George Pace - Keeping Pace with ChatGPT.pdf
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George Pace provides an insider's guide to understanding ChatGPT and large language models. The presentation covers many topics in less than 20 minutes including terminology, trust in chatbots, augmenting vs replacing human work, governance and safety issues, and resources for learning. Pace emphasizes that keeping pace in this rapidly changing area requires ongoing learning and that successfully leveraging large language models is a team effort that touches on many complex issues.
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George Pace - Keeping Pace with ChatGPT.pdf
- 1. Insiders Guide to “Keep Pace” with ChatGPT George Pace Keep Pace Technology June 15, 2023 Unique insights on understanding ChatGPT & LLM Ecosystems Disclaimer – No AI was used in creating this presentation Slide 1
- 2. I have experienced numerous Disruptive Technologies They all followed a repeatable pattern “No Instructions included” “No User Guide Available” How to “Keep Pace” is RARELY Provided! As a Lifelong Disruptive Technology Practitioner Hype, Excitement and FOMO Eventual “Ubiquity” Slide 2 Insider Tip – My Focus is to make this time different !
- 3. Abstracting Technical Complexities from the User Technology Holy Grail – “Simplicity” Insider Tip – Usage Proficiency is NOT the same as Ecosystem/Domain Expertise Slide 3 Web Search Smart Phone Digital Assistant Self Driving Car Large Language Models
- 4. Becoming a ChatGPT Insider User Level – ChatBot / Stand-Alone GPT Enhanced Tooling SME Level – Architecture / Prompt Engineer Implementer Level – Developer / Low-No Code Disruptor Level – Strategy / Innovation / “Think Differently” Slide 4 Insider Tip - There is NO easy – OR - Correct answer! What Level of Expertise do YOU want to obtain? 1 2 3 4
- 5. Insider Tip - It can be VERY Challenging to “Keep Pace” The Large Language Model (LLM) Universe is RAPIDLY Evolving 2023 LLM Announcements (“Foundation Models”) LLM topics I expect to see over time ! (Ecosystem Expertise) • *Model Plugins (Recent !!) • Bot Automation • Bot Personas • Industry Standards • Government Regulation(s) • Ethics / Morality • Personalization • Model Versioning • Total Cost of Ownership • Trust (Correct vs Accurate vs “Close Enough”) Slide 5 • OpenAI – ChatGPT 4.0 • Google – BARD • Facebook - LLaMA • China – MOSS • Baidu - Ernie Bot • Anthropic – Claude • Stanford - Alpaca • Together – OpenChatKit • Amazon – Bedrock • DataBricks - Hello Dolly • Cerebras – GPT3 Model • Bloomberg – BloombergGPT • Microsoft – BioGPT • HuggingFace – HuggingGPT • United Kingdom – BritGPT • Salesforce – Einstein GPT • Elon Musk – TruthGPT Insider Tip: Put more energy into understanding LLM Ecosystems vs LLM Foundation Models
- 6. “Peak of Inflated Expectations” / “Trough of Disillusionment” ALWAYS Occur Source: https://en.wikipedia.org/wiki/Gartner_hype_cycle My experience tells me the LLM Ecosystem is somewhere in this area With more usage – Limitations WILL be Encountered Don’t take my word for it People are begging to be disappointed (ChatGPT4) and they will be“ OpenAI CEO Sam Altman Slide 6
- 7. Being a ChatGPT “Insider” requires LOTS of Knowledge Terminology BUCKLE UP - Topics I will touch upon in LESS than 20 Minutes ! Visualizing ChatGPT Slide 7 Governance / Safety / Regulation “Sentience” Augment vs Replace ChatBot Trust Right vs Accurate vs Close Enough Skillset Options Explainable AI How to “Keep Pace” Use Cases Resources Insider Tip: Expect the range of LLM topics to evolve/grow
- 8. Large Language Model “Sentience” LLM Prediction / Lookahead is NOT Sentience Slide 8 Insider Tip: Sentience is a Red Herring Topic – Don’t waste time on it Human Feedback Prompt Engineering TIME investigation found OpenAI used outsourced Kenyan labor to make ChatGPT less toxic Users need to massage prompts to get the most out of LLM Models Hallucination Models return “made up” results
- 9. Terminology - Words Matter ! Hallucination Few Shot Learning Knowledge Models Large Language Models Parameters Transformers Retrievers Explainable AI Prompt Engineer Context Window Reinforcement Learning from Human Feedback (RLHF) Slide 9 Supervised / Unsupervised Learning Sentience Generative AI Cognitive Architecture Prompt Chaining / Chaining Tokens Stochastic parrots Trust ( What I see as a KEY Topic ) Insider Tip: Knowledge of AI Terminology will make or break you in conversations Foundation Models Singularity Embedding Vectors
- 10. Engagement Sources Large Language Model (LLM) GPT 3 ( Similarity ) Generic GPT Data Sources Custom Data Sources Visualizing Large Language Ecosystems Data Bias Cleansing GPT Ecosystem as of April 07, 2023 Query Response How results are delivered to the user (Generator) Data Validation Applications Digital Assistants Chatbots User “Query” The decomposition of the user request (Retriever) ChatGPT / Model Implementation User Personalization How interactions are formatted for the requestor LLM Persona How is the returned results formatted for the requestor Regulatory Alignment External Data & Services Slide 10 Generic vs Custom Models / Open vs Closed Platforms LLM Hardware (SaaS vs Server vs Phone vs Raspberry PI ) LLM Plugins Context Window The depth/knowledge of the interaction Large Language Model (LLM) Construction Robots Coding Automation Reinforcement Learning Realtime GPT Data “Static GPT Model / Data” Insider Tip: LLM Ecosystems are complicated – A Diagram is worth MORE than a Thousand Words ! Conversation History / Cache www.keeppace.com
- 11. Legal Copyright Lawsuits Career / Skill Opportunities Do not assume ChatGPT is “JUST” Technology Constitutional AI Rules for AI Self Training Data Bias Remediation Safety / Ethical AI Proper Bot Output Slide 11 AI “Whisperer” Helping Others Leverage Regulation Industry Compliance Insider Tip: Successful LLM Implementations are Team Efforts !
