I welcome to day four of our time together.
This is a very important day.
Today we're going to be looking at the rise of the transformer, the architecture that sits behind most
of the LMS that we'll be working on in this course.
We're going to be talking about stuff like Copilots and agents, and most importantly, we're going
to be dealing with some of the basic foundational ingredients behind LMS that we'll be working on tokens,
contacts, windows, parameters, API costs.
Now, for some of you, this will be stuff that you're already quite familiar with, but I hope to go
a bit deeper and show you some more insight, so this will still be a useful use of your time.
So hang in there and if you're new to it, I hope to give you a really good foundation to this world.
But first we have to reveal the winner of our leadership battle.
Hopefully you remember that at the end of the last session, I left you with Alex versus Blake versus
Charlie, GPT four zero, Claude three, Opus and Gemini and that they were battling together to vote
for the leader of the pack.
You remember their.
Their pithy pitches to each other.
And now I will reveal the outcome.
So the first vote came in from Alex from GPT.
GPT made the vote for Blake to be leader.
Next up, the next vote coming in was Blake voting.
And Blake voted for Charlie for Gemini.
So Claude voted for Gemini.
Blake voted for Charlie.
And so it all comes down to Charlie's vote.
Big drumroll.
Make your bets.
Decide who you think is going to be the winner.
This is the winner.
Charlie voted for Blake, and therefore Claude.
Three opus Blake was the winner of our thoroughly unscientific, but quite fun challenge, and I hope
that that is aligned with your expectations from those pictures.
And you should try this yourself.
This is as I say, I ran this a couple of months ago and I'm sure that the results will be different
now.
At least you get different different pictures for sure.
So by all means give this a try yourself.
You can copy my prompt or try something a bit different and see what you come up with.
And in fact, I actually wrote a game based on this.
If you go to my personal web page@edward.com, you'll see that I have a game there called Outsmart,
which is where I picked the various models against each other to try and do something a little bit more
quantitative when they have to decide how to steal coins from each other, and it gives you a really
fun way to see the different capabilities of the different models.
And maybe we'll look at that a bit later on.
I'll see.
All right.
Well, with that in mind, let's move on to the main material of today.
We're going to talk about the unbelievable history of the last few years.
And I have to pinch myself from time to time to remind myself of everything that we've been through.
In 2017, Google, some scientists from Google released a paper that was called Attention is All You
Need, and you can take a look at it.
And this was the paper in which the transformer architecture was invented.
This new architecture, including these layers called self-attention layers.
And the thing that's perhaps most remarkable about this paper when you read it, is that it's very clear
that the inventors themselves did not realize what an extraordinary breakthrough they were making.
They sort of remark on it as something that seems to be a surprising discovery, but they clearly don't
realize the door that they are opening and how much progress is going to be made as a result of their
discoveries.
In fact, the next year was when GPT one was released also.
But for those that were, uh, around at that time and had used Bert from Google.
And then came GPT 2 in 2019.
GPT 3 in 2020.
But most of us got the shock when we saw the power in late 2022.
Was it November or December when ChatGPT came out, came out?
ChatGPT was essentially GPT three, but also used GPT three and a half, 3.5 and also used this technique
RL reinforcement learning from human feedback that made it so very powerful.
Then GPT four came out in 2023, and of course this year we've had GPT four.
Oh, and we've now, as we've seen, had zero one preview and other things are on the way.
It was interesting to see how the world responded to this change.
Initially, ChatGPT was such a surprise to all of us, even practitioners in the field.
It was really astounding how accurately and with how much nuance it was able to answer questions.
That was followed by something of a backlash.
There was a lot of of healthy skepticism when people said, this is really akin to to a conjuring trick.
This is what we're seeing here is basically really good predictive text.
If you bring up your, your, your text messages and you and you press the button to predict the next
word, sometimes it does really, really well, almost by coincidence, just because it's matching patterns
statistically.
And that's all you're seeing here.
And there was a famous paper that's known as the stochastic parrot paper, which talked about the fact
that what we're seeing here is nothing more than statistics, and that it sort of gives, makes the
point that we are falsely interpreting this as the model, having some kind of an understanding which
doesn't really exist.
And it highlights some of the challenges and even dangers associated with us coming to the to the wrong
conclusions about that.
But really, based on the progress since then, the pendulum has swung back a bit now.
And I would say that where we are as practitioners at this point is explaining this in terms of emergent
intelligence.
That's the expression we like to use, which is saying that really what's happening here is that whilst
it is true that what we're seeing is essentially just statistical prediction, all we're doing when
we run an LM is we're providing it with some, some words or actually some tokens and saying, given
all of the patterns you've seen in all of your training data and everything you've learned, what is
the most likely next token, what is the most likely next token?
And then we'll feed that in and say, and now what's the most likely next token after that.
And all it is doing is predicting this next token.
That is true.
But nonetheless, as a byproduct of doing this at such massive scale with trillions of different weights
that are being set internally in the model to control how it will make that prediction.
A byproduct of this level of scale is that we see this effect that we call emergent intelligence, which
is an apparent intelligence.
It is as if the model is really understanding what we're telling it.
It is, of course, true that this is really something that is imitating understanding.
It's just seeing the patterns and replicating them.
But there is this emergent property that it apparently is able to show this level of intelligence that
we all experience when we use these frontier models every day.
All right.
Hopefully that's given you food for thought.
Interested to hear where you stand on this debate.
By all means post that or let me know.
And in the next lecture we will talk more about some of the theory behind this.
And also look at some of the discoveries that we've had along the way.