Video

Transcript

joining us today.

Please welcome to the stage Sam

Altman.

[music]

[cheers and applause]

Good morning.

Welcome to our first ever

OpenAI DevDay.

We’re thrilled that you’re here

and this energy is awesome.

[cheers and applause]

And welcome to San Francisco.

San Francisco has been our home

since day one, the city is

important to us and to the tech

industry in general.

We’re looking forward to

continuing to grow here.

So we’ve got some great stuff to

announce today, but first, I’d

like to take a minute to talk

about some of the stuff that

we’ve done over the past year.

About a year ago, November 30th

, we shipped ChatGPT as a

low-key research preview, and

that went pretty well.

[laughter]

In March we followed that up

with the launch of GPT-4,

still the most capable model out

in the world.

[applause]

And in the last few months, we

launched voice and vision

capabilities so that ChatGPT

can now see, hear, and speak.

[applause]

There’s a lot, you don’t have to

clap each time.

[laughter]

More recently we launched DALL•E

3, the world’s most advanced

image model.

You can use it, of course,

inside of ChatGPT.

For our enterprise customers, we

launched ChatGPT Enterprise,

which offers enterprise grade

security and privacy, higher

speed GPT-4 access, longer

context windows, a lot more.

Today, we’ve got about 2 million

developers building on our API

for a wide variety of use cases,

doing amazing stuff.

Over 92% of Fortune 500

companies building on our

products, and we have about 100

million weekly active users now

on ChatGPT.

[applause]

And what’s incredible on that

is, we got there entirely

through word of mouth.

People just find it useful and

tell their friends.

OpenAI is the most advanced and

the most widely used AI platform

in the world now.

But numbers never tell the whole

picture on something like this.

What’s really important is how

people use the products, how

people are using AI.

So I’d like to show you a quick

video.

I actually wanted to write

something to my dad in Tagalog.

I want a nonromantic way to tell

my parent that I love him and I

also want to tell him that he

can rely on me, but in a way

that still has the respect of,

like, a child-to-parent

relationship that you should

have in fill I teen zero culture

and in taking a long.

I love you very deeply and I

will be with you no matter where

the path he leads.

I see so many possibilities,

I’m like, who he, sometimes I’m

not sure about some stuff, and I

feel like the actual

ChatGPT – just thinking about

giving it more confidence.

The first thing that blew my

mind was that it levels with

you.

That’s something that a lot of

people struggle to do.

It opened my mind to just what

every creative could do if they

just had a person helping them

out who listens.

So this is to represent

circulating hemoglobin –

And you built that with

ChatGPT.

ChatGPT built it with me.

I started using it for daily

activities like, hey, here’s a

picture of my fridge, can you

tell me what I’m missing because

I’m going grocery shopping and I

really need to do recipes that

are following my Vegan diet.

As soon as we got access to

Code Interpreter, I was like,

wow, this thing is awesome.

It can build spreadsheets.

It can do anything.

I discovered about – on my h

birth date.

Very friendly, very patient,

very knowledgeable, and very

quick.

It’s been a wonderful thing.

I’m a 4.0 student but I also

have four children.

When I started using ChatGPT,

I realized I could ask ChatGPT

that question, and not only does

it give me an answer, but it

gives me an explanation.

Didn’t need computer go as

much.

It gave me a life back.

I gave me time for my family and

time for me.

I have a chronic nerve pain

on my whole left half of my

body, nerve damage.

I had like a spine – brain

surgery.

I have limited use of my left

hand.

Now you can just have the

integration of voice input, and

the newest one where you can

have the back-and-forth

dialogue, that’s just like

maximum best interface for me.

It’s here!

[music]

[applause]

So we love hearing the stories

of how people are using the

technology.

It’s really why we do all of

this.

Okay, so now on to the new

stuff, and we have got a lot.

[cheers and applause]

First, we’re going to talk about

a bunch of improvements we’ve

made, and then we’ll talk about

where we’re headed Next.

Over the last year, we spent a

lot of time talking to

developers around the world.

We’ve heard a lot of your

feedback.

It’s really informed what we

have to show you today.

Today, we are launching a new

model.

GPT-4 Turbo.

[cheers and applause]

GPT-4 Turbo will address many

of the things that you all have

asked for.

So let’s go through what’s new.

