Guide to Understanding the Power of Causal AI

Do you feel overwhelmed with terms like Artificial Intelligence (AI), Machine Learning (ML), Causal AI, and Large Language Models (LLMs)? Well, you're not alone. These are big words in the tech world today, but they're not as hard to understand as you might think.

ChatGPT (a type of AI) goofed up and gave some bad advice about breast cancer treatment.

That doesn't mean AI is useless, though. It's great at simpler tasks, like fixing your grammar or tweaking sentences. The main problem is with how these AI models work. They're like big pattern-hunters, trying to find trends and similarities in heaps of data.

The issue? These AI models answer questions based on these patterns, not on the specifics of the problem. For instance, even if they know loads about liver disease treatments, you wouldn't want your doctor relying solely on their advice!

So, what can we do about this? One solution could be something called "Causal AI".

This lack of transparency is a big criticism of AI. But Causal AI could be the answer. It promises to shine a light on the inner workings of AI systems.

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