-: Hey, I'm gonna walk you through what is ChatGPT.
This is the original AI model that everyone knows and loves.
So you've probably heard of it,
but we're gonna walk you through it anyway.
What would be the next word in this sentence?
The students open their what?
It could be books.
They could open their laptops,
they could open their exams, their minds.
There's a lot of different words
that could come next in that sentence.
And we can predict what word might come next
based on the context in which we're seeing the sentence,
and so can ChatGPT,
and that is essentially how these models work.
They have figured out the probability
of what word comes next based on all of the different words
that they've seen on the internet.
And you know, without getting too technical,
they have those probabilities baked into the model
and they can decide based on the context,
based on the prompt that you give them,
what word comes next.
And they specifically use tokens rather than words.
A token is about three fourths of a word on average.
Some differences in how they split those words up.
But the model is trained
until it can predict the next token.
And then when it knows on average what the right tokens are
to come next in the sequence,
then you can give it a new sequence
and figure out what comes next after that.
And through understanding that,
through being able to predict the next token,
you have to understand quite a lot about how the world works
because you need to know how things are associated
and therefore we get the magic
that is a large language model.
ChatGPT specifically is based on GPT-4,
which is a transformer model.
Transformer models
coming from the "Attention Is All You Need" paper
which is a famous paper,
but I'm not gonna run you through too much
of the jargon here.
It won't mean too much to you.
You just need to know is that this is,
you know, the current kind of state of the art
of this type of model.
We don't have another type of model
other than transform models that is doing as good.
But you know, scientists are working
on other types of architecture as well.
So how did they train ChatGPT?
Well, it wasn't just the transformer model.
That was pretty good on its own
and that's where we had GPT-3.
But to get ChatGPT to actually kind of make it behave
like an assistant, we had to have additional steps.
So after training the initial, you know,
predict the next token model, the foundation model,
then they had to do some fine tuning on top.
They would have humans figure out
which outputs were best and worst from different questions,
and then they would use that to reward the model.
So they would predict tokens that, you know,
humans thought were good answers to questions.
Then they had to develop a type of policy reward model,
PPO reinforcement learning.
And what that would allow you to do
is take a prompt or a sample from the dataset,
use that PPO model to generate the output,
and then the reward model calculates a reward for the output
and the reward is used to update the policy using PPO.
So this is kind of an additional set
of reinforcement lending on top.
Again, technical jargon, but all that means really
is that you have the transformer model
that's trained on the internet,
then you fine tune it based on what people find helpful
and useful in terms of responses,
and then you optimize it based on policies
so that when you get the results,
you can be sure that they're much better on average
than what you would get
if you just used the foundation model.
It was created by OpenAI,
You can access it at chat.openai.com or chatgpt.com.
They have a free version that you can try out
and the free version is pretty comprehensive and generous.
But you can also buy plus or pro version.
The pro version's $200 a month currently,
and you get some additional powerful models for that.
The features are numerous,
and we're not gonna cover all of them here,
but the main things that we find are useful day-to-day
are the chat history.
So being able to see what are the, you know,
previous chats that you've had,
just the ability to generate text
so you can ask it for a blog post
or explain how the solar system was made
or whatever it is you're trying to generate.
And then also image generation is built into ChatGPT
through Dall-E 3.
It has the ability to browse the web
as you can get up to date current information,
which is really powerful.
The models themselves are a little bit confusing.
There's lots of different models
and they change all the time.
But broadly speaking, we have GPT-4.0
which is the workhorse.
You know, the really good, useful and scalable model
that most people use for most questions.
We have some of the research preview-type models
like GPT 4.5, which is a little bit more creative.
And then you have some of the reasoning models
like 0.1, 0.3 Mini, 0.1 Pro mode,
and these are the ones where they think first
before giving you a response,
so they're pretty good for mathematical questions
or things where you need a lot of logic.
There's also custom GPTs,
and you can think of these as custom prompts
where they've given
these versions of ChatGPT set of tools they can use.
Maybe with the Expedia one, you could book a drip,
but also they have a certain set of instructions
that tells 'em how to behave.
Things I use ChatGPT for most,
I would say creating blog posts.
They have a Canvas editor now, which is quite useful.
Creating social media posts,
particularly if you wanna create them in a specific style.
It's very good at adhering to specific styles,
and then automating tasks as well.
Whenever I need to do something formal,
fill in documentation, or do my taxes
or appeal a parking ticket,
then I tend to use ChatGPT for that.