You Ask, I Answer: Understanding Generative AI Capabilities?

In today's episode, Alison asks, I agree that generative AI will affect all jobs in some way, but is generative AI widely understood right now. I was under the impression that the majority isn't aware of its capabilities yet. What's your recommendation in bringing up the topic of generative AI and its implementations? Okay. The term generative AI definitely does not have the mind share of among regular people, right?

It's very much an uh an industry and marketing term. Tell someone about chat GPT, they will understand. Tell somebody about uh Dolly 2 and AI created art, they will understand. Those the individual category leaders have a lot more brand awareness than the uh than the category as a whole, right? To explain to someone transformer models or diffuser models, you're gonna get a blank look.

Like nobody knows what those are, even though those are the technological implementations of these tools. The tools themselves are the ones that are leading the awareness. Now, here's the thing. When it comes to generative AI and its implementations, like many other technologies, no one really cares about the technology outside of the technologists, right? No one cares how many billion parameters a model has or how many tokens it was trained on, or the model weights and things.

It's not of interest to the common person any more than the type of electric motor that is inside your blender, the magnetron inside your microwave, right? These are things that people don't care about because it doesn't change their implementation of the technology, right? Whether your microwave has a magnetron with you know six emitters or eight emitters, or whether it uses uh an Arduino circuit board on the inside, you don't care. You care that when you push the popcorn button, it makes popcorn and it does so without burning it. And so that's the approach that people who are advocates for generative AI need to take into account, right?

We don't think about, we don't think about the implementation of the technology. We think about the output, we think about the results. What can this thing do for me? And that is the way to be successful with generative AI. The way to be successful with encouraging adoption is to show people here's what this thing can do.

It can write this letter, it can make this picture, it can draw this graph, it can write this code. People get that. People intuitively understand, hey, this thing can make my life easier. In the same way that if you've been churning butter all your life and you suddenly see a high-speed blender, you're like, I want that. I don't know how it works, but instead of me churning butter for three and a half hours with a wooden stick, I can get it done in 10 minutes and get on with my day.

That I would say is the important thing. Now, here's the other thing. When it comes to capabilities, because uh Alicia said something really interesting. Uh, I was under the impression that the majority are not aware of the capabilities of a generative AI. Most people are not aware of their capabilities.

No one, including me, including the people writing the code and building these things, no one knows their full capabilities because the technology is too new. It's too new and it's too quickly moving and quickly changing for us to know what all the different use cases are. What we do know is that the tools are extremely good at language and language like operations. But language is such a big, broad category. Like, what are the things you can do with language?

Well, you can teach, you can give instructions, you can educate, you can entertain, um, you can communicate, you can tell somebody to um you know, drop a bomb on somebody else. You can tell somebody uh that you love them and you want to marry them. Language is so flexible because it is code that we use to program each other. And so large language models and their ability to generate language is as flexible as we are. So no one knows the full capabilities of language models.

There's a lot of speculation, uh, and there's obviously a lot of the practical use cases use on a day-to-day basis, and they're sort of the big categories, but even then you do get these things called emergent properties, which are capabilities that a system exhibits that on paper it probably should not be able to exhibit, right? If you are building a word prediction machine, which is what a language model really is, it conceptually shouldn't make any difference whether it's got you know 10,000 tokens, 10 million tokens, 10 billion tokens. But what seems to keep happening is they start developing emergent behaviors like the ability to do some basic reasoning when they they get to a sufficient amount of complexity. So we don't know what the capabilities of these models are as they get bigger and bigger and bigger. All we know is that they are getting increasingly more complex, particularly in networks of them, and what they can do, we don't know.

So it's an interesting question, it's a good question of the ways that that these tools are going to be changing things. If you want to spur adoption, it has to be around here's what it does for you and not here's how it works. So good question. Thanks for asking. Talk to you next time.

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