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the kind we make at year-end meetings, but the deeper promises organizations make when they deploy AI A Cybernetic Approach to AI Compliance Two insights have been particularly valuable: First, trying to This changes how I think about AI governance. in regulated environments, this offers a more realistic path forward than waiting for "explainable AI I've been working through these ideas in more detail—how cybernetic principles apply to AI governance

When Words are Not Enough: The Limitations of AI In the race toward artificial general intelligence, AI systems can now analyze research papers at unprecedented speeds, identify patterns in complex datasets Yet, there's a fundamental limitation in this approach that we must acknowledge: AI systems don't directly As we continue to advance AI technology, we must remain mindful of these limitations. While AI represents a powerful tool for processing and analyzing human knowledge, it cannot replace the

For AI safety analysis, STAMP/STPA appears better suited to AI's systemic and emergent risks, but the choice becomes more nuanced when considering AI's integration into traditional process systems.  The real challenge lies in analyzing AI-augmented process control systems—where an AI controller making Rather than viewing these as competing methodologies, the most thoughtful approach recognizes that AI as AI becomes embedded throughout industrial infrastructure.

As we push the boundaries of technological innovation, we're witnessing a fascinating progression of AI The Learning Foundation: Machine Learning Systems Imagine an AI that can learn from past experiences, Where machine learning sees patterns, AI systems see stories, connections, and deeper meanings. Autonomous Action: Agentic AI Systems The plot thickens with Agentic AI Systems – the problem-solvers The Frontier of Self-Evolution: Autopoietic AI Systems Here's where things get truly mind-bending.

) there is one question that must be answered, "What is AI?" At one level AI consists of the same computing technology we have used in the past. How should AI be best defined? The same principle applies to AI. More work is needed to develop clarity to what AI is and what it is not.

In recent days, a thought-provoking statement was made suggesting that artificial intelligence (AI) should they create a cross-functional AI Ethics Committee to oversee the ethical implications of AI use within maintain ethical guidelines for AI development and deployment Provide guidance on complex AI-related ethical dilemmas Monitor emerging AI regulations and industry best practice. and explainability measures for AI-driven decisions Foster a culture of responsible AI use throughout

AI compliance demands a fundamentally new mindset. Neither alone is sufficient to ensure AI delivers real benefits in a safe and responsible manner. When it comes to AI, the stakes are exceptionally high, with both significant risks and opportunities This environment demands real-time AI governance, supported by programs, systems, and processes that Applying Lean Startup to AI Compliance in Practice The Lean Startup approach for AI compliance focuses

AI Assurance isn't just about checking boxes before deployment. With today's AI systems, we simply can't predict everything in advance—we need to stay vigilant while In the paper published by the European Defence Agency (EDA), entitled “Trustworthiness for AI in Defence in Defence - Figure 14 The introduction of AI technologies and autonomy capabilities has tipped the adequate confidence and evidence that the AI system satisfies the intended function (System Assurance

Each organization will and should determine how they will govern the use of AI and the risks associated There is going to be a cost and side effects from using AI that we need to account for. Data used in AI will also need to be protected. How are you governing your use of AI. What standards are you using? Are you handling the risk from using AI?

Artificial intelligence (AI) has become increasingly prevalent in our lives, and as we interact more However, there are several reasons why we should avoid anthropomorphizing AI systems. First and foremost, AI is not human. Secondly, anthropomorphizing AI systems can be misleading and even dangerous. How to Stop Humanizing AI Systems To prevent or stop anthropomorphizing AI systems, here are some steps

This year will mark the end of unregulated use of AI for many organizations. AI safety regulations and responsible use guidelines are forthcoming. This will require building Responsible AI and/or AI Safety Programs to deliver on obligations and contend with AI specific risk.  To stay ahead of AI risk you can no longer wait.

Rapid advancements in AI technology present a double-edged sword: exciting opportunities alongside significant Proposed strategies often emphasize establishing entirely new AI governance frameworks. Provincial regulators must act now to elevate engineering's role in the AI landscape. responsible development and deployment of AI technologies. development and secure its position as a leader in the global AI landscape.