We’ve got six major things to

talk about for this part.

Number one, context length.

A lot of people have tasks that

require a much longer context

length.

GPT-4 supported up to 8k and

in some cases up to 32k

context length but we know that

isn’t enough for many of you and

what you want to do.

GPT-4 Turbo supports up to

128,000 tokens of context.

[cheers and applause]

That’s 300 pages of a standard

book, 16 times longer than our

8k context.

And in addition to longer

context length, you’ll notice

that the model is much more

accurate over a long context.

Number two, more control.

We’ve heard loud and clear that

developers need more control

over the model’s responses and

outputs, so we’ve addressed that

in a number of ways.

We have a new feature called

JSON load which ensures that the

model will respond with valid

JSON.

This has been a huge developer

request, it will make calling

APIs much easier.

The model is also much better at

function calling.

You can now call many functions

at once.

It will do better at following

instructions in general.

We’re also introducing a new

feature called reproducible

outputs.

You can pass the seed parameter

and it will make the model

return consistent outputs, which

gives you a higher degree of

control over model behavior.

This rolls out in beta today.

[cheers and applause]

And in the coming weeks, we’ll

roll out a feature to let you

view log probs in the API.

[cheers and applause]

Number three, better world

knowledge.

You want these models to the

access better knowledge about

the world, so do we.

We’re launching retrieval in the

platform.

You can bring knowledge from

outside documents or databases

into whatever you’re building.

We’re also updating the

knowledge cutoff.

We are just as annoyed of all of

you, probably more than, that

GPT’s knowledge of the world

ended in 2021.

We will try to never let it get

that out of date again.

GPT Turbo has knowledge of the

world up to April 2023 and we

will improve that over time.

Number four, new modalities.

Surprising no one, DALL•E 3,

GPT-4 Turbo with Vision, and

the new text-to-speech model are

all going to into the API

today.

[cheers and applause]

We have a handful of customers

that have just started using

DALL•E 3 to programmatically

generate images and designs.

Today, Coke is launching a

campaign that lets its customers

generate Diwali cards using

DALL•E 3, and our safety systems

help developers protect their

applications against misuse.

Those tools are available in the

API.

GPT-4 Turbo can now accept

images as inputs via the API,

can generate captions,

classifications, and analysis.

For example, Be My Eyes uses

this technology to help people

who are blind or have low vision

with their daily tasks like

identifying products in front of

them.

And with our new text-to-speech

model, you’ll be able to

generate incredibly natural

sounding audio from text in the

API with six preset voices to

choose from.

I’ll play an example.

Did you know that Alexander

Graham bell, the eminent

inventor, was enchanted by the

world of sounds?

His ingenious mind led to the

creation of the graphophone,

which etched sounds onto wax,

making voices Whisper through

time.

This is more natural than

anything else we’ve heard out

there.

Voice can make apps more natural

to interact with.

It unlocks a lot of use cases,

like language learning and voice

assistance.

Speaking of new modalities,

we’re also releasing the next

verse of our open source speech

recognition model, Whisper V3,

today, and it will be coming

soon to the API.

It features improved performance

across many languages and we

think you’re really gonna like

it.

Okay.

Number five, customization.

Fine-tuning has been working

really well for GPT-3.5 since we

launched it a few months ago.

Starting today, we’re going to

expand that to the 16k version

of the model.

Also starting today, we’re

inviting active fine-tuning

users to apply for the GPT-4

fine-tuning, experimental access

program.

To fine fine an API is great for

adapting our models to achieve

better performance in a wide

variety of applications with a

relatively small amount of data,

but you may want to model to

learn a completely new knowledge

domain or to use a lot of

proprietary data.

So today we’re launching a new

program called Custom Models.

With Custom Models, our

researchers will work closely

with a company to help them make

a great custom model, especially

for them, and their use case,

using our tools.

This includes modifying every

step of the model training

process, doing additional

domain-specific pre-training, a

post-training process tailored

to a specific domain.

We won’t be able to do this with

many companies to start, it will

take a lot of work and in the

interest of expectations, at

least initially it won’t be

cheap, but if you’re excited to

push things as far as they can

currently go, please get in

touch with us and we think we

can do something pretty great.

Okay.

And then number six.

Higher rate limits.

