Generative AI Summary

How generative AI works

• Can and cannot do
• Common use cases: writing, reading, chatting

Generative AI Projects

• Lifecycle of the project
• Technology options: Prompting, RAG, Fine-tuning

Implications on Business and Society

• Potential for automation and augmentation
• Responsible AI, concerns

注意：加快2x没问题🉑️（andrew ng的语速挺慢）

Generative AI For Everyone (non-tech, 手把手零基础小白ok, free if audit) 要求：零基础ok，入门级non-tech内容

• Text image audio video generation for own work+business
• E.g. ChatGPT, Google Bard, Microsoft Bing, Midjourney
• AI world – supervised learning (labelling things), unsupervised learning, reinforcement learning
• Repeatedly predict the next word

Generative LLM Applications as thought partner

• Write – brainstorm, write press release, translate
• Read – proofread, summarize an article, summarize call center conversations, reputation monitoring, sentiment analysis
• Chat – specialized chatbot,

What can they do and cannot do?

• Can a fresh college graduate to complete it with correct instructions?
• Knowledge cutoffs
• Making things up – hallucinations
• Text count limit
• Not really good with tables data
• Bias in society

Tips for prompting

• Be detailed and specific, provide context
• Guide the model to think through the answer
• Experiment and iterate
• Don’t overthink the initial prompt

Generative AI Project (E.g. Food Ordering Chatbot)

• Repeatedly find and fix mistakes to improve performance, lifecycle:
  • Scope project
  • Build/improve system
  • Internal evaluation
  • Deploy and monitor

Cheap Option 1 – Retrieval Augmented Generation (RAG) – Reasoning Engine, NOT source of information

• Retrieve relevant text (e.g. upload PDF, based on website articles) from documents to answer questions

Cheap Option 2 – Fine-tuning

• Provide datasets (e.g. in certain style or structure, speaking style)
• Gain specific knowledge (e.g. medical, financial)
• To get a small model is OK (cheaper, can run on phone)

Very Expensive – Pretraining an LLM  

Part 3: Business & Society

• Writing assistant
• Marketer brainstorm
• Recruiter summarizing comments
• Programmer initial Python draft

Replace jobs?  Automation or Augmentation (human to double check)

• Doesn’t replace jobs, but automate/speed up tasks
• Technical feasibility and business value (costly! Can save money?)
• Not just the most iconic role, can help save time and cost in some tasks

Common roles

• Software engineer
• ML
• Product Manager
• Prompt engineer?
• Impact on knowledge workers

Concerns about AI

• Concern 1: Humanity worst biased impulses
• Reinforcement learning from human feedback (RLHF)
  • Human can score the generated answer to reduce bias
• Concern 2: Job Less
• Concern 3: Harm – self-driving cars, stock crash, unjust cass in court

Artificial General Intelligence (AGI)

• Can do anything that a human can

Responsible AI