Gartner+超越炒作：ChatGPT和生成式AI的企业影响

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BeyondtheHype:EnterpriseImpact

ofChatGPTandGenerativeAI

FrancesKaramouzis

DistinguishedVPAnalyst

BernElliot

DistinguishedVPAnalyst

ErickBrethenoux

DistinguishedVPAnalyst

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Months

5Months

10Months

24months

TimetoMillionUsers

ChatGPT

5Days

Instagram

Spotify

Facebook

Twitter

2.5

Source:Statista

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Understanding

ChatGPT,LLMs,

FoundationalModels

andGenerativeAI

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Foundationmodel

Definitions

Foundationmodels

Largeamount

ofunlabeled

data

TransformerAlgorithm

LargeLanguageModels(LLMs)

TransformerAlgorithm

Largeamountofunlabeled

text-based

data

LLM

GenerativeAI-GenerativeAIreferstoAItechniquesthatlearnarepresentationofartifactsfromdataandmodels,anduseittogeneratebrand-new,completelyoriginalartifactsthatpreservealikenesstooriginaldataormodels.

Foundationmodel-Afoundationmodelisalargemachinelearningmodeltrainedonaverylargeamountofunlabeleddatausingatransformeralgorithm;thistraining,augmentedbyarangeoffine-tuning(adapter)mechanisms,resultsinamodelthatcanbeadaptedtoawiderangeofapplications.

LargeLanguageModel(LLM)–anLLMisatypeof

foundationmodelspecificallyfocusedonnatural

language.

ChatGPTisaconversationalapplicationbuiltontopofanLLM(inthiscaseOpenAI’sGPTmodel).

Programming&DataAssets

NaturalLanguageDesignsStrategiesTactics

•Unprogrammedgame

strategies

•Unanticipatedstrategicscenarios

•Trainingdatae.g(events,

POS,behavior)

•Test&SyntheticData

•Code

•ContextDatae.g.MarketConditions

•Questions/Answers

•Summaries/Roundups

•Stories/Narratives

•Documents

•JobListings

•ProductDesigns

•Materials

•3Dmodels

•Gameworlds

•Processflows

•Schematics/Specifications

e.g.drugs,Infrastructureandbuildings,filmedits...)

•2D/3D

Faces/Bodies

•Drawings/Photographs

•Art

•Voice/visualcharacters

•Music

WhatCanWeGenerate?

GenerativeAI

LearningMethods

Artifacts

Audio/Visual

•Rules/Inferences

•Reinforcementlearningmodels

•Alternativelearningmechanisms

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Example:GenerativeAI

/blog/emergent-tool-use/

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GartnerUseCasePrismforGenerativeAI

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TheChallengesAre

NontrivialforEnterprise

Deployments

Benefits&RisksofFoundationModels

BENEFITS

•PotentialforLowerCostofEntry

•DomainAdaptation

•Versatility

•Accessibility

•Ecosystem

RISKS

•DomainAdaptation

•CopyrightIssues

•ConcentrationofPower

•Hallucination

•PotentialforMisuse

•Opaque(blackbox)

DeeperDive

ChatGPT

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Textinput/output

*Enterprisecontrolscanonlybeappliedhere:

ConceptualFlowoftheChatGPTService

OUTPUT:

acceptabilityfiltering&conv.preparation

INPUT:filtering,

promptpreparation

(&conversation)

Session

Context

Chat/

Prep

ClosedGPT3Model

LLM

ClosedversionofGPT3trainingdata&process

RLHF:Reinforcement

Learning&HumanFeedback

training

Manage&Operate(notrealtime)

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•UsesChatGPTserviceas-is,nodirectaccesstoGPT3.5model.

•Pro–Fasttomarket,limitedinvestments,gainexperience.

•Con–Limiteddifferentiation,controlrangeislimited.

•Toolstocreate,tune,andevaluatepromptinputs&outputs.

•Pro–BettertargetedChatGPT&GPT3results,lowstartupcosts.

•Con–Mustintegratewithbusinesssystemstointroducedata.

•Leverage(build/license)GPTorotherlanguagemodelsdirectly.

•Pro–Customizeoroptimizedmodels,data,parameters&tuning.

•Con–Requiresaddedfundingandskills.ThisisnotChatGPT.

