Deciphering AI: Human Expertise in the Age of Evolving AI

1. Australia’s National Science Agency
Deciphering AI:
Human Expertise in the Age of
Evolving AI
Dr/Prof Liming Zhu
Research Director, CSIRO’ Data61
• Expert, OECD.AI – AI Risks and Accountability
• Expert, ISO/SC42/WG3 – AI Trustworthiness
• Member, National AI Centre (NAIC) Think Tank
All pencil drawings in this presentation are created by AI

2. • Australian government
• “An engineered system that generates predictive outputs such as content,
forecasts, recommendations or decisions for a given set of human-defined
objectives or parameters without explicit programming. AI systems are designed
to operate with varying levels of automation.”
• EU AI Act
• “Software that is developed with one or more of the techniques and approaches
listed in Annex I and can, for a given set of human-defined objectives, generate
outputs such as content, predictions, recommendations, or decisions influencing
the environments they interact with.”
AI Definition – Examples
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3. • Purposes & Requirements
• AI governance/regulation: under/over-inclusiveness, flexibility, practicality…
• Business transformation: applicability, measurability, clarity…
– Executive Education: skills, culture, governance, ethics...
• R&D, Public understanding…
• Definition types
• Capabilities: human-like; reasoning, learning, perception, communication..
• Application: generate contents, recommendations, decisions…
• Approaches: rule/logic-based, (un)supervised machine learning…
• …
AI Definitions – Fit for Purpose
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4. • Example - Fraud detection
• Data->Features: transaction time/amount/frequency, account age, geolocation…
• Rule/logic-based
• data, feature, data -> rules, feedback, + AI helps manage/derive complex rules
• Machine learning (learned model: Y=weightsi*Xi+ b & human-designed learning algorithm)
• Supervised: labelled data, features, AI learns rules, feedback
• Unsupervised: no labelled data, features, AI learns rules, feedback
Approaches & Role of Human Expertise
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5. • Deep learning/neural networks (billions of weights/features)
• No feature engineering, ”dumb” algorithm + big data, emergent/alien capabilities
• Non-domain experts improve learning efficiency; domain expert feedback
Approaches & Role of Human Expertise
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Encoding human expertise ->
Learning human-understandable expertise from human
expertise and data ->
Invalidating human expertise
Explaining alien intelligence in human-understandable terms

6. Deep Neural Network -> ChatGPT
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Reinforcement Learning
AI learns to make decisions by interacting
with an environment to maximize
cumulative reward through trial & error.
https://www.understandingai.org/p/large-language-models-explained-with
https://huyenchip.com/2023/05/02/rlhf.html

7. Foundation Models – Generality is Free?
Problem-specific training + generalization --> general capability training + adaptation
Value of unique data & human expertise in training vs predicting?
Bommasani, R. et.al , 2022. On the Opportunities and Risks of Foundation Models.
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8. Generative AI
•Text
•Image/video
•Code/Scripts
•Data
Predictive
Diagnostic
Generative AI – Generate Anything?
Prescriptive
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9. Business Transformation with AI
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• General Capability
• human resources or tools/functionality
• Ease of Access
• Cost-benefit analysis/plan ->low-cost exp.
• Changing nature/role of human expertise
• Explanation & understanding
• Changing org structure & collaboration mode
• Reverse Conway’s law

10. 10 | https://a16z.com/2023/06/20/emerging-architectures-for-llm-applications/
Example: LLM App Architecture
Zero-gradient Infrastructure

11. Responsible AI – Regulation & Ethics
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Australia’s AI Ethics Principles (developed by Data61)
1) Human, societal and environmental wellbeing
2) Human-centred values
3) Fairness
4) Privacy protection and security
5) Reliability and safety
6) Transparency and explainability
7) Contestability
8) Accountability
Australia’s Responsible AI Network (RAIN)
Minister Husic: “I'm determined that we go further than ethics principles. I
want Australia to become the world leader in responsible AI.”

12. Best Practices for Responsible (Generative) AI
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Lu, Q., Zhu, L., Xu, X., Xing, Z., Whittle, J., 2023. Towards Responsible AI in the Era of ChatGPT: A Reference
Architecture for Designing Foundation Model-based AI Systems. http://arxiv.org/abs/2304.11090
CSIRO Responsible AI (RAI)
Pattern Catalogue
• RAI-by-Design Products
• Development Processes
• Governance
https://research.csiro.au/ss/science/projects/responsible-ai-pattern-catalogue/

13. Summary & Questions
• Business transformation with this new wave of AI
• General capabilities/”interns” vs specific tools
• Low-cost experimentation vs problem-driven planning
• Value of unique data & human knowledge
• Universities Accord
• Skills: What human expertise/knowledge?
• VET->HighEd->Biz: “the revenge of the generalist”
• Access/Investment: FM/GenAI
• Governance/Accountability: Responsible AI
More info & Contact
https://research.csiro.au/ss/
Liming.Zhu@data61.csiro.au
Brendan.Omalley@data61.csiro.au
Coming out late 2023
For the latest, follow me on
Twitter: @limingz
LinkedIn: Liming Zhu
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