Introduction to Generative AI 3

AI that can produce high quality content, such as text, images, and audio. Link

Materials

Generative AI and business

Task analysis of jobs

Augmentation vs Automation

• Augmentation
  • help humans with a task
    • recommend a response for a customer service agent to edit/approve
• Automation
  • automatically perform a task
    • automatically transcribe and summarise records of customer interactions

Evaluating AI potential

The potential for augmenting/automating a task depends on:

• technical feasibility (Can AI do it?)
  • can a fresh grad following the instructions in a prompt complete the task?
  • assess if RAG, fine-tuning, or other techniques can help
• business value (How valuable is it for AI to augment or automate this task?)
  • how much time is spent on this task?
  • does doing this task significantly faster, cheaper or more consistently create substantial value?

New workflows and new opportunities

Teams to build generative AI Software

software engineer

• writing software applications
• ideally someone who has learned basics of LLMs/prompting machine learning engineer
• implementing AI system
• ideally familiar with LLMs/prompting, RAG, fine-tuning product manager
• identifying and scoping the project

Generative AI and society

Concerns about AI

1. amplifying humanity’s worst impulses
   • fine-tuning, RLHF
2. job loss
   • bring a huge amount of growth and create many new jobs in the process
3. human extinction
   • perfect control not needed to be valuable and safe

Artificial General Intelligence (AGI)

Definition: AI that can do any intellectual task that a human can.

Examples:

• learn to drive a car through ~20 hours of practice
• complete a PhD thesis after ~5 years of work
• do all the tasks of a computer programmer (or any other knowledge worker)

Responsible AI

Dimensions:

• fairness: ensuring ai does not perpetuate or amplify biases
• transparency: Making AI systems and their decisions understandable to stakeholders impacted
• privacy: Protecting user data and ensure confidentiality
• security: Safeguard AI systems from malicious attacks
• ethical use: Ensuring AI is used for beneficial purposes

Tips:

• Build a culture that encourages discussion and debate on ethical issues
• Brainstorm how things can go wrong
  • E.g., Could there be issues with fairness, transparency, privacy, security, ethical use?
• Work with a diverse team and include perspectives from all stakeholders

Course Summary

• How generative AI works
  • What it can and cannot do
  • Common use cases: Writing, reading, chatting
• Generative AI Projects
  • Lifecycle of a generative AI project
  • Technology options: Prompting, RAG, Fine-tuning
• Implications on Business and Society
  • Analyzing tasks in jobs for automation or augmentation potential
  • Societal concerns, responsible AI