GPT3Model

DeploymentApproachesforGPT&ChatGPT

1

OutoftheBoxModelUsage

ChatGPT

PromptEngineering

2

3.LeveragingCustomModels

CorpusofData

2.UsesearchTerm(s)

1.Pre-processRequest

3.Submittopresultsvia

Prompt

5.Checkandpresentresults

LargeLanguageModel

4.LLMSummaryOutput

UsingsearchbeforeLLMprompting

UserInterfaces

GoogleAssistant

Amazon’s

Alexa

Legend

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Softwaresupporting

LLMInteraction

ChatLLMscanproducea“draft”outputoftext,whichisthenreviewedbytheuser.ChatLLMscanproducethelengthandstyleoftextdesired..

Enablesuserstolocateanswerstoinput,basedondataandpromptinformation.UsableforoutofboxQ&A.

Textmodifiedtosoftenlanguageorprofessionalizetext,forexampletakingformaldocumentandmakingaconversationaltextforemailorachat..

Offersshortenedsummariesofconversations,articles,emailsandwebpagestospecificlength.Conversionoftexttobulletpoints.

Creationoftitles,outlines,andextractionofkeycontentfromothercontentorsummaries.

Reviewcontentandclassifyintodefinedareas.Forinstance,classificationbasedonsentiment,topic,requirementorintent.

ExamplesofLLMUsageAreas

Contentcreation

&augmentation

Q&Aand

discovery

Toneofcontent

Summarization

Simplification

Classificationofcontent

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CustomerService

Healthcare

Chatbotsandapplicationscanprovidesimplelanguagedescriptionsofmedicalinformationandtreatmentrecommendations.

Improvechatbotintentidentification,summarizeconversation,answercustomerquestionsfromasearch,directingcustomerstoresources.

Createpersonallearningexperience,liketutors.Generatelearningplansandcustomlearningmaterial.

Education

Sales&Marketing

Writecodefromprose,convertcodefromoneprogramminglanguagetoanother,correcterroneouscode,explaincode.

SoftwareDevelopment

Engagewithpotentialcustomersonwebsiteorinachatbot.Providerecommendations.Provideproductdescriptions.Customizeemails.

IndustryExamples

Personal

Assistants

Manageschedules,summarize

emails,composeemails(and

chains),replies,and

summaries,draftcommon

documents.

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VendorLandscape

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ContentCreation

JasperAI,Writesonic,Rytr

Workforce

Productivity

Supernormal,Cogram

KnowledgeMgmt.

Sana,Algolia,Glean

Metaverse

Replikr,Tavus

Software

Engineering

GitHub,Tabnine,Replit

Biotech

InsilicoMedicine,Exscientia

OSSFMs

ProprietaryFMs

StabilityAI

EleutherAI

HuggingFace

Databricks

DeepMind

DistilBERT

Meta

ZhipuAI

XLNet

GenerativeAIProviders

Applications

OpenAI

GoogleAI

Cohere

Alibaba

Group

Anthropic

Baidu

Microsoft

AI21Labs

Tencent

VendorChoicesNeed

to“Fit”Together

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FutureDirections

WhatdoesGartner

seeastheenterprise

trajectories?

Bern’s

EnterpriseTrajectories

By2025,30%ofenterpriseswillhaveimplementedanAI-augmenteddevelopmentandtestingstrategy,upfrom5%in2021.

By2026,generativedesignAIwillautomate60%ofthedesigneffortfornewwebsitesandmobileapps.

By2026,theroleofdesignstrategist—ablendofthecurrentrolesofdesigneranddeveloper—willlead50%ofdigitalproductcreationteams

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Source:

Predicts2022:GenerativeAIIsPoisedtoRevolutionizeDigitalProductDevelopment

11January2022-IDG00757883

ByBrentStewart,VanBaker,JoachimHerschmann

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Erick’s

EnterpriseTrajectories

Revengeofthesoftwaregreasemonkeys:theincreaseduseoffoundationmodelswillshifttheskillsemphasisfromAIexpertstotraditionalsoftwareengineering,validation&testing.

TheexplosionofadaptermodelswillacceleratethedevelopmentandadoptionofCompositeAImodelsbyanorderofmagnitudeinthenext3years.

Bymid-2024,DecisionIntelligencewillhavesupersededtheGenerativeAI'shypewave.

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