The document discusses AI and generative AI. It provides an overview of CSIRO's Data61, Australia's largest data and digital innovation organization. It discusses definitions of AI, different AI approaches, and the roles of human expertise. It covers recent advances in generative AI like GPT-3 and foundation models. It also discusses opportunities and risks of generative AI, and Australia's vision for responsible AI through ethics principles and the Responsible AI Network. The document advocates for system-level practices and governance to manage risks of generative AI while understanding and explaining models rather than just building them.

1. Australia’s National Science Agency
AI Unveiled
From Current State to Future Frontiers
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. CSIRO’s Data61: Australia’s Largest Data & Digital
Innovation R&D Organisation
1000+
talented people
(including
affiliates/students)
Home of
Australia’s
National AI
Centre
Data61
Generated
18+ Spin-outs
130+ Patent
groups
200+
Gov &
Corporate
partners
Facilities
Mixed-Reality Lab
Robotics Inno. Centre
AI4Cyber HPC Enclave
300+
PhD students
30+
University collaborators
(Responsible)
Tech/AI
Privacy & RegTech
AI Engineering
AI/GenAI
AI for Science
Resilient &
Recovery Tech
Cybersecurity
Digital Twin
Spark (bushfire) toolkit
CSIRO's Data61
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3. • 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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4. • Purposes & Requirements
• AI governance/regulation: under/over-inclusiveness, flexibility, practicality…
• Business transformation: applicability, measurability, clarity…
• R&D, Education & 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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5. • Example - Fraud detection
• Features: transaction time/amount/frequency, account age, geolocation…
• Rule/logic-based
• data -> rules (human) + AI helps manage/derive complex rules
• Machine learning (model: Y=weightsi*Xi+ constant & human-designed learning algorithm)
• Supervised: labelled data (human), features (human), AI learns rules
• Unsupervised: no labelled data, features (human), AI learns rules
• Deep learning/neural networks (billions of weights/features)
• No feature engineering, ”dumb” algorithm + big data, emergent/alien capabilities
• Non-domain human experts improve learning efficiency
Approaches & Role of Human Expertise
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Encoding -> Learning from -> Invalidating human knowledge…

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

7. Foundation Models – Generality is Free?
Problem-specific training + generalization --> general capability training + adaptation
Value of unique Data 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 GenAI
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• General Capability
• human resources or tools/functionality
• Ease of Access
• More low-cost experimentation driven
• Less cost-benefits analysis/planning
• Changing the nature/role of human knowledge
• Explanation & understanding
• Value of data and human knowledge?

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

11. Australia’s Responsible AI Vision
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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 & Future Frontiers
• 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
• Managing risks of foundation models/GenAI
• System-level practices and guardrails
• Understand/Explain rather than build
More info & Contact
https://research.csiro.au/ss/
Liming.Zhu@data61.csiro.au
Brendan.Omalley@data61.csiro.au
Coming out late 2023
Collaborate with CSIRO’s Data61 on
• (Responsible) AI Engineering best practices & governance
• LLM/Foundation model-based system design/eval
For the latest, follow me on
Twitter: @limingz
LinkedIn: Liming Zhu
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