We’re doubling the tokens per

minute for all of our

established GPT-4 customers,

so that it’s easier to do more.

And you’ll be able to request

changes to further rate limits

and quotas directly in your API

account settings.

In addition to these rate

limits, it’s important to do

everything we can do to make

it – you successful building on

our platform.

We’re introducing Copyright

Shield.

Copyright Shield means that we

will step in and defend our

customers and pay the costs

incurred if you face legal

claims around copyright

infringement, and this place to

both ChatGPT Enterprise and

the API.

And let me be clear.

This is a good time to remind

people, we do not train on data

from the API or ChatGPT

Enterprise ever.

All right.

There’s actually one more

developer request that’s been

even bigger than all of these.

So I’d like to talk about that

now.

And that’s pricing.

[laughter]

GPT-4 Turbo is the industry

leading model.

It delivers a lot of

improvements that we just

covered, and it’s a smarter

model than GPT-4.

We’ve heard from developers that

there are a lot of things that

they want to build, but GPT-4

just costs too much.

They’ve told us that if we could

decrease the cost by 20, 25%,

that would be great, a huge leap

forward.

I’m super excited to announce

that we worked really hard on

this, and GPT-4 Turbo, a

better model, is considerably

cheaper than GPT-4 by a factor

of 3X for prompt tokens –

[applause]

And 2X for completion tokens,

starting today.

[cheers and applause]

So the new pricing is 1 cent per

thousand prompt tokens and $0.03

per thousand completion tokens.

For most customers that leads to

a blended more than 3.75%

cheaper to use.

We worked super hard to make

this happen.

We hope you’re as excited about

it as we are.

[cheers and applause]

So we’ve decided to prioritize

price first because we had to

choose one or the other but

we’re going to work on speed

next.

We know speed is important,

too.

Soon you will notice GPT-4

Turbo becoming a lot faster.

We’re also decreasing the cost

of GPT-3.5 Turbo 16k, also input

tokens for 3X less, and output

tokens are two, less.

Which means 16k is now cheaper

than the previous model.

Running a fine-tune GPT-3.5 16k

version is also cheaper than the

old version.

We just covered a lot about the

model itself.

We hope these changes address

your feedback , we’re really

excited to bring all of these

improvements to everybody now.

In all of this, we’re lucky to

have a partner who’s

instrumental in making it

happen.

So I’d like to bring out a

special guest, Satya Nadella,

the CEO of Microsoft.

Music.

[cheers and applause]

Welcome.

Thank you so much.

Thank you.

Satya, thanks so much for coming

here.

It’s fantastic to be here,

and, Sam, congrats.

I’m really looking forward to

Turbo and everything else that

you have coming is, it’s been

just fantastic partnering with

you guys.

Awesome.

Two questions, I won’t take too

much of your time.

How is Microsoft thinking about

the partnership currently?

First –

[laughter]

We love you guys.

Look, it’s been fantastic for

us.

In fact, I remember the first

time I think you reached out and

said, hey, do you have some

Azure credits, we’ve come a long

way from there.

Thank you for those.

That was great.

You guys have built something

magical.

There are two things for us when

it comes to the partnership.

The first is, these workloads

and even when I was listening

backstage to how you’re

describing what’s coming even,

it’s just so different and nut.

I’ve been in the infrastructure

business for three decades –

No one has seen

infrastructure like this.

The workload, the pattern of

the workload, the training jobs

are so synchronous and large and

data parallel.

The first thing we’ve been doing

is building in partnership with

you the system all the way from

thinking from power to the DC to

the rack, to the accelerators,

to the network, and just really

the shape of Azure is

drastically changed.

And it’s changing rapidly in

support of these models that

you’re building.

And so our job number one is to

build the best system so that

you can build the best models,

and then make that all available

to developers.

The other thing is, we ourselves

are developers, building

products.

My own conviction of this entire

generation of foundation models

has completely changed.

The first time I saw GitHub

Copilot on GPT.

And so we want to build our

Copilot, GitHub Copilot, all

as developers on top of OpenAI

APIs so we’re very, very

committed to that.

What does that mean to

developers?

I always think of Microsoft as a

platform company, a developer

company, and a partner company,

and so we want to make – for

example, we want to make

GitHub available –

GitHub Copilot available,

the enterprise he diddation to

all the attendees here so they

can try it out.

