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.
What’s the Big Deal with AI and LLMs?
AI is pretty cool – it can solve lots of problems. But it’s not perfect, especially when we get into tricky stuff like health care. A while back, 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”.
Causal AI: The Solution to AI’s Limitations?
When you ask an AI model a question, it gives an answer based on patterns it’s found in its data. This can be misleading and sometimes plain wrong.
What if there was a way to know why the AI is giving the answers it’s giving? That’s where Causal AI comes in. It aims to dig into the “how” and “why” behind an AI’s answers, making them more reliable.
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.
How Causal AI Could Change the Game
Causal AI’s main goal is to make AI responses more transparent, so we know why a decision was made.
For example, it could tell you why a bank denied your loan application, or why an AI thinks it’s a good idea to increase a car’s engine horsepower. It provides clarity and reasoning behind these decisions.
Causal AI’s Power Over Standard AI
Let’s look at a couple of situations where Causal AI could be a game-changer.
1. Jail Sentencing in the U.S.: AI can sometimes make decisions based on flawed patterns. For example, it might decide that certain races are more likely to commit crimes. But that’s just correlation, not causation. Causal AI digs deeper into the root causes like education, socioeconomic conditions, and more.
2. Treating Heart Conditions: Sometimes, AI suggests that certain races are more prone to heart diseases. But relying only on these suggestions for treatment can be dangerous. Causal AI, however, can take individual cases into account, making healthcare decisions more precise.
The Big Picture
With all this talk about Causal AI, does that mean AI is old news? Nope! AI is still really useful in areas like farming, where it can help predict pest attacks and more. But for sectors like finance, policy-making, and healthcare, we need more than predictions.
This is where Causal AI comes in. It looks at the cause-and-effect relationships in AI’s data, leading to stronger analysis and better decisions.
In the end, AI and Causal AI are a great team. They work together to help us understand and make better decisions in our complex world.
Related Articles
