Mind Readings: The Future of AI Models

In today’s episode, I discuss the future of AI and how it relates to the concept of specialization. Just like a car that tries to be sleek, rugged, and spacious all at once ends up looking strange and unappealing, monolithic AI models that aim to do everything fall short of greatness. The future lies in task-specific AI models, similar to individual apps on a smartphone. By fine-tuning and purpose-building models for specific tasks, we can achieve exceptional results. Join me as I explore this exciting direction for AI and its implications for businesses and marketers. Hit that subscribe button if you enjoyed this video!

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Mind Readings: The Future of AI Models

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Christopher Penn 0:00

Imagine going to a car dealership and saying, Here’s what I want.

In a car, I want it to be a sleek and sexy car like a sports car.

But But I want it to be rugged, like an SUV.

And actually, I want to have a lot of cargo capacity like a van.

And it’s got to have good fuel efficiency.

So maybe it should be electric or hybrid.

Oh, and I want to be safe.

So maybe you should have armor, right? So this bullet resistant? What kind of vehicle would you end up with? With that hybrid list of Frankenstein characteristics, right, you would get a really weird looking car.

And in an attempt to be everything to you, you’re probably going to look at going you know what, I don’t really want to buy that I think I will just go and buy a sports car separately and I and a van separately and maybe a camper separately.

I don’t want all that.

Can you imagine a cell phone or a smartphone? We open it up with just one app.

And the manufacturer is like yeah, this app does it.

All right, play games, it can take voice notes, it can make calls, it can play podcasts, and you’d be like, I don’t know, it’s going to do all that really well.

And it’s going to be kind of a nightmare to navigate.

Can I just have a regular phone or I can install my own apps? So what does this have to do with AI? The future of AI is having in particular language models everywhere, but not having to be monolithic.

Right now in the early stages we are, we are sort of at the the apple two plus era of language models where there’s one computer, right that you can get.

And you’re limited to what that can do.


There’s not a lot of variety right now, which is not technically true.

But for this analogy, it more or less is we’re operating in the AI space with the idea of very large monolithic models that try to be everything to everybody, right, it can write poetry and songs and rewrite content and translate and do blog posts and, and all these things.

And they’re good.

They’re good at what they do.

But they’re not great.

They’re not Pulitzer Prize winning, they’re not going to win Emmys, or Oscars.

And the reason for that is that is the same reason that you’re not going to buy a car that’s fully efficient, heavily armored, sleek, and, and you know, carries a lot of cargo.

You can’t have it all in one thing.

So the future of AI the future of generative models in particular, is going to be fine tuned task specific models.

Think of AI software models, as apps, right, you don’t have one app for everything, you have individual apps, you have Angry Birds to play Angry Birds, you have candy crush to play Candy Crush, you have Microsoft Word to write stuff in Word, you have the Gmail app to check your Gmail, that is the direction that AI is likely to go, probably will go.

And it’s a good thing for it to go because one of the challenges of ever bigger models is that they are ever more expensive computationally to run.

Today, as we are now seeing in the open source space where these these models are fragmenting into 1000s of use cases, an individual model can be very small, very light, very efficient at doing one thing.

It’s not good and other things anymore, right? It has been specialized.

But the one thing it’s good at, it’s getting really good at this one model on Hugging Face called Karen, the editor.

Karen, the editor only does one thing, grammar, spelling, punctuation and repairs to text right? Can the other does not right.

It does not rephrase it does not summarize, it just fixes text.

And its existence very efficient, because it only does that one limited set of tasks.

This is how to think about the future of AI.

So that we have individual applications that use AI in the same way that we have individual pieces of software today that do different things, right, Adobe Photoshop and Microsoft Word, two totally different things.

And you really wouldn’t want them trying to do what the other does, because it wouldn’t go very well.

But you will have that commonality of the AI model within every one of these tools.

And this is already happening today.

As of the time I’m recording this.

Adobe Photoshop has a language model and to do generative fill.

Adobe Illustrator just announced a generative model where you can recolor your illustrations.

Microsoft has announced co pilot for Microsoft Office that will have generative modeling within the entirety of the Office Suite.

Every one of these models is going to be different separate and and tuned towards specific tasks.

So if your company or your marketing group or you are thinking about how am I going to integrate AI so that I I can remain competitive, I can offer great benefits to people.

Don’t worry about trying to build the biggest, baddest, hugest, most flexible machine.

Think instead, how can I fine tune in purpose build a model that just does one thing really, really well as the best at that thing, and then deploy it.

So that’s the future of AI models.

Thanks for tuning in.

We’ll talk to you next time.

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