1) Dr. Liming Zhu from CSIRO's Data61 discusses using generative AI like ChatGPT to assist with scientific discovery by acting as smart interns or tools that can provide low-cost experimentation ideas and analyses. 2) This raises opportunities for changing the role of scientist expertise and facilitating new cross-discipline collaborations, but also risks around ensuring AI systems are developed and used responsibly and trustworthily. 3) CSIRO is working on best practices for responsible generative AI, including through their Responsible AI Pattern Catalogue, to help address issues of trust, transparency, and accountability as general AI capabilities are applied to science.

1. Australia’s National Science Agency
GenAI in Research
with Responsible 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. • Example – animal image classification
• Data->Features: number of legs, presence of fur, shape of the ear….
• Rule/logic-based
• data, feature, data -> rules, feedback, + AI helps manage/derive complex rules
• Machine learning
• Learned model: P = weighti* featurei… ; human-designed learning algorithm
• Supervised: labelled data, features, AI learns rules, feedback
• Unsupervised: no labelled data, features, AI learns rules, feedback
• Learning new features (potentially replacing human-designed features and processes)
– Human-interpretable but not used before: texture of fur to size ratio
– Human-interpretable but not easily human-usable: fur colour variance in milliseconds in video
– Not fully interpretable to humans: complex relationship edge orientation + texture gradient + shadowing +…
Approaches & Role of Human Expertise
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4. • 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
4 |
Encoding human expertise ->
Learning human interpretable expertise from human
expertise and data ->
Invalidating human expertise and human processes
Explaining alien intelligence in human-understandable terms

5. 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

6. Foundation Models – Generality is Free?
Problem-specific training + generalization --> general capability training + adaptation
Value of unique data & science expertise in training vs predicting?
Bommasani, R. et.al , 2022. On the Opportunities and Risks of Foundation Models.
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7. Generative AI
•Text
•Image/video
•Code/Scripts
•Data
Predictive
Diagnostic
Generative AI – Generate Anything?
Prescriptive
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• Hypothesis
• Experiment plans
• New data
• Scripts to run/analyze experiments
• Diagnostic results
• Predictions/Answers
• Program/Scripts for Predictions/Ans
…

8. Science Discovery with GenAI/FM
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• General Capability
• Smart interns/colleagues or tools
• Ease of Access
• Cost-benefit analysis/plan ->low-cost exp.
• Changing nature/role of scientist expertise
• Explanation & understanding
• Changing x-discipline collaboration mode
• Reverse Conway’s law

9. • One all-discipline science FM
• No fancy curriculum tricks
• Discipline data WGs
• Responsible AI & Trustworthiness
Trillion Parameter Consortium
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10. 10 |
Example: AI CoPilot for Science

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
Science Discovery with this new wave of AI
• General capabilities/”interns” vs specific tools
• Low-cost experimentation vs problem-driven planning
• Value of unique data & scientist knowledge
• Responsible AI for science
• Multi-use opportunities and risks
• Trustworthiness and trust in science
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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Collaborate with CSIRO’s Data61 on
• (Responsible) AI Engineering best practices & governance
• LLM/Foundation model-based system design/eval