A fear of missing out and a fear of messing up : A Strategic Roadmap for ChatGPT Integration at Work
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The document outlines a roadmap for integrating Generative AI (Gen AI) and ChatGPT at the enterprise level, including implementation phases, use cases, and associated risks. It discusses the benefits of using ChatGPT, such as improved productivity and efficiency, while highlighting concerns like data privacy and ethical implications. The presentation emphasizes the need for cross-functional teams, policies, and training to ensure successful adoption and compliance.
A fear of missing out and a fear of messing up : A Strategic Roadmap for ChatGPT Integration at Work
- 1. Strategic Gen AI (ChatGPT) Integration at Enterprise-level Kevin Lee
- 2. Disclaimer The views and opinions presented here represent those of the speaker and should not be considered to represent any companies or organizations.
- 3. Agenda ➢ Gen AI (ChatGPT) Implementation Roadmap for the whole organization ➢ Gen AI & ChatGPT Introduction ➢ Risks and Concerns ➢ Benefits and Use Cases ➢ Cross Functional Team ➢ Policy and Guidelines ➢ Training and Education ➢ PoC ➢ Evaluation ➢ Discussion 3
- 4. Does your company allow ChatGPT at Work? 4
- 5. 5 A fear of missing out A fear of messing up
- 6. Gen AI (Generative AI) - Introduction - Gen AI Market Trend
- 7. What is Gen AI? Gen AI – a trained Machine Learning model that generate new contents with a simple prompt. • Text (e.g., Large Language Models) : Content Writing, Chatbots, Assistants, Search • Code : Code Generation, Data Set Generation • Image : Image Generation, Image Edit • Audio : Voice Generation/Edit, Sound creation, Audio Translation • Video : Video Creation/Edit, Voice Translation, Deepfake 7
- 8. Gen AI in AI Landscape 8 AI ML DL Gen AI
- 9. How Gen AI ( LLM) Works • So, when ‘input texts’ are prompted into LLM, LLM provide ‘response’, which show a highest probability. 9
- 10. 10 Bloomberg Gen AI Market Prediction
- 11. ChatGPT - Introduction - Development - Plan - Popularity - Why Should I use it & Why not
- 12. What is ChatGPT? • ChatGPT is an advanced LLM developed by OpenAI. • ChatGPT is trained on large corpus of text (~300B tokens). • Its main strength • the ability to generate human-like responses in various contexts. • The ability to understand and generate text in a wide range of domains. • Its application • Inquiry/prompt ( rather than search ) • Content Generation • Customer support • Interactive Storytelling • Coding • Image Analysis • Art Development 12
- 13. ChatGPT Development 13 ChatGPT4 • March’2023 • 8K tokens • 32k tokens (ChatGPT4- 32k) • ~1,760B parameters ChatGPT3.5 • April’2023 • 4K tokens (3,072 words) • 16K tokens (ChatGPT3.5-turbo-16k) • 175B parameters ChatGPT3 • June’2020 • 1,536 words • 175B parameters ChatGPT2 • Feb’2019 • 768 word • 1.5B parameters Reference Points • Tweet : 55 words • NY Times Article : 622 words • Standard Novel : 90K words • Kings James Bible : 783,137 words Each prompt include the current tokens and previous tokens.
- 14. ChatGPT Popularity ChatGPT gained 100M users within 2 months since its release. 14
- 15. Reasons that ChatGPT is not being used • A lack of understanding of ChatGPT Use Cases / Benefits • A lack of ChatGPT Usages( Prompt Engineering) • Concerns/Risks using ChatGPT 15
- 17. 17 Gen AI (ChatGPT) Implementation Roadmap Risk and Concerns Benefits and Use Cases Cross Functional Team Policy and Guideline Training / Education PoC ROI
- 18. Concerns using AI (ChatGPT) - Data Privacy & Security - Bias - Regulatory Compliance - Ethical Consideration - Black Box Feature - Resource Requirements - Lack of Expertise - Cost & ROI
- 19. 19 Data Privacy • Data Privacy – the right of a person to have control over how their personal information is collected and used. (e.g., clinical trial data) • Since ML is built using data, ML algorithms can contain sensitive information even though it is one of a Big Data. • The growth of ML has increased the possibility of using sensitive data in ways that may violate data privacy. (e.g., ChatGPT)
- 20. 20 Bias of Machine Learning • Phenomenon that occurs when ML algorithms produces results that are prejudiced due to wrong assumptions • Examples : • Amazon ML recruiting engine show a bias on women. • ML was trained to vet applicants using 10 years of resume • Most came from men, a reflection of male dominance across tech industry.
- 21. 21 Regulatory Compliance • Regulatory Compliance – the process of adhering to laws, regulations, standards, and other rules set by governments and other regulatory bodies. • For pre-market approval, AI- based software needs to follow regulatory guidelines (e.g., Good Machine Learning for Medical Device Development Guiding Principles)
- 22. 22 Ethical Consideration • Who will make decision? AI or human? • Can AI be responsible for their decision? • Is AI transparent?
- 23. Potential Benefits using ChatGPT - Higher Productivity / Efficiency - Better Patient Engagement - Patient’s Recruitment - Enhancement in Image - Market Research & Insights
- 24. ChatGPT Users vs Non-users • Results of consultants using ChatGPT • finished 12.2% more tasks on average • completed tasks 25.1% more quickly • produced 40% higher quality results 24
- 25. Bar Exam Score Performance : ChatGPT3.5 vs ChatGPT4 25 10% vs 90%
- 26. CPA Exam Performance: ChatGPT3.5 vs ChatGPT4 26 35.1% vs 85.1%
- 29. AI Implementation through Enterprise Policy and Guidelines - Data Privacy - Regulatory Compliance - Secure Data Storage - Secure Access in Gen AI
- 30. 30 Risks Enterprise-wide Policies & Guidelines Compliant AI Implementation Risk mitigation through Policies and Guidelines
- 31. 31 Employee Training • Employees who will be using and managing ChatGPT • Security best practices • Data Privacy • Data Handling Procedures with ChatGPT • Ethics and Compliance
- 33. 33 So, should we use Gen AI such as ChatGPT at work?
- 34. Discussion : Q & A