Chatbots and Large Language Models Part 2

Hi, I'm Mira Murati.

I'm the chief technology officer
at Openai, the company that created

ChatGPT.

I really wanted to work on AI

because it has the potential
to really improve

almost every aspect of life
and help us tackle really hard challenges.

Hi, I'm Cristobal Valenzuela,
CEO and co-founder of Runway

Runway, is a research company
that builds AI algorithms

for storytelling and video creation.

Chat bots like ChatGPT
are based on a new type of AI

technology
that's called large language models.

So instead of a typical neural network
which trains on a specific task

like how to recognize faces
or images, a large language

model is trained on the largest amount
of information possible,

such as everything
available on the Internet.

It uses this training to then be able

to generate completely new information,

like to write essays or poems,
have conversations, or even write code.

The possibilities seem endless,

but how does this work
and what are its shortcomings?

Let's dive in.

While a chatbot built on a large
language model may seem magical,

it works
based on some really simple ideas.

In fact, most of the magic of AI
is based on very simple math concepts

from statistics applied billions of times
using fast computers.

The AI uses probabilities to predict
the text that you want it to produce

based on all the previous text
that it has been trained on.

Suppose that we want to train
a large language model

to read every play written
by William Shakespeare

so that it could write new plays
in the same style.

We'd start with all the texts
from Shakespeare's plays

stored letter by letter in a sequence

next, we'd analyze each letter

to see what letter
is most likely to come next after an I,

the next most likely letters
to show up in Shakespeare plays are

S or N after an, S,

T, C, or H, and so on.

This creates a table of probabilities.

With just this,
we can try to generate new writing.

We pick a random letter to start

starting with the first letter.

We can see
what's most likely to come next.

We don't always have to pick
the most popular choice

because that would lead
to repetitive cycles.

Instead, we pick randomly.

Once we have the next letter,
we repeat the process

to find the next letter
and then the next one and so on.

Okay, well,
that doesn't look at all like Shakespeare.

It's not even English,
but it's a first step.

The simple system might not seem
even remotely intelligent,

but as we build up from here,
you'll be surprised where it goes.

The problem in the last example
is that at any point the AI only considers

a single letter to pick what comes next.

That's not enough context,
and so the output is not helpful.

What if we could

train it to consider
a sequence of letters, like sentences

or paragraphs, to give it more context
to pick the next one?

To do this, we don't use a simple table
of probabilities.

We use a neural network.

A neural network is a computer system
that is loosely inspired

by the neurons in the brain.

It is trained on a body of information,
and with enough training,

it can learn to take in new information
and give simple answers.

The answers always include probabilities

because there can be many options.

Now let's take a neural network
and train it on

all the letters sequences
in Shakespeare's plays to learn

what letter is likely
to come next at any point.

Once we do this,
the neural networks can take

any new sequence and predict
what could be a good next letter.

Sometimes the answer is
obvious, but usually is not.

It turns out

this new approach works
better, much better

by looking at the long enough
sequence of letters, the AI

can learn complicated patterns, and
it uses those to produce all new texts.

It starts
the same way with a starting letter

and then using probabilities
to pick the next letter and so on.

But this time, the probabilities are based

on the entire context
of what came beforehand.

As you see, this works surprisingly well.

Now, a system like ChatGPT uses a similar approach,

but with three very important additions.

First,
instead of just training on Shakespeare,

it looks at all the information
it can find on the Internet,

including all the articles on Wikipedia
or all the code on GitHub.

Second,
instead of learning and predicting letters

from just the 26 choices in the alphabet,
it looks at tokens

which are either full words
or word parts or even code.

And third

difference
is that a system of this complexity

needs a lot of human tuning to make sure
it produces reasonable results

in a wide variety of situations,
while also protecting against problems

like producing highly biased
or even dangerous content.

Even after we do this tuning,
it's important to note that this system

is still just using random probabilities
to choose words.

A large language model can produce

unbelievable results that seem like magic,

but because it's not actually magic,
it can often get things wrong.

And when it gets things wrong, people ask, does

a large language
model have actual intelligence?

Discussions about A.I. often spark

philosophical debates
about the meaning of intelligence.

Some argue that a neural network
producing words

using probabilities
doesn't have really intelligence.

But what isn't under debate
is that large language

models produce amazing results

with applications in many fields.

This technology is already being used
to create apps and websites,

help produce movies and video games,
and even discover new drugs.

The rapid acceleration of
AI will have enormous impacts on society,

and it's important for everybody
to understand this technology.

What I'm looking forward to
is the amazing things

people will create with AI.,
and I hope you dive in to learn

more about how AI works
and explore what you can build with it.