Al Bubble & Machine learning

Al Bubble & Machine lerning

Machine learning (ML) isn't new. However, the field of big data is revitalizing the subject and more organizations are relying on ML models to scale their operations, support staff in working better and faster, to uncover hidden insights from data, or even confirm and challenge underlying assumptions. This is creating widespread interest in related topics with the C-suite, and across business lines and job roles, as enterprises embrace the value of artificial intelligence (AI) and ML.

AI is everywhere now. It's in our phones, AR apps, electronics, smart devices, and even small online tools we use every day. The word "AI" has become hype. For many people, AI only means ChatGPT, and nothing more. But AI is much bigger and wider than just one tool. What we really need is a clear understanding of how to use AI the right way.

Yes, AI makes work faster and easier, but the idea that it will take all jobs is overstated. AI still needs people like developers, engineers, designers, testers, and even prompt engineers to guide it and keep it on track. AI is not a replacement for the human mind. It can still make mistakes, create bugs, or give wrong answers if we rely on it blindly.

The real bubble is not AI itself. The bubble is the belief that AI can run everything on its own.