Models, Harnesses, and Multi-Agent Systems

**SPEAKER_1** (0:02)
Welcome to the Practical AI Podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work and create. Our goal is to help make AI technology practical, productive and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place.
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**Daniel Whitenack** (0:41)
Welcome to another episode of the Practical AI podcast. This is Daniel Whitenack. I am CEO at Prediction Guard and I'm joined as always by my cohost, Chris Benson, who is a principal AI and autonomy research engineer. And on these episodes where it's just the two of us, no guests, we like to take a moment to really sometimes talk about just a topic we want to talk about and learn about, sometimes be more educational, dig into specific details. And I think that's more of the direction we wanted to go today, right, Chris?

**Chris Benson** (1:17)
It is. And actually, we've done several of these recently. And I think it's important to call out if you've been listening to some of the recent episodes, there's so much happening right now that one of the priorities for us in the show is to try to help people, you know, kind of really stay up with what's going on, not just from a newsy standpoint. In a practical way. In a practical way so that you can actually go do this yourself in your organizations. And we kind of realized that while there are some, like, we always have a huge funnel of guests to come in, we realized we needed to kind of pause and take a few episodes and try to just get people caught up to where they need to be because so much is happening right now.
And so that's a big part of why we're doing this right now. And so we hope this is going to be helpful for a lot of folks out there.

**Daniel Whitenack** (2:07)
Yeah, this is always evolving, and the way people use terms is always evolving. So it's useful to even sometimes redefine some of those terms. And I promise we won't belabor this point too long, but I do think it's worth us defining a few terms here up front. So we're just all on the same page. We're talking about the same thing. And some of these we've defined before in other episodes, like you said. But again, the semantic variance of terms changes over time, and it's worth just updating and reminding. So the first term obviously is an AI model or a model.
And very often, and this actually hasn't changed, although maybe some of the things we refer to as models have changed, but an AI model is really, if you're more coming from like the technical side, you might think about this like a function or a software function. Maybe if you're coming from another angle, really it's just a transformation of data, right? Like I give you an image and you tell me what's in the image. Transforms to objects in the image. That's an object recognition model, right? Or I give you a series of words and you tell me what word should come next. That's a large language model. It's a transformation of input to output. It's a function implemented in software. And these models generally do a single type of transformation. So a large language model does that transformation that I mentioned. It takes input words or text and then generates text output.
Language vision model changes the input side changes. So it might take language or text plus images or just images and then produces text output. An image generation model, as the name might suggest, takes in text and goes the other way. It generates an image out the other side of function or data transformation. There's basically any combination, just think of the model as that data transformation. Then you could have a lot of things on the input side, you could have a lot of things on the output side. Depending what type of data is coming out on the input side, coming out on the output side, we refer to these as different types of models. An LLM, a language vision model, an image generation model, a video generation model, a forecasting model, an autoregressive model in that case, an anomaly detection model, et cetera, et cetera. There's all sorts of types of models.
And not all these are neural network based models. A lot of the ones we typically think of today are, but a model itself could be composed of guts that take a variety of forms. And the way that that function does the transformation, that could take a variety of forms. But at the end of the day, it's a function or a data transformation.