I'm going to walk you through notebook LM, which is a really useful tool that Google has released.
It feels like almost a ChatGPT moment.
It's pretty amazing how well it works.
And so at its core, what it is is just a way of sharing context with the LM, its free tool, which
is really nice.
And it has a bunch of crazy features which you'll see, but by and large, what I'm using it for is
a very quick to upload sources.
Let me see.
I have here we go, a bunch of famous AI papers.
So say, for example, I wanted to understand a little bit more about DSP.
So this is a paper that was tough to read potentially.
Right if you're not very technical.
And and it could be, even if you are to be honest, it could take a while to really understand the
things that you need.
There's a few different things that we've just done here.
First of all is like all we did was just upload that PDF and then boom, we suddenly have a summary,
right?
Like we have a pretty straightforward summary.
We also have some suggested questions of like things we could ask, but we could ask our own stuff so
we could say, what is the firm using DSP given paper?
And it makes it into a chat like you can chat with your documents.
This is not groundbreaking, right?
There are things like this exist, but it's pretty cool that you can do this for free and it works pretty
well.
But we'll get around to get into the really interesting features.
Here we go.
So this is really cool.
Now you have some answers here.
You can click and it's going to find specifically where in that PDF it's talking about these improvements.
So you can sense check it.
You know yourself which is really helpful.
And if you find this really useful you can click save.
And now you have that saved somewhere.
You can also delete the note or add more notes.
Right now a note could be a prompt.
You can think about it like this.
So you can.
Or it could be a prompt like a chat, or it could just be something that you want to know or like something
that you want to keep to yourself.
So just say this Fury could be a great way to summarize my prompts.
Or client X didn't have to.
It doesn't have a mixed mode, right?
Like you don't have to use LMS for everything.
It can be a note taking app as well.
For this project, there's a few different really cool extra features that I want to show you, and
these are the things that actually make it really powerful because even this is useful in itself.
But if you click Notebook Guide, you can do things like set up a timeline.
So if you want to kind of see how different things have been developed or whatever, then you can generate
that from the prompt, which is really cool.
You can go, you can just create like a table of contents or a study guide.
So say, for example, you wanted to study the spy and get really good at it, then you could, you
could do generate that and then take a look at it.
Here we go.
The timeline wasn't applicable for this one, so it's smart enough to know when it doesn't make sense.
But it has talked about the different people involved, which is quite nice and who they are.
It's extracted these entities, which is quite helpful.
I find that really useful.
You can also generate a briefing doc or FAQ or whatever it is.
You have the suggested questions, but the really cool thing is you can click here and generate.
You can actually generate a podcast of people talking about DSP.
So they're talking about this paper here.
And if you haven't heard one of these yet, it's pretty powerful.
I'm going to share the recording of this one in the in the module.
So you'll be able to download that and listen to it.
But you could also go check this out yourself.
I'm just going to call this the as Why Paper podcast.
And it takes a few minutes.
You don't have to stick around to like it says.
And that's the nice thing here, is that you can just let it go and it will come up with different things.
By the way, this is the study guide.
So the prompt engineering challenge is their approach to figure out what the key abstractions are.
Signatures versus prompts, teleprompters versus manual optimization.
The advantages talked about which what data sets they do tests on.
Yeah pretty interesting stuff.
It even come up with some FAQs like quizzes.
Yeah.
Really powerful.
This is, I think, already like the most powerful way to learn something that I've come across.
It's genuinely amazing.
When you listen to these, this conversation, you're going to be blown away at the quality.
Yeah.
If you want to check this is experimental.
It's relatively new.
I'm sure they're going to be making a bunch of changes.
They already made a bunch of changes recently.
One of the things you can do now is you can upload a YouTube video into here.
You can upload planned websites.
So you could actually have multiple sources in here and get it to summarize different things.
So for example, let's see if I've got another DSP paper in here or something.
Oh yeah here's Oprah.
This is one of the papers that influenced DSP.
Once that's uploaded I could just ask it like how is Oprah's cited in the DSP paper?
Or you know, what are the similarities?
Similarities or differences between and for DSP approaches.
And that you can make those comparisons between the two, which is pretty amazing.
The conversation's going to be generated soon, so I'll put that up with the with the link to this guide.
But yeah, pretty cool stuff.