- 12. “Explainable” AI Getting Closer – But still not there ! Slide 12 How EXACTLY did the Model Arrive at this answer? Citations to the underlying content Sample created using Bing and Microsoft Edge Browser Insider Tip: As Hallucinations are still possible – You should always validate the results
- 13. Visualizing ChatBot “Trust” Slide 13 Exactly like Human interactions – But completely different !! Knowledge Personalization Personalization Model “Knowledge” Personalization Model” Knowledge” Human Friend ChatBot #1 ChatBot “N” Human Stranger Knowledge Personalization Engagement Etiquette Engagement Etiquette Insider Tip: Chatbot Trust is an unexplored topic – Expect frustration for Early Adopters Situational Expectations Previous Experience Your Knowledge / Bias Your “Trust” Profile Context Window Context Window Conversation Length Conversation Length Two-way Trust Your Human “Conversation” Knowledge Personalization Etiquette Conversation Length Work Associate Personalization Model” Knowledge” Engagement Context Window ChatBot #2 Your Chatbot “Interaction” Human Bias Experience Human Bias Experience Human Bias Experience Data Bias Experience Data Bias Experience Data Bias Experience One -Way Trust www.keeppace.com Chatbot Trust as of April 18, 2023
- 14. What are YOUR Expectations for AI Answers? Right, Accurate, Close Enough or Confidently Wrong? Slide 14 Right The information provided is completely correct and free of errors Accurate The information is precise and conforms to the facts or reality Insider Tip: Focus on Situational Awareness / Decision Magnitude ( Key to establishing Trust !) Close Enough The information provided is nearly correct/accurate but may have errors or imprecision Confidently WRONG The MODEL might “think” it is correct – and the answer may look correct – but can you TRUST it?
- 15. Augment vs Replace Considerations Slide 15 Will likely evolve to include many of the issues I covered in this presentation Insider Tip: That which SHOULD be automated – WILL be automated Augment Replace • Problem Solving • Enhancement • Ideation • Repetitious Tasks • “Good Enough” activities
- 16. AI Governance / Safety Slide 16 Constitutional AI Country Regulation AI Principles (2017 - Asilomar) Laws AI Detection Tools Insider Tip: With Great Power comes Great Responsibility Data Bias Security / Privacy
- 17. Your ChatGPT Journey - “Where do YOU start ?” User Level – Learn to maximize GPT Interactions SME Level – Cognitive Architectures / LLM Futures Implementer Level – ChatGPT Application Integration Disruptor Level – Opportunistic Utilization / Disruption Slide 17 What Level of Expertise do YOU want to obtain? 1 2 3 4 Insider Tip: As this space is moving at light speed – waiting is NOT a recommended strategy ”
- 18. Education Resources Don’t be Shy – Give it a Try AI Tooling Sites https://bestwebbs.com/ Slide 18 Feeds TLDR / Google Alerts Discussion Groups Facebook / Linkedin Insider Tip: You need to invest at least 30 Minutes a DAY to this effort Community Sites https://huggingface.co/ GPT Research Whitepaper's https://arxiv.org Intro to Prompt Engineering https://learnprompting.org/
- 19. The key “Takeaways” to Keep Pace Slide 19 This will be an Exciting Journey – BUT at times VERY Uncomfortable This will be hard to do on your own – Think “Buddy System” Understand how GPT/LLM will impact your job/career With Rapid Growth – Comes Rapid Confusion Insider Tip: Technology Moves Fast – You can Move FASTER – If you “Keep Pace” with Technology Focus on the ChatGPT Experience – Don’t just mimic examples
- 20. How to “Keep Pace” with my GPT Journey www.twitter.com/keeppace www.youtube.com/keeppace www.keeppace.com www.facebook.com/keeppace https://www.twitch.tv/keeppace https://www.linkedin.com/in/keeppace/ Streaming Channels Video Repository https://keeppace.substack.com/ Slide 20 Every Sunday at 9AM EST ! All Previous Webinars