---
title: "Slides: From Text to Vision to Voice Exploring Multimodality with Open AI: Romain Huet"
category: "slides"
video_id: "yJHw33cVeHo"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# 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](https://www.youtube.com/watch?v=yJHw33cVeHo)

## 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
![[assets/slides/yJHw33cVeHo/slide-001.jpg]]

OCR text:

> INNOVATION SPONSOR
> aws
> PLATINUM SPONSORS
> MongoDB.
> Google Cloud
> neo4j

![[assets/slides/yJHw33cVeHo/slide-002.jpg]]

OCR text:

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

![[assets/slides/yJHw33cVeHo/slide-003.jpg]]

OCR text:

> 01 AI Outlook
> 02 GPT-4o
> 03 What's Next

![[assets/slides/yJHw33cVeHo/slide-004.jpg]]

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]

![[assets/slides/yJHw33cVeHo/slide-005.jpg]]

OCR text:

> Programming Assistance
> Compliance Legaland CodeReview
> AndVirtual Assistants Chatbots Use Cases GPT-3 Information Searchand Retrieval
> Language Creation Content
> Communication Emailand

![[assets/slides/yJHw33cVeHo/slide-006.jpg]]

OCR text:

> images and leaningsand Interpret Summarize visuals findings Personalize content Predict trends Use Cases GPT-4 Create assistive experiences prooesses Augment existing Automate workfiows Accelerate lsunches
> concepts Explain Analyzedata
> Translatetext inrealtime Visualizedata orsystems resources Allocate

![[assets/slides/yJHw33cVeHo/slide-007.jpg]]

OCR text:

> images and learnings and Interpret Summarize visuals findings Personalize content Predict trends Use Cases GPT-4 Create assistive experiences processes Augment eoosting Automate workflows Accelerate launches Goodmorning
> concepts Explain Analyzedata
> Translatetext inrealtime Visualizedata orsystems Alocate DJ Aaxdfea

![[assets/slides/yJHw33cVeHo/slide-008.jpg]]

OCR text:

> CR
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> > GPT-40 . ——
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> terre car eee PPC NNT ot - on 7
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> Haste (ah tale moacomae) :
> ane rt ae eral

![[assets/slides/yJHw33cVeHo/slide-009.jpg]]

OCR text:

> Milcrosoft Worid'sFall
> OB World'sFair Goog World'sFair AlEngi neo4j
> VISVIVO Galileo World'sFair
> World'sFair ALER Covalent.
> sFair ee
> dby Wc Crusoe World's Fair AlEng

![[assets/slides/yJHw33cVeHo/slide-010.jpg]]

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

![[assets/slides/yJHw33cVeHo/slide-011.jpg]]

OCR text:

> ——oo Te CGR a Bt @ 7 2B wera
> ° ueers: Kemet: lem Greens 3
> Mey O 2004
> Hello GPT-40
> wet ntrorees Nthat
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> a GPT-40

![[assets/slides/yJHw33cVeHo/slide-012.jpg]]

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

![[assets/slides/yJHw33cVeHo/slide-013.jpg]]

OCR text:

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

![[assets/slides/yJHw33cVeHo/slide-014.jpg]]

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
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> (ed Ne
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> Taped asecpmoecennneenes
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![[assets/slides/yJHw33cVeHo/slide-015.jpg]]

OCR text:

> 6 wer le em ewe we ~‘~ ee OL @c a Gia a8 wer em
> [AE] | a
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![[assets/slides/yJHw33cVeHo/slide-016.jpg]]

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

![[assets/slides/yJHw33cVeHo/slide-017.jpg]]

OCR text:

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

![[assets/slides/yJHw33cVeHo/slide-018.jpg]]

OCR text:

> AIE
> Our investment areas
> Textual
> intelligence
> Microsoft
> smol.ai

![[assets/slides/yJHw33cVeHo/slide-019.jpg]]

OCR text:

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

![[assets/slides/yJHw33cVeHo/slide-020.jpg]]

OCR text:

> Our investment areas
> Textual
> intelligence
> Cheaper and
> faster models

![[assets/slides/yJHw33cVeHo/slide-021.jpg]]

OCR text:

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

![[assets/slides/yJHw33cVeHo/slide-022.jpg]]

OCR text:

> Our investment areas
> Textual intelligence
> Cheaper and faster models
> Custom models

![[assets/slides/yJHw33cVeHo/slide-023.jpg]]

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

![[assets/slides/yJHw33cVeHo/slide-024.jpg]]

OCR text:

> Co-worker agents
> Docs
> Code Repo
> Calendar
> CRM
> Email
> Multimodal AI
> AI 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.