AI has grown to become one of the most useful and powerful tools in the world of technology. To the point where even schools and universities are taking advantage of it and implementing AI into their curriculum, with restrictions of course. Although artificial intelligence continues to evolve, it is not what it’s cracked up to be. Reports have shown that generative AI has been having issues in quality production. It is a hurdle that puts great limits on itself according to Harvard Business Review.
According to their article “Addressing Gen AI’s Quality-Control Problem”, AI has had issues with their output. This type of technology has tendencies of creating problems like making things up, leaving certain answers out and producing limitless possibilities, making difficulties of finding efficient solutions. While companies have found different alternatives to address this matter, it has become very expensive to do.
By bringing this topic to the forefront, we get to see the reality of how AI isn’t always the solution in terms of finding the answers to different situations. This is why I personally chose this to begin with. A vast majority of this current generation uses this tool on a daily basis, but we don’t truly understand the efficiency of it. Especially in the workplace, where problem solving is crucial.
Amazon, for instance, uses Catalog AI as a better approach to avoid quality issues. It detects and blocks unreliable data, produce ideas for new product pages, run tests and uses feedback as a method to keep improving. The upside of this is while various organizations struggle to recieve positive results from gen AI, specifically a financial investment return, eight percent of Amazon’s suggestions recieved positive impact from sale revenue, considering it “measurable value”.
Reviewing this information, it didn’t quite surprise me that different groups of people are using alternative methods to get results like this. Even just in using AI myself, if I notice that I’m not getting accurate answers from one AI assistant, then I turn to others like Microsoft Copilot, ChatGPT, Google AI, etc. It’s also useful for when you want to see different outputs and compare them to each other. The point of performing all these tests is to test the effectiveness of this tool.
This especially applies to companies, who use very similar process to see if their methods work. Going back to Amazon, eighty percent of their outputted data was very unreliable. After going back and reviewing the progress and performance, they were able to make vast improvements. Now approaching to hopefully start in the computer science work field very soon, I pretty much know the do’s and don’ts of using gen AI. And having a process to heighten company data.
Article Source: Addressing Gen AI’s Quality-Control Problem
From the blog CS@Worcester – theJCBlog by Jancarlos Ferreira and used with permission of the author. All other rights reserved by the author.