That’s awesome.

We’re very excited about

that.

[applause]

And you can count on us to build

the best infrastructure in Azure

with your API support, and bring

it to all of you, and then even

things like the Azure

marketplace, building out

products here to get to market

rapidly.

That’s sort of really our intent

here.

Great.

How do you think about the

future?

Future of the partnership or

future of AI or whatever.

[laughter]

Anything you want.

You know, like, there are a

couple of things for me that I

think are gonna be very, very

key for us.

One is, I just described how the

systems that are exceeded as you

aggressively push forward on

your roadmap, requires us to be

on the top of our game, and we

intend fully to commit ourselves

deeply to making sure you all,

as builders of these foundation

models, have not only the best

systems for training but the

most compute so you can keep

pushing forward.

We appreciate that.

On the frontiers because I

think that’s the way we’re going

to make progress.

The second thing I think we both

care about, in fact, quite

frankly, the thing that excited

both shades to come together is

your mission and ours.

Our mix is to empower every

person and organization on the

planet to achieve more, and

ultimately AI is only going to

be useful if it does empower.

I saw the video you played

earlier.

That was fantastic to see

those – hear those voices

describe what AI meant for them

and what they were able to

achieve.

So ultimately it’s about being

able to get the benefits of AI

broadly disseminated to

everyone, I think is going to be

the goal for us.

The last thing is we’re very

grounded in the fact that safety

matters and safety is not

something that you care about

later but it’s something we do

shift left on and we’re very,

very focused on that with you

all.

Great.

I think we have the best

partnership in tech, I’m excited

to be working together.

Thank you for coming.

Thank you.

[applause]

Okay.

So we have shared a lot of

great updates for developers

already, and we’ve got a lot

more to come, but even though

this is a developer conference,

we can’t resist making some

improvements to ChatGPT.

So, a small one, ChatGPT now

uses GPT-4 Turbo with all the

latest improvements including

the latest knowledge cutoff,

which we’ll continue to update,

that’s all live today.

It can now browse the web when

it needs to, write and run code,

analyze data, take and generate

images and much more, and we

heard your feedback that model

picker was extremely annoying,

that’s gone starting today.

You will not have to click

around the dropdown menu.

All of this will just work

together.

[cheers and applause]

ChatGPT will just know what to

use and when you need it.

But that’s not the main thing.

And neither was price, actually

the main developer request.

There was one that was even

bigger than that.

And I want to talk about where

we’re headed and the main thing

we’re here to talk about today.

So we believe that if you give

people better tools, they will

do amazing things.

We know that people want AI that

is smarter, more personal, more

customizable, can do more on

your behalf.

Eventually you’ll just ask a

computer for what you need and

it will do all of these tasks

for you.

These capabilities are often

talked in the AI field about as

agents.

The upsides of this are going to

be tremendous.

At OpenAI we really believe that

gradual iterative deployment is

the best way to address the

safety challenges with AI.

We think it’s especially

important to move carefully

towards this future of agents,

it’s going to require a lot of

technical work, and a lot of

thoughtful consideration by

society.

So today we’re taking our first

small step that moves us towards

this future.

We’re thrilled to introduce

GPTs.

GPTs are tailored versions of

ChatGPT for a specific

purpose.

You can build a GPT, a

customized version of ChatGPT,

for almost anything, with

instructions, expanded

knowledge, and actions, and then

you can publish it for others to

use.

And because they combine

instructions, expanded

knowledge, and actions, they can

be more helpful to you.

They can work better in any

context and they can give you

better control.

They’ll make it easier for you

to accomplish all sorts of tasks

or just have more fun and you’ll

be able to use them right

within ChatGPT.

You can in effect program a GPT

with language just by talking to

it.

It’s easy to customize the

behaviors so that it fits what

you want.

This makes building them very

accessible and it gives agency

to everyone.

So, we’re going to show you what

GPTs are, how to use them, how

to build them, and then we’re

going to talk about how they’ll

be distributed and discovered.

And then after that, for

developers, we’re going to show

you how to build these

agent-like experiences into your

own apps.

First, let’s look at a few

examples.

Our partners at Code.org are

working hard to expand computer

science in schools.

They’ve got a curriculum that is

used by tens of millions of

students worldwide.

