dubbed a ticking timebomb by a lot of experts, which has resulted in an uptick in interest. According to specialists, COBOL is actively driving many critical systems for business, finance, and administration globally. It’s estimated that roughly 70% of today’s businesses employ COBOL.
They didn’t breakdown the business categories, but it’s reasonable to expect that the power delivery industry is part of that 70%, since they too utilize massive billing systems, customer records, and other enterprise-grade applications. It’s an issue because there are only a small number of COBOL experts around the world capable of working with the quirky programming, and no one wants to do a rewrite!
In short, modern systems are cloud oriented and COBOL isn’t, and they don’t play nicely together. So why not replace COBOL and be done with it? Well, that is easier said than done. One anecdote quoted by the experts features a bank in Australia forced by a buyout to replace its COBOL platform at a cost over US$700 million and it took five years. That is where the GenAI technology comes in.
Companies such as IBM, Google, Microsoft, and others are developing GenAI translators that can quickly convert legacy COBOL into newer languages like Java. Experts are predicting these GenAI translators will reduce replacement times from years to months, with significant cost savings. These translators still require human interaction to review and test what they produce, so it’s not total sorcery on GenAI’s part yet.
This is just the tip of the iceberg as far as the global trending of GenAI is concerned. The application is getting a lot of interest. Fortune Business Insights reports, “The global generative AI market size was valued at US$ 29.0 billion in 2022. The market is projected to grow from US$ 43.87 billion in 2023 to US$ 667.96 billion by 2030, exhibiting a CAGR (compound annual growth rate) of 47.5% during the forecast period.”
“Charging Ahead” has featured the integration of AI into systems such as asset management, demand forecasting, renewable energy forecasting, and many other applications. It has been incorporated into just about every aspect of the smart grid technology. It would probably easier to list what hasn’t been combined with AI than what has been. All of these cases have one thing in common and that’s the big-data used to generate the models used for pattern recognition, forecasting and optimization.
GenAI’s ability to create new content based on those models and to improve the performance of the models is going to be utilized in ways that haven’t been anticipated before. This GenAI transition into digital technologies has happen much faster than anyone expected and it’s giving those who use it a big edge over those not taking advantage. It’s an ambitious undertaking, but it’s definitely not going to be boring!