Fallacies of GenAI by Speaker 3

In this session, we will delve into the practical challenges of deploying generative AI, addressing issues such as bias, hallucination, and the infrastructural and financial implications. We will discuss scenarios where generative AI may not be the optimal solution and clarify common misconceptions about its capabilities and usability. Additionally, we will examine the current state of Artificial General Intelligence (AGI) and its distinction from existing AI technologies. The impact of AI on the job market will be explored, along with the importance of balancing core skills acquisition with mastering AI APIs.

1. Thefallacies ofGenerative AI
Opportunities& Challenges inproductionizing

2. Introduction
WhoamI?
Overview ofSession Goals
•Fallacies ofGenerative AI
•Opportunities & Challenges
•Strategiesforproduct
•Impactsand Future ofAI
Agenda

3. TheAIPyramid
Don'tcareaboutAI
UseAI at work
IntegrateAIin
Enterprisesolutions
BuildAI
fromscratch
Big techlandscape
Opportunity
Opportunity

4. Opportunity Radar

5. Hype Cycle
Trends that would reach productivity < 2 years
•Retrieval Augmented Generation (RAG)
•GenAI Enabled applications
•GenAI Workload accelerators
•GenAI enabled virtual assistants

6. Common Fallacies
šFallacy 1: Generative AI is a Magic Bullet
šMyth: One magicpillfor all our problems
šReality: Needs specific training and fine-tuning, richness of AI comes from your data
šFallacy 2: Generative AI Understands Context
šMyth: Perfect comprehension of context and nuance
šReality: Limitations in understanding complex, nuanced queries
šFallacy 3: Generative AI is Completely Autonomous
šMyth: No human intervention needed
šReality: Requires human oversight and validation

7. Data Powers AI - Challenges
The data needed to power AI are scattered
Data are not NLP friendly
Solution: Transform your data platform as single source of truth with NLP friendly JSON schemas
Data platforms are
not built for AI
AI may not be able to chew our complex production schemas
Solution: Keep the data for AI mostly in denormalized form like a datawarehouse
Complex production
data
Duplication of data results in Bias
Solution: Qualitative assessment of dataData duplication

8. Challenges in Productionizing
Bias and Hallucination
Explanation of bias in AI models
Examples of hallucination and its
impact
Inference and Infrastructure
Technical challenges in deploying
Generative AI
Infrastructure requirements and
associated costs
Cost Considerations
Budgeting for AI development and
maintenance
Cost vs. benefit analysis

9. ProductStrategy
šNot allproducts andsolutionneedGenerativeAI
šAsuccessful Generative AIbased productexhibits
oGenerative AIis not aforce fit into theproduct
oProduct deliversvalueeven withoutGenerativeAI
oSolves areal userpainpoint or a problem
oUseof generative AI amplifies thevaluedelivered
oAutomates a complexset of operations within the
product

10. IdeatoProduct
PoC/MVPwithcommercialAPIs(ChatGPT, Gemini)
Richbusinessdomaindataset
Self hostedLLMmodels
A/B&RCwithfeedback
Fine tuning
01
02
03
04

11. Model
Landscape

12. Team
Composition
AI architect
Data Scientist
Data Engineer
Developer
Business Analyst
/ Scrum MasterQuality Analyst
Product
Manager
NLP Engineer
(Optional)
MLOps
Engineer
(Optional)

13. OtherConsiderations
šAIassistedsoftwaredelivery
šAIassistedcontentcreation
šAGI?
šAI andworkforce
šSuccess Stories
šAdobe – Generative Fill
šMicrosoft 360 – GenAI Designer

14. Recap
šOpportunities andchallenges
šYour AI Strategy

15. Questions?

16. Thank you!
Raju Kandaswamy
https://www.linkedin.com/in/rajukandasamy/
https://medium.com/@raju.kandasamy