Code.org crafted Lesson Planner

GPT to help teachers provide a

more engaging experience for

middle schoolers.

If a teacher asks it to explain

four loops in a creative way, it

does just that.

In this case, it will do it in

terms of a video game character,

repeatedly picking up coins,

super easy to understand for an

eighth grader.

As you can see, this GPT brings

together Code.org’s extensive

curriculum and expertise and

lets teachers adapt it to their

needs quickly and easily.

Next, Canva has built a GPT that

lets you start designing by

describing what you want in

natural language.

If you say, make a poster for a

DevDay reception this afternoon,

this evening, and you give it

some details, it will generate a

few options to start with by

hitting Canva’s APIs.

This concept may be familiar to

some of you.

We’ve evolved our plug-ins to be

custom actions for GPTs.

You can keep chatting with this

to see different iterations, and

when you see one you like, you

can click through to Canva for

the full design experience.

So now, we’d like to show you a

GPT live.

Zapier has built a GPT that lets

you perform actions across 6,000

applications to unlock all kinds

of integration possibilities.

I’d like to introduce Jessica,

one of our solutions architects,

who is going to drive this

demo.

Welcome, Jessica.

[cheers and applause]

Thank you, Sam.

Hello, everyone.

Thank you all.

Thank you all for being here.

My name is Jessica Shay, I work

with partners and customers to

bring their product to life.

Today I can’t wait to show you

how hard we’ve been working on

this, so let’s get started.

To start, where your GPT will

live is on this upper left

corner.

I’m going to start with clicking

on the Zapier AI actions.

And on the right-hand side, you

can see that’s my calendar for

today.

So it’s quite a day.

I’ve used this before so it’s

connected to my calendar.

To start, I can ask what’s on my

schedule for today.

We built GPTs with security in

mind, so before it performs any

action or shares data, it will

ask for your permission, so

right here I’m going to say

allowed, so GPT is designed to

take in your instructions, make

a decision on which capability

to call to perform that action,

and then execute that for you.

So you can see right here, it’s

already connected to my

calendar, it pulls in my

information, and then I’ve also

prompted it to identify

conflicts on my calendar.

You can see right here it

actually was able to identify

that.

So it looks like I have

something coming up.

So what if I want to let Sam

know that I have to leave

early?

Right here I say, let Sam know I

gotta go, chasing GPUs.

[laughter]

With that, I’m going to swap to

my conversation with Sam, and

then I’m going to say, yes,

please run that.

Sam?

Did you get that?

I did.

[applause]

Awesome.

So, this is only a glimpse of

what is possible, and I cannot

wait to see what you all will

build.

Thank you and back to you, Sam.

[cheers and applause]

Thank you, Jessica.

So those are three great

examples, in addition to these,

there are many more kinds of

GPTs that people are creating

and many, many more that will be

created soon.

We know that many people who

want to build a GPT don’t know

how to code.

We’ve made it so that you can

program the GPT just by having a

conversation.

We believe that natural language

is going to be a big part of how

people use computers in the

future, and we think this is an

interesting early example.

So I’d like to show you how to

build one.

All right.

So I’m going to create a GPT

that helps give founders and

developers advice when starting

new projects.

I’m going to go to create a GPT

here.

And this drops me into the GPT

builder.

I worked with founders for years

at YC and still, whenever I meet

developers, the questions are

always about how do I think

about a business idea, can you

give me some advice.

I’m going to see if I can build

a GPT to help with that.

So to start, GPT builder asks me

what I want to make, and I’m

going to say I want to help

start-up founders think through

their business ideas and get

advice.

After the founder has gotten

some advice, grill them –

[laughter]

On why they are not growing

faster.

[laughter]

All right.

So to start off, I just tell the

GPT a little bit about what I

want here, and it’s going to go

off and start thinking about

that, and it’s going to write

some detailed instructions for

the GPT.

It’s also going to ask me about

a name.

How do I feel about start-up

mentor?

That’s fine.

That’s good.

So if I didn’t like the name, of

course I could call it something

else but it’s going to try to

have this conversation with me

and start there.

And you can see here on the

right, in the preview mode, that

it’s already starting to fill

out the GPT, where it says what

it does, it has some ideas of

additional questions that I

could ask.

It just generated a candidate.

