Dr. Liming Zhu, Research Director at CSIRO's Data61, gave a presentation on AI transformation and its potential clash with human expertise. The presentation discussed frontier AI risks and the Australian approach to responsible AI. It highlighted how general AI capabilities could replace specific tools and enable low-cost experimentation over problem-driven planning. The presentation concluded by encouraging collaboration with CSIRO's Data61 on AI engineering best practices and governance and foundation model-based system design and evaluation.

1. Australia’s National Science Agency
AI Transformation
A Clash with Human Expertise
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. Consider a future device … in which an individual stores all his books,
records, and communications, and which is mechanized so that it may
be consulted with exceeding speed and flexibility. It is an enlarged
intimate supplement to his memory.”
- Vannevar Bush
A Future Device – to Extend the Power of Minds
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3. Frontier AI Risks
Responsible AI: The Australian Approach
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Hendrycks, D., Mazeika, M., Woodside, T., 2023. An Overview of
Catastrophic AI Risks. https://doi.org/10.48550/arXiv.2306.12001
Anderljung,et. al, 2023. Frontier AI Regulation: Managing Emerging
Risks to Public Safety. https://doi.org/10.48550/arXiv.2307.03718

4. Summary & Questions
AI 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
• Responsible AI at the system level
More info & Contact
https://research.csiro.au/ss/
Liming.Zhu@data61.csiro.au
Simonette.Cox@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
• AI Engineering best practices & governance
• LLM/Foundation model-based system design/eval