A highlight from AI Today Podcast: AI Glossary Series: Augmented Intelligence

Automatic TRANSCRIPT

Hello and welcome to the AI today podcast. I'm your host, Kathleen mulch. And I'm your host Ronald schmeisser. And thanks again for joining us on the AI today podcast, as you know, we have well over hundreds of episodes and 5 plus years we've been doing this for a long time. And we've never run out of things to say. On AI today. And part of it is because we keep hearing from a lot of you our listeners who are telling us about the need to not only put AI into practice. But even to understand terms, we're still surprised maybe we shouldn't be that people are asking us about some terms that have been around and for artificial intelligence. Even data terms that have been around for decades. So we decided, hey, let's put together a glossary, just a glossary it's visible on our site. We'll link to it in the show notes, but a glossary that goes over all of the major terms and concepts you need to know about artificial intelligence. And even putting that together, there's like hundreds of terms that you really need to know. And that could be a little overwhelming. So part of what we decided to do is put together not just the glossary, but a little podcast series where we can explain each term, but sometimes a couple terms that are related in one podcast so that you could say, oh, I understand what this term means. And that can have a conversation with someone, and I can really, really know what to do. Of course, putting it into practice is a whole other thing, understanding the terminology is one thing knowing what to do with that it's another, that's what our CPA certification and training is all about, but we'll get to that later on in this podcast. Exactly. In our podcast series, we really wanted to just go over some key AI machine learning and big data terms at a high level because as we mentioned, some people just overly complicate these terms for no good reason, and I think that that doesn't help to make people less confused about some of these terms. So we wanted in our glossary and then in our companion glossary podcast series to just go over it at a more high level. Again, these podcasts will be at that high level. If you're interested in digging deeper than we encourage you to take the CPM AI training and certification and CPM AI for our listeners who have been listening to us for a while, know that that's the cognitive project management for AI. Methodology, we are big advocates of doing AI right, including following best practices, which is CPM AI methodology. But again, on today's podcast, we really wanted to just talk about some of those terms. And we will be focusing on the term augmented intelligence. Yeah, so one of the things you might be thinking about with intelligent machines is that we're really thinking about the machine. And what we want the machine to do. But machines are limited. This is, of course, they're not we don't have general intelligence. We talked about that in other podcasts and other things. So we don't have machines that can really truly do everything that humans can. At least even at the ability, even for basic tasks that humans can do. We're just really good at what we do. So the idea with augmented intelligence is instead of thinking about this machine that's going to do things perhaps on its own is, what can we do with an intelligence system if we can work together with the human to make the human better? So in many ways, it's really the machine augmenting the human intelligence. So we're trying to make the human more capable. And that's really what we mean by augmented intelligence. If you've heard this term. And the reason I want to do that is because humans are really, really good at some things. Machines are really, really, really good at some things. Let's do the chocolate and peanut butter thing and put these two things together and just make people better at what they do. And that's the whole idea of augmented intelligence. So what are humans really good at not good at machines good at not good at that together? Everything is better at. Yeah. Yeah, because that is important, right? We want to make sure we're taking the best of both. So humans are really great. We have great intuition. We have emotional IQ. We have common sense, and we have creativity. We're really creative beings. We're able to draw pictures, write poetry, sing songs, but we're not good at probabilistic thinking. Also, we're not good at dealing with very large volumes of data. If you've ever looked at spreadsheets, once they start getting past a page, I'm like, oh my goodness, my eyes are glassing over. And also humans just inherently have bias. So those are what we're not good at. But then now let's think about, well, what are computers really good at? Well, they're really good at probabilistic thinking. They're really good at dealing with very large volumes of data and information in a very quick amount of time. And they're also really good at being trained. But machines and computers are not good at intuition. They lack emotional IQ. They lack common sense. They also lack creativity. As we mentioned, they're good at being trained, so they may produce something that seems that it's creative, but they are not creative like humans. And then also machines do have bias. So if you take what's humans are great at, what machines are great at, move it together, then that's the idea of augmented intelligence.

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