Of course I could regenerate

that or change it but I sort of

like that, so I will say, that’s

great.

And you see now that the GPT is

being built out a little bit

more as we go.

Now, what I want this to do, how

it can interact with users, I

can talk about style here but

what I’m going to say is, I am

going to upload transcripts of

some lectures about start-ups I

have given.

Please give advice based off of

those.

All right.

So, now it’s going to go figure

out how to do that, and I would

like to show you the configure

tab so you can see some of the

things that were built out here

as we were going by the builder

itself and you can see there’s

capabilities here that I can

enable.

I could add custom actions.

These are all feign to leave.

I’m going to upload a file.

Here’s a lecture that I gave

with some start-up advice, and

I’m going to add that here.

In terms of these questions,

this is a dumb one.

The rest of those are

reasonable.

And very much things founders

often ask.

I’m going to add one more thing

to the instructions here, which

is be concise and constructive

with feedback.

All right.

So, again, if I had more time,

I’d show you a bunch of other

things but this is like a decent

start, and now we can try it out

over on this preview tab.

So I will say – what’s a common

question?

What are three things to look –

what are three things to look

for when hiring employees at an

early stage start-up?

Now, it’s going to look at that

document I uploaded.

It will also have all of the

background knowledge of

GPT-4.

That’s pretty good.

Those are three things that I

definitely have said many

times.

Now, we could go on and it would

start following the other

instructions and grill me on why

I’m not growing faster, but in

the interest of time, I’m going

to skip that.

I am going to publish this only

to me for now.

I can work on it later, I can

add more content, I can add a

few actions that I think will be

useful, and then I can share it

publicly.

So that’s what it looks like to

create a GPT.

[applause]

Thank you.

By the way, I always wanted to

do that, after all of the YC

office hours, I thought, some

day I’ll make a bot that can do

this and that will be awesome.

[laughter]

With GPTs we’re letting people

easily share and discover all

the fun ways that they use

ChatGPT with the world.

You can make private GPTs like

I just did.

Or you can share your creations

publicly with a link for anyone

to use.

Or if you’re on ChatGPT

Enterprise, you can make GPTs

just for your company.

And later this month, we’re

going to launch the GPT Store.

You can list a –

[applause]

Thank you, I appreciate

that.

[applause]

You can list a GPT there, and

we’ll be able to feature the

best and the most popular

GPTs.

Of course, we’ll make sure that

GPTs in the store follow our

policies before they’re

accessible.

Revenue sharing is important to

us.

We’re going to pay people who

build the most useful and the

most used GPTs a portion of

our revenue.

We’re excited to foster a

vibrant ecosystem with the GPT

Store just from what we’ve been

building ourselves over the

weekend, we’re confident there’s

going to be a lot of great

stuff, we’re excited to share

more information soon.

Those are GPTs, and we can’t

wait to see what you’ll build.

But this his a developer

conference and the coolest thing

about this is we’re bringing the

same concept to the API.

[applause]

Many of you have already been

building agent-like experiences

on the API.

For example, Shopify Sidekick,

which lets you take actions on

the platform, Discord’s Clyde,

lets Discord moderators create

custom personalities for, and

Snap’s My AI, a custom island

chatbot that can be added to

group chats and make

recommendations.

These experiences are great but

they have been hard to build,

sometimes taking months, teams

of dozens of engineers, there’s

a lot to handle to make this

custom assistant experience.

So today we’re making it a lot

easier with our new Assistants

API.

[cheers and applause]

The Assistants API includes

persist tents threads so they

don’t have to figure out how to

deal with long conversation

history, built-in retrieval,

Code Interpreter, a working

Python interpreter in a sandbox

environment, and of course the

improved function calling that

we talked about earlier.

So we’d like to show you a demo

of how this works and here is

Romain, our head of developer

experience.

Welcome.

[music]

[applause]

Thank you, Sam.

Good morning.

Wow.

It’s fantastic to see you all

here.

It’s been so inspiring to see so

many of you infusing AI into

your apps.

Today, we’re launching new

modalities in the API, but we

are also very excited to improve

the developer experience for you

all to build assistive agents.

So let’s dive right in.

Imagine I’m building Wanderlust,

a travel app for global

explorers and this is the

landing page.

