Another of the many reasons why Generative AI requires other methods -- such as the 70 yeas of AI and computer science -- to test, evaluate, and correct anything and everything that it "generates", As the explanation below says, it does not "UNDERSTAND" what it is doing It just finds and reproduces patterns that it finds in its huge volume of data. Giving it more data gives it more patterns to choose from. But it does nothing to help it understand any of them. This method enables it to surpass human abilities on IQ tests, law exams, medical exams, etc. -- for the simple reason that the answers to those exams can be found somewhere on the WWW. In other words, Generative AI does a superb job of CHEATING on exams. But it is hopelessly clueless in solving problems whose solution depends on understanding the structure and the goal of the problem. For similar reasons, the article mentions that self-driving cars fail in complex environments, such as busy streets in city traffic. The number and kinds of situations are far more varied and complex than anything they have been trained on. Carnegie Mellon University is involved in more testing of self-diving cars because Pittsburgh has the most complex and varied patterns. It has more bridges than any other city in the world. It also has three major rivers, many hills and valleys, steep winding roads, complex intersections, tunnels, foot traffic, and combinations of any or all of the above. Drivers who test self-driving cars in Pittsburgh say that they can't go for twenty minutes without having to grab the steering wheel to prevent an accident. (By rhe way, I learned to drive in P:irravurgh. Then I went to MIT and Harvard,, where the Boston patterns are based on 300-year-old cow paths.) John ________________________________________________ AI-Generated Code Has A Staggeringly Stupid Flaw It simply doesn’t work. https://medium.com/predict/ai-generated-code-has-a-staggeringly-stupid-flaw…. . . So, what is the problem with AI-generated code? Well, one of the internet’s favourite developers, Jason Thor Hall of Pirates Software fame, described it best in a recent short. He said, “We have talked to people who’re using AI-generated code, and they are like, hey, it would take me about an hour to produce this code and like 15 minutes to debug. And then they are like, oh, the AI could produce it in like 1 minute, and then it would take me like 3 hours to debug it. And they are like, yeah, but it produced it really fast.” In other words, even though AI can write code way faster than a human programmer, it does such a poor job that making the code useful actually makes it far less efficient than getting a qualified human to just do the job in the first place. . . . Well, AI doesn’t actually understand what it is doing. These generative AI models are basically overly developed predictive text programs. They use statistics based on a stupidly large pool of data to figure out what the next character or word is. As such, No AI actually ‘knows’ how to code. It isn’t cognitively trying to solve the problem, but instead finds an output that matches the statistics of the data it has been trained on. As such, it gets it massively wrong constantly, as the AI isn’t actually trying to solve the problem you think it is. As such, even when the coding problem you are asking the AI to solve is well-represented in its training data, it can still fail to generate a usable solution simply because it doesn’t actually understand the laws and rules of the coding language. This issue gets even worse when you ask it to solve an AI problem it has never seen before, as the statistical models it uses simply can’t be extrapolated out, causing the AI to produce absolute nonsense. This isn’t just a problem with AI-generated code but every AI product, such as self-driving cars. Moreover, this isn’t a problem that can be easily solved. You can’t just shove more training data into these AIs, and we are starting to hit a point of diminishing returns when it comes to AI training (read more here). So, what is the solution? Well, when we treat AI as it actually is, a statistical model, we can have tremendous success. For example, AI structural designs, such as those in the Czinger hypercar, are incredibly efficient and effective. But it falls apart when we treat AI as a replacement for human workers. Despite its name, AI isn’t intelligent, and we shouldn’t treat it as such. [End]