Slides: From Text to Vision to Voice Exploring Multimodality with Open AI: Romain Huet
Source Video
From Text to Vision to Voice Exploring Multimodality with Open AI: Romain Huet
Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.
Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.
Extracted Slides

OCR text:
AWS
Y MongoDB o Cloud NeOY

OCR text:
From Text to Vision to Voice:
Exploring Multimodality
with OpenAl
RomainHuet
Head ofDeveloper Experience,OpenAl

OCR text:
01Al Outlook
02 GPT-40
03 What's Next

OCR text:
| ae Mission
We are a research and deployment
ANE company working to build
artificial general intelligence (AGI)
that benefits all humanity.
|
J eee]

OCR text:
ne
Pa ae
EY ees een
Pv Aree GPT-3 ee es i aa
Centon Use Cases pea . a ’
. Cows
il Soy BEEF mad . 7
ee aoe a Reet,
Lee eye “ ae -

OCR text:
Perepottepeep, 0 Caitmast ites
o ee
cnn
ar ae a a ue
seen a
ete Use Cases mens
ae ee Ben —

OCR text:
EX
oa
- he
sea ea OC Tn Ss o oo F
8 eae) e,
nae a , ;
creer GPT-4 ee Good morning
Ris ar Use Cases ccs
eee Tis
Sn een a aoe eS
“ eee ee ee
ee oe

OCR text:
CR
cay
GPT-40 . ——
AN ; Te
terre car eee PPC NNT ot - on 7
penn comer py C
uo dl eine Es
aed ie f - >
B GPT-4 4 ase, A Mi
ee Use Cases oon
Z ee eI a
jog E i.
ena .
nes Da ea Lol f -- ——
: ve
F yY eH,
Pe ear ae ers ;
a en ns
Saar eas 5 f r f
Haste (ah tale moacomae) :
ane rt ae eral

OCR text:
et a cee oe Whol We Ba eg Se 2 9d bb
. ana
a Pernrenoeeralll MOO ay
yo: Gaiileo World's Fair
oo ore | NK World's Fair a’ Covalent
cu
' nH Crusoe World's Fail
Tomehy

OCR text:
eo
La
Introducing GPT-4o, our new flagship model
A step towards natural
human-computer interaction
e Multimodal reasoning
e Natural conversation @ee0ee@
e High intelligence
e Improved language support
e Vision and audio
r 4

OCR text:
——oo Te CGR a Bt @ 7 2B wera
° ueers: Kemet: lem Greens 3
Mey O 2004
Hello GPT-40
wet ntrorees Nthat
ca ime.
cower A Oem
ey E — oo
den Hy
toy
we el rn’
a GPT-40

OCR text:
(GO ewer we tw mmm ee ae eee ma Rte 6 oe Berean
oe eee ey a
[are Hello GPT-40
to 3a ee
. 4
wg at
| a @ GPr4o oY

OCR text:
5 afer nia ence y
| : ee rr cers ra
AlEngineer
Fy .
World's Fair
cERTeaa Taare Oy] eee it
Microsoft

OCR text:
Oo Ct fe te vee we Ue r @ @CGLj #@Q ti aBMBtia © a8 weriaw
8
.
er 2 cond Se
aoe eines se
i pyrene Sea ae
red ried
(ed Ne
Tot ad
Taped asecpmoecennneenes
i o
_ L

OCR text:
6 wer le em ewe we ~‘~ ee OL @c a Gia a8 wer em
[AE] | a
meee a ose!
ee cS oa Se loeseed .
* wee
| —_ o
es

OCR text:
"use client":
EventsourceMessage,I
fetchEventSource,
)fro"omicrosoft/fetch-eveet-source"
inpsrt(naneid}frcaai":
AIE
inport(useCallback,useEffect,useState}fron
"react":
inprtASSISTANT_ID,DNSTRUCTIONS,MOEL}r/COStas
lnport(Toolbax)fros*./toolbex":
export interface Message
id:string:
content: stringi
role:"ur"assistant"I"tool":
nawe?:string:
status?i"rumming”|"cogletee"
export Snterface MessagePaylead
content:string:
attachents7:(fste_sd:strlng: toots:(type:string 1)11:
port default fnction usessistant( toolbox):(toolbox Toolbox))
const (threadib,setThreaiD]auseStatestring|rutt-(ntt);
wecost [sessages, setHessages]-usestate-Hessagell):
coest[isurming,setisRuening]-use5tate(faise):
cotst [inpt,setInput]-usestate()
STsFa
Worid'sFai
Microseft

OCR text:
nf
os
: ig e
7 1 han
i >a : ; , = a
f ‘

OCR text:
Our investment areas
ay ar
intelligence
| | BE Microsoft Qo?
7 ial.

OCR text:
ino
o
(o7
Cc
ov
2
co
= aferor-W]
®
8
=
GPT-3 Era GPT-4 Era “GPT Next” Future Models

OCR text:
Ourinvestmentareas
Textual
Cheaper and
intelligence
fastermodels

OCR text:
@
Cheaper and faster models
Our models will We will continue
keep getting to release
cheaper. models of
different sizes.

OCR text:
Our investment areas
Custom models

OCR text:
Aaa
ag
Harvey.
Al Legal Technology
for Attorneys
ei deHsrent SaeraU lies)
Harvey worked with OpenAl to develop a - 83% increase in factual responses
custom trained model that has extensive
domain knowledge of case law to improve - Attorneys at top law firms preferred the
answer depth and reduce hallucination rates. custom trained model's outputs 94% of the
time over GPT-4
iTerotataliel tis
Custom Trained Model

OCR text:
Co-worke ; . Oren
id } agents id ») .
Xa) a)
Docs ‘ a (os,
ce) CodeRepo + ey i Email
Multimodal Al Al Agents
Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.