I’ve actually used GPT-4 to

come up with these destination

ideas, and for those of you with

a keen eye, these illustrations

are generated programmatically

using the new DALL•E 3 API

available to all of you today.

So it’s pretty remarkable.

But let’s add a very simple

assistant to it.

This is the screen, we’ll come

back to it in a second.

I’m going to switch over to the

assistants playground.

Creating an assistant is easy,

you give it a name, some initial

instructions, the model, GPT-4

Turbo, and I’ll go ahead and

select tools.

I’ll turn on Code Interpreter

and retrieval and save.

And that’s it.

Our assistant is ready to go.

Next I can integrate with two

new primitives of this

Assistants API, threads and

messages.

Let’s take a quick look at the

code.

The process here is very

simple.

For each new user, I will create

a new thread, and as the users

engage with their assistant, I

will add their messages to the

threads, very simple.

And then I can simply run the

assistant at any time to stream

the responses back to the app.

So we can return to the app and

try that in action.

If I say, hey, let’s go to

Paris, all right.

That’s it.

With just a few lines of code,

users can now have a very

specialized assistant right

inside the app.

And I’d like to highlight one of

my favorite features here,

function calling.

If you have not used it yet,

function calling is really

powerful.

As Sam mentioned, we’re taking

it a step further today.

It now guarantees the JSON

output with no added latency and

you can invoke multiple

functions at once.

If I say, what are the top 10

things to do, I’m going to have

the assistant respond to that

again.

And here what’s interesting that

the assistant knows about

functions, including those to

annotate the map that you see on

the right, and now all of these

pins are dropping in real-time

hire.

[cheers and applause]

It’s pretty cool.

And that integration allows our

natural language interface to

interact fluidly with components

and features of our app, and it

truly showcases now the harmony

you can build between AI and UI

when the assistant is actually

taking action.

But let’s talk about retrieval.

And retrieval is about giving

our assistant more knowledge

beyond these immediate user

messages.

I got inspired and already

booked my tickets to Paris so

I’m going to drag and drop this

PDF.

While it’s uploading I can sneak

peek at it, typical united

flight ticket, and behind the

scene here, what’s happening is

that retrieval is reading these

files and, boom, the information

about this PDF appeared on the

screen.

[cheers and applause]

And this is of course a very

tiny PDF but assistants can

parse from documents, from

extensive texts to intricate

product specs depending on what

you’re building.

I booked an AirBNB so I’m going

to drag that over to the

conversation as well.

We’ve heard from so many of you

developers how hard that is to

build yourself.

You typically need to compute

your on biddings, set up

chunking algorithm, now all of

that is taking care of.

There’s more than retrieval.

With every API call, you usually

need to resend the entire

conversation history, which

means, you know, setting up a

key value store, that means

handling the context windows,

serializing messages and so

forth.

That complex it now completely

goes away with this new stateful

API.

Just because OpenAI managing

this API does not mean it’s a

black box.

In fact, you can see the steps

that the tools are taking right

inside your developer

dashboard.

So here if I go ahead and click

on threads, this is the thread I

believe we’re currently working

on, and these are all the steps,

including the functions Building

Coded with the right parameters

and the PDFs I’ve just

uploaded.

Let’s move on to a new

capability that many of you have

been requesting for a while.

Code Interpreter is now

available today in the API as

well.

That gives the AI the ability to

write and execute code on the

fly but even generate files.

So let’s see that in action.

If I say here, hey, we’ll be

four friends staying at this

AirBNB.

What’s my share of it plus my

flights?

All right.

Now here what’s happening is

that Code Interpreter noticed

that it should write some code

to answer this query so now it’s

computing, you know, the number

of days in Paris, number of

friends, it’s doing some

exchange rate calculations

behind the scenes to get this

answer for us.

Not the most complex math but

you get the picture.

Imagine you’re building a very

complex finance app that’s

crunching countless numbers,

plotting charts, so, really, any

task that you’d normally tackle

with code, Code Interpreter will

work great for you.

I think my trip to Paris is

sorted.

To recap here, we’ve seen how

you can quickly create an

assistant that manages states

for your user conversations,

leverages external tools like

knowledge and retrieval and Code

Interpreter and I know vocation

your own functions to make

things happen.

But there’s one more thing I

wanted to show you to really

open up the possibilities using

function calling defined with

our new modalities that we’re

launching today.

