In this video, Dawid Naude explains the four major changes that have recently converged to drive the adoption of AI as a main stream business strategy.

VIDEO TRANSCRIPT

I'm gonna spend about less than five minutes just giving you some grounding of wine now with AI, and then I'm gonna dive into some, not for profit specific, frameworks that we've been working on. So the first one is, you know, AI isn't new, but this AI is new.

So, this is this is very different to what it was in the past. So the large language models have really changed a lot of things.

And that trigger has led to a lot of other things changing very quickly. So the first one is that we've gone from a data scientist to being the the key person who can, do AI to moving to a software engineer, but now also business users. The fact that some power user just through being able to speak to a computer in a different way can actually be an AI engineer is a very new concept for us. This is this is net new. So any company probably has maybe one or two data scientists if they're large enough, but they might have thirty or forty software engineers, but they have a few hundred and thousands and tens of thousands of business users. So all of a sudden, we have AI everywhere.

That's led to tons of new money into this industry.

And so now there's a whole focus on trying to find the next unicorn. So we have all this explosion of, of AI innovation everywhere. And if there's three options for a CRM, you're gonna pick the one with an AI road map in strategies. So, you know, we we all get to benefit from this.

There's a science race between the large language model providers, because when the one that wins wins everything, know, to be in every platform, on every application that we use all their time. And so they've got an incentive to be the cheapest, the fastest, the most usable which we all benefit from as well. And the final one, which we've never really grappled with at all, is, you know, we can we can instruct computers now in plain English, which is very different to what we've done in the past. Now also one thing that kinda led to this being so amazing is, it happened so quick.

Like, AR went from kind of kind of crap to really good instantly.

And and when I say crap, I mean, it's it's for the typical business years. I wouldn't really feel AI except for the banks looking after our credit card, trying to prevent fraud, Google Maps, and spam filtering. But even those things have their own misses, and we were trying to build chat bots and that they all kind of sucked up until very quick up until very recently. I mean, this was two thousand and sixteen where tell Telstra had this blunder with with Cody, and I was part of this, this AI chat bot wave.

But it didn't really get much better. To be honest, I could, like, over the last eight years, I don't think anybody can really think of great chatbot experiences. Yeah. You always still end up asking for an agent.

But but all of a sudden now we can describe quantum physics to me like I'm six, and it'll do that. It does a cracking job of that. And you can say, you know, describe it to me in half as much, content, and and it'll do that. And then you can give it a full medical document and say, break it down to me so I can understand it.

And and it'll do this. This is something that I personally did when, when I had to have a scan done on my neck. And, I got a report like this that I had to take to a doctor. I didn't wanna wait.

I just put it, into chat GPT, and I was able to interpret it really, really well. And converting a sentence like the evidences of diffuse hepatic hyper attenuation to deliver as more fat than usual.