Mind Readings: Generative AI Lessons from Consumer Electronics

In today's episode, let's talk about small consumer electronics and what they teach us about generative AI. Things like blenders or shavers or pretty much any device. Do you need to know how consumer electronic works to get benefit from it? No. Right?

If you have a little electric shaver like this, right? You need to open it up, you push the on switch, and you shave. Do you know how this works? Maybe, maybe not. Do you care?

Probably not. Unless it stopped working. Um, do you know how it works? Again, probably not. If you were to take this thing apart, you would see there's a grill with some sharp stuff there and some more sharp stuff there, and a little head and a little motor and a little set of magnets that alternate current back and forth very quickly.

And those little magnets make this little part here, bounce back and forth, like so. And that in turn makes it shave. Why do you need to know that? You don't. To get normal use out of this device, you don't need to know that.

If it stops working, you need to know that. Assuming you want to try and fix it or at least figure out why it's no longer working. If you want to make it better, you need to know how it works. You need to know, like, oh, what kind of batteries does this take? Well, maybe if we instead of putting uh nickel metal hydrate batteries, we put lithium batteries instead.

Maybe that'll make it work better. Spoiler alert, it does. Um, if you want to know, can this be used for other purposes? You need to know how it works, right? There's a trick for example getting fuzz off of things like sweaters and and things.

You can use a razor, a regular razor, to prune them off. And you do that with this? Well, if you have no idea how this works, the answer is probably no. And the answer is no. You this is not does not function the same way that this does.

This has exposed metal blades. This one does not. So, what does all this have to do with AI? When it comes to the use of artificial intelligence, do you need to know how a transformers-based model works to get value out of a tool like Anthropic Claude or ChatGPT or Google Gemini? No, you don't.

You have to be able to write prompts and you have to be able to articulate yourself well. And these tools generally can figure out what you mean, more or less. However, if you're not getting good results out of your prompts, should it would it be helpful to know how the tool works under the hood? Yes. Yes, it would.

It's as helpful to know that these tools take words, break them into pieces called tokens, and then go and find similar probabilities for those tokens in the order that you write them and in the order that it was trained on them and the order it read them, so that it understands things like word orders and inherently has a grasp of grammar. Knowing how the model works means that you know that things like jargon are really important. You know, write for the lowest common denominator. But when you're using AI, because of the way the model works, jargon is your friend. You might say, hey, I want to write a blog post about the FAFSA, the free application for federal student aid.

Just including that word FAFSA changes how the model works when it goes to retrieve information, because it has such a niche word. No one says that word outside of the context of financial aid, which means that if you use it in a prompt, it automatically activates a whole bunch of probabilities inside the model that says, hey, we're talking about financial aid. We're not talking about beer. We're not talking about uh you know Olympic sports. We're talking about the free application for federal student aid.

Jargon is an unlock for how to use these models. But you wouldn't know that if you didn't know how the models worked. You will notice that tools like ChatGPT come with absolutely no manual whatsoever. Um, Professor Ethan Molllock likes to make the accurate observation that your garden hose that you order on Amazon comes with more directions than you get with a massive powerful tool like ChatGPT. So knowing how the tools work lets you diagnose when they're not doing what they're supposed to, and it lets you know what the limitations are as well.

If you understand the architecture of a generative AI model, whether it's a transformers-based model or a diffusers-based model, you know that in some ways it really kind of is just a really big autocomplete. Now there's more complexity to that, it's more mathematics, but conceptually, that's not an entirely inaccurate observation, which in turn means it's not magic. It's just a bunch of mathematics. Very sophisticated math, but it's still just a bunch of math, which means there's but a bunch of things it can't do because that's beyond the capability of the way it works. Knowing how they work, at least at a cursory level, gives you the that ability to say, okay, I understand how this thing works, so I understand what's going to go wrong, I understand how to make it more effective, and I understand what its limitations are.

So I'd encourage you, as the takeaway from this video, to spend some time learning how these models work so that you can overcome their limitations or work around them, so that you know why things aren't working when they don't work, and so that you know what to do to make them work as efficiently and as effective as possible. That's gonna do it for this episode. Thanks for tuning in. I'll talk to you soon. If you enjoyed this video, please hit the like button, subscribe to my channel if you haven't already, and if you want to know when new videos are available, hit the bell button to be notified as soon as new content is live.