While working a DevDay, I’ve

built a small custom assistant

that knows everything about this

event.

But instead of having the chat

interface while running around

all day today, I thought, why

not use voice instead.

So let’s bring my phone up on

screen hear so y so

you can see it.

On the right you see a simple

swift app that takes microphone

input.

I’m going to bring up my

terminal log so you can see

what’s happening behind the

scenes.

Let’s give it a shot.

Hey there, I’m on the keynote

Stage Right now, can you greet

our attendees here at DevDay?

Hey, everyone, welcome to

DevDay, it’s awesome to have you

all here.

Let’s make it an incredible

day.

[cheers and applause]

Isn’t that impressive?

You have six unique voices to

choose from in the API, each

speaking multiple languages so

you can find the perfect fit for

your app.

On my laptop, you can see what’s

happened behind the scene.

I’m using whisper to converts

the voice input into text, and

the new SSI to make it speak.

Function calling, things get

even more interesting when the

assistant can connect to the

internet and take real actions

for users.

So let’s do something A even

more exciting here.

How about this?

Assistant, can you randomly

select five DevDay attendees

here and give them $500 in

OpenAI credits.

Yes.

Checking the list of attendees.

[laughter]

Done.

I picked five DevDay attendees

and added $500 of API credits to

their account.

Congrats to: (Reading name).

If you recognized yourself,

awesome, congrats.

That’s it, a quick overview

today of the new Assistants API

combined with new tools and

modalities we launched, all

starting with the simplicity of

a rich text or voice

conversation for you end users.

We really can’t wait to see what

you build and congrats to our

lucky winners.

Actually, you know what?

You’re all part of this amazing

OpenAI community here so I’m

going to talk to my assistant

one more time before I step off

the stage.

Hey, assistant, can you actually

give everyone here in the

audience $500 in OpenAI

credits?

[cheers and applause]

Sounds great.

Let me go through everyone.

[cheers and applause]

All right.

That function will keep running,

but I’ve run out of time so

thank you so much, everyone,

have a great day.

Back to you, Sam.

[cheers and applause]

Pretty cool, huh?

[cheers and applause]

So that Assistant API goes into

beta today and we’re super

excited to see what you all do

with it.

Anybody can enable it.

Over time, GPTs and assistants

are precursors to agents, are

going to be able to do much,

much more.

They’ll gradually be able to

plan and to perform more complex

actions on your behalf.

As I mentioned before, we really

believe in the importance of

gradual iterative deployment.

We believe it’s important for

people to start building with

and using these agents now to

get a feel for what the world is

going to be like as they become

more capable.

And as we’ve always done, we’ll

continue to update our systems

based off of your feedback.

So, we’re super excited that we

got to share all of this with

you today.

We introduced GPTs, custom

versions of ChatGPT that

combine instructions, extended

knowledge and actions.

We launched the Assistants API

to make it easier to build

assistive experiences with your

own apps.

These are our first steps

towards AI agents and we’ll be

increasing their capabilities

over time.

We introduced a new GPT-4

Turbo model that delivers

improved function calling,

knowledge, lowered pricing, new

modalities and more.

And we’re deepening our

partnership with Microsoft.

In closing, I wanted to take a

minute to thank the team that

creates all of this.

OpenAI has got remarkable talent

density but it takes a huge

amount of hard work and

coordination to make this

happen.

I truly believe I’ve got the

best colleagues in the world.

I feel incredibly grateful to

get to work with them.

We do this because we believe AI

is going to be a technological

and societal revolution, will

change the world in many wakes,

and we’re happy to get to work

on something that will empower

all of you to build so much for

all of us.

We talked about earlier how if

you give people better tools,

they can change the world.

We believe that AI will be about

individual empowerment and

agency at a scale that we’ve

never seen before, and that will

elevate humanity to a scale that

we’ve never seen before,

either.

We’ll be able to do more, to

create more and to have more.

As intelligence gets integrated

everywhere, we will all have

superpowers on demand.

We’re excited to see what you

all will do with this

technology, and to discover the

new future that we’re all going

to architect together.

We hope that you’ll come back

next year.

What we launch today is going to

lack very quaint to what we’re

creating for you now.

Thank you for all that you do.

Thank you for coming here