I'm Mukundan Sankar, the host of Data and AI with Mukundan. And today I'm excited to bring to you another episode of Neurosymbolic AI. The last one I had done was more focused on using Neurosymbolic AI in SWOT analysis, and how that was better than the previous approach I tried.

But I did want to focus this episode specifically on Neurosymbolic AI and like what it is and why it's important to understand this concept and how I think it's going to change the future. I'm going to explain to you like it's a mystery, right? I want to get you as curious, or maybe at least a little bit as curious as I am about this. And so here goes my story.

Think you're in the, think you're deep in the world of AI. It's a hidden technology, a blend of the old and the new. It's called Neurosymbolic AI.

Some say it's the key to solving AI's greatest mysteries. But what is it really? And why does it matter? In this episode, we're unravelling the secret of Neurosymbolic AI, where brain-inspired neural networks meets logic-driven symbolic reasoning. It's a tale of conflict, harmony, and a quest for ultimate intelligence.

So let's dive in. For years, two schools of thought have vied for dominance in AI. On one side, you have this neural networks, which is massive data-driven systems and which are inspired by the human brain.

They're brilliant at recognising patterns, but they struggle with logic. Now, on the other hand, what we have is Symbolic AI. And Symbolic AI is nothing but a rule-based system which thrives on logic and reasoning, and it falters when it's faced with some kind of an uncertain situation.

So these are two titans, and they're each, by themselves, they're very incomplete, you can say. What if we have a world where they join forces? And fortunately, we do. This is where Neurosymbolic AI comes in, and it is the peace treaty in this Cold War of neural networks and Symbolic AI.

It combines the intuitive learning of neural networks and with the structured reasoning of Symbolic AI. You can think of it as a detective who's like a hybrid detective. And these days, if you're in the workforce, you know what a hybrid work situation is.

You're basically going to the office sometimes and you're working at home the other times. But coming back to this, think of it like a hybrid detective, a Sherlock Holmes and a half Dr. Watson. So either of them could not do it alone, so they combine forces.

I want to dive into the weakness of standalone AI systems. Imagine an AI that can recognise a face but can't explain why it belongs to a specific person. That's what a neural network is.

It's smart, but it's a black box. You can think of another logical AI which can list every rule for identifying a face, but it can't adapt to something it sees new, like new data. It can't adapt to that.

That is what Symbolic AI is. It's rigid. It's rule-based, so it's rigid, but it is explainable.

The question is now, how do we create an AI that is reasoning like a detective, but can also learn from the evidence it encounters? That's where Neural Symbolic AI holds the answer. So in medical imaging, I want to talk about more practical examples. In medical imaging, what happens is you have a neural network, which detects a tumour in the X-ray, but the symbolic layer explains why it flagged it.

It's based on the size, shape and historical patterns. And now if I want to think about the more reasoning aspect, that was a more vision aspect. But if I talk about a more reasoning aspect, you have in fraud detection, the neural layer which spots anomalies in the financial data, while the symbolic layer deduces which patterns are most likely fraudulent.

So this combination creates something which is extraordinary, which is like an AI which is not just smart, but also transparent, adaptable and capable of reasoning. Now Neural Symbolic AI doesn't just see patterns, it understands them. Right, and then let's think of some breakthrough cases which happened.

Now in one case, doctors used the Neural Symbolic system to diagnose rare diseases. Let's look at some breakthrough cases which happened. One I want to talk about was in healthcare.

So what happened here was doctors used the Neural Symbolic system to diagnose rare diseases. The neural network helped spot abnormalities and the symbolic layer explained the reasoning behind those abnormalities. So it gave doctors the confidence to trust the AI's decisions.

Now if I want to talk about Neural Symbolic AI's ability to learn complex rules. For example, if we take an example of autonomous driving, Neural Symbolic AI helps these cars not just recognize pedestrians, but it also helps to reason about their likely actions, whether they're standing still or about to cross. So that's another example.

And another case that I wanted to look at was Multimodal AI. So Neural Symbolic systems are excelling in combining text, images and logic, which is a multimodal system. And for example, an AI that reads a recipe, analyzes a photo of ingredients and determines if you have everything to cook the dish, right? And then, so like I said, I think this is the future.

And I strongly believe that actually, I would say. That's why I want to talk about the next thing is the future of AI. If Neural Symbolic AI can master learning and reasoning, it just can be a success to solve the hardest AI problems.

And what I mean is ethical decision making, general intelligence and trustworthiness. So these are really hard AI problems and Neural Symbolic AI can master that, I feel. But can this hybrid approach of combining neural networks and symbolic reasoning, can this hybrid approach truly scale? Or will it remain like a niche detective who's solving just rare cases? Now that's the challenge that researchers face today.

All right, so Neural Symbolic AI may not have all the answers yet, but it is a step closer to creating machines that think and learn like us. And in this ever evolving mystery of AI, it's just one of the most promising clues here. Do you think this hybrid detective could be the future of AI? Let me know and join me next time as we tackle another case in this ever expanding world of AI.

Thanks, everyone. I will see you in the next one.