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Use CasesStakeholdersDatasetsImpacts (areas for “good”, “bad”, “other”)Changes and Resulting ImpactsOther ideas, defenses, project ideas, etc
Use cases and stakeholder brainstorming (different regions of whiteboard board / different color post-it notes) [5-10 minutes + discussion] [do not be constrained to current technologies] [stakeholders include users, non-users, companies, governments, M&V populations, other countries]Datasets + incentive structure and impacts brainstorming (different regions of whiteboard board / different color post-it notes) [5-10 minutes + discussion] [datasets means “inputs to training systems” and impacts means “what happens as a result”; impacts can be “good”, “bad”, “both good and bad”, “unclear”]
Writing/summarizingCreative writing Generating social media content / newsMeeting notes & agenda makingWriting help Text summarizing Summarizing articles or other content Reading/writing assistantSummarization (downstream tasks in NLP) Dating conversationsNews generationJournalist news writing News/media reporting News generationProgrammingCreative Co-pilot CodingCode comment generationCode generationCode generation in new domains (hardware? Other misc. Boilerplate?Text-to-appGenerating latex or codeData generation (reverse prompting?) Inputting problem statement & getting out an algorithmInputting app spec and outputting app code Professional Services Legal Legal helpWriting waivers or other legal documents Legal document drafting Automated defense attorneysLegal system Making legal decisions based of court scriptWriting legal documentsGenerating privacy policy, Terms of ServicesBusiness document draftingMedicalGiven symptoms, output diagnosis (automated Web MD) Medical doctor understanding & analysis Financial/others Investment theses/RLAdvice: what should I do to get promoted?Tell me recipe given ingredientsProfessional assistant tools for doctors, CPAs, lawyersMenus, food descriptions, nutrition labels Generative tech used in other AI fields e.g., planning for debtsResearch helpIdea generation Help science communication by translating things into lay languageGenerating research ideasResearch to gauge the “average sentiment” of a topic/person, etc. Overleaf + ChatGPTFinding research methodology to useWriting background section or conclusion of research paperAcademic writingGive difficult Q+A after practice talkArts & cultureMachine generated art & literatureText-to-videoWriting songs, movies, plays, etc. Entertainment (Books/TV)Prediction of non-word features (large music models, large image models?) Content recommendation → on-demand content generation Personalized content creation/curationCompanionshipGive pets a voiceCommunication substitute Companionship systems (e.g., elderly who live alone to converse with)Metarverse dating appsTalk with deceased relativesDeceased relativePeople who want chatbot friendImaginary pets (Damagochi) Virtual romantic partnerImaginary significant otherApps that mimics interacting with celebrity Emotional support/TherapyRelationship adviceTherapy sessionsPersonalized emotional support. Therapist?Automatic therapy Talk-based therapy Sentiment: based on chats or emails from person X, what does person X think about me?EducationEducationEducationEducation, homework help Education e.g., language learning toolsChildren in school learning to {write} (Insert topic) Machine generated education curriculum Virtual school tutor/teacherHelp learn new languageInteractive QA system for kids to learn about reading & thinking Dissemination of history TranslationTranslation Translation appsLanguage translation to facilitate inter-cultural/regional communicationPracticing something in another languageInformationSearch engineSearch for knowledgeMarketingCreating marketing languageAdvertisingCustomer interactive, compelling ads Recommendation systems maybe personalized adsVirtual selfAPs artificial personas Personalized virtual representationPersonal assistant/plannerPersonal assistant Event plannersVirtual assistants / chatbotsCustomer serviceCustomer serviceGovernment benefit claims & complaintsPatient intakes systems Bank assistants Tech support scriptsAccessibility Accessibility (dyslexia, neurodivergence) Accessibility toolsHarmful contentSpam/scams Terrorism propagandaDisinformation & astroturfing Automated spam/harassmentDeep fake audio (phishing…) Sexual materialsPorn Interactive deep-fake/pornography CelebritiesPast romantic partnersStalking victimEmotionally engaging content Political persuasion Cognitive science strong generating stimuli Law enforcement Police/immigration: Interrogation assistant Government uses - summarize spy messages, search for secret informationGovernment summarize intercepted calls OthersFirewall monitoring (regex → LLM) packet introspectionCircumvent your “textual footprint” by generic LLM language insteadChild protective services interview assistantsImprove training (if there is a speech synthesizer) Online content moderationMetal model of mindsApp for “Ferris Bueller” (Help trick parents about locations) Parent’ control on child’s personal LLM
EducationChildrenChildren-parentsChildrenChild interacting on webParentsParentsTeachersTeachersTeachersTeachers, tutors, coachesTeachers-students Teachers-studentsSchool administratorsStudentsStudentsStudents-children Professionals Authors/writersWriters (journalists, marketing)Newscasters, journalistsJournalistsAll kinds of visual, digital artistsMuseum/tour guides Sex workersLibrariansReligious person (ideology in speech) Therapists, therapy clients Content creators, influencersPlanners AssistantsActorsPoliticiansChefsLawyersPhilosophersInvestorsGovernment/regulatorGovernmentsRegulatorsStandard-setting organizationsPerson at customs RegulatorGovernment as a userGovernment as a regulatorGovernmentsForeign governmentInfrastructureNetwork companiesUtility companiesCloud service / database providerNVIDIASales/marketingSales/marketing individuals Social engineersAdvertisersPeople not as a userWorkers replaced by modelsLaypeople who’re not aware the AI tools are being in usePeople contributing to training dataMalicious usersSpy who wants help blending in Person automating hate/harassment (e.g., on Twitter) Person who wants to spread fake newsTrawlers UsersCompany that uses ChatGPT as a serviceProgrammers/promptersConsumers of AI generated contentSocial media usersEnd users
Expert Panel
Instruction
March 2 (Thu) 12:00-1:30 PM CSE376
Participants: Rachael Hong, Rachel McAmis, Liwei Jiang, Kaiming Cheng, Gregor Haas, Tina Yeung, Miranda Wei, Miro Enev, Yoshi Kohno, Inyoung Cheong
[ Primary goal is to get as many different things on the board as possible – optimize for breadth of use cases, stakeholders, datasets, impacts. Use “other ideas” section to record other ideas that are worth discussing later, e.g., ideas about how to build better systems ]
Overview
In this expert panel, we are envisioning the future of generative language models and their use cases. We want to understand both what good things might be in the future but, especially for our purposes, also what bad things might be in the future. When envisioning “bad things”, our goal is to consider situations in which there are no technical or policy mechanisms in place that might prevent, mitigate, or minimize those “bad things”. I.e., even if we do not think that a certain “bad thing” might happen, for any reason (policy or technical), let’s still envision them here.
When we say “generative language models”, we are thinking of large-scale systems that use an understanding of language to generate text (written or spoken or otherwise communicated). That text might be an answer to a question, an answer to a prompt, part of a communications exchange (a dialog), or more – this session is about envisioning those possible use cases, so do not restrict your thinking only to the types of systems we currently encounter.
To do at / before start of meeting
Create the following regions on the whiteboard / wall:
Procedure:
Results
Use case